Papers by Zhang Zheng
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models (2025.acl-long)
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Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions. |
| Approach: | They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information . |
| Outcome: | The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities. |
Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models (2024.emnlp-main)
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| Challenge: | Existing methods for code retrieval struggle to balance scalability and annotation quality. |
| Approach: | They propose a method that integrates functions called within the repository and information on third-party APIs to enhance the annotation context. |
| Outcome: | The proposed method improves the annotation context by incorporating functions called within the repository and information on third-party API functionalities. |
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
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| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution (2021.emnlp-main)
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Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh
| Challenge: | Existing methods to defend against adversarial word-substitution attacks have not been evaluated or compared in a systematic manner. |
| Approach: | They propose to compare different defense methods under representative adversarial attacks . they propose a method that improves the robustness of neural text classifiers against such attacks a . |
| Outcome: | The proposed method improves robustness of neural text classifiers against such attacks by a significant margin. |
Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders (2026.acl-long)
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Xiangchen Song, Aashiq Muhamed, Yujia Zheng, Lingjing Kong, Zeyu Tang, Mona T. Diab, Virginia Smith, Kun Zhang
| Challenge: | Sparse Autoencoders (SAEs) are a tool in mechanistic interpretability (MI) but the aspiration to identify a canonical set of features is challenged by the observed inconsistency of learned SAE features across different training runs. |
| Approach: | They propose to use the Pairwise Dictionary Mean Correlation Coefficient to quantify SAE feature consistency as an evaluation axis alongside reconstruction and sparsity. |
| Outcome: | The proposed measure is based on the pairwise dictionary mean correlation coefficient (PW-MCC) on LLM activations. |
RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories (2026.findings-acl)
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| Challenge: | Existing benchmarks for large language models fail to capture complex interplay between functionality and security. |
| Approach: | They propose a benchmark for secure code generation constructed from real-world, high-risk Java repositories. |
| Outcome: | The proposed benchmarks highlight the gap between functional and secure code generation in LLMs. |
Controllable Abstractive Sentence Summarization with Guiding Entities (2020.coling-main)
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| Challenge: | Existing text summarization models lack guiding entities to ensure that entities are present in summaries. |
| Approach: | They propose a controllable abstractive sentence summarization model which generates summaries with guiding entities. |
| Outcome: | The proposed model outperforms the state-of-the-art models in evaluation scores and informativeness metrics. |
KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality (2026.acl-long)
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| Challenge: | Existing Reinforcement Learning approaches rely on outcome-oriented rewards to reinforce fabricated reasoning paths when the final answer is correct. |
| Approach: | They propose a framework that integrates factual supervision directly into reasoning . they propose to decompose chain of thought into atomic facts and verify them against ground-truth knowledge . |
| Outcome: | The proposed framework reduces the Incorrect Rate on SimpleQA by 20.3% while maintaining strong performance on complex reasoning benchmarks. |
GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation (2024.emnlp-main)
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| Challenge: | Existing datasets are too challenging for direct model learning or suffer from misalignment between text and images. |
| Approach: | They propose a pipeline that leverages GPT-4 and GPT4V to generate geometry problems with aligned text and images, facilitating model learning. |
| Outcome: | The proposed pipeline generates 4.9K geometry problems with aligned text and images, facilitating model learning. |
VLA-Mark: A cross modal watermark for large vision-language alignment models (2025.emnlp-main)
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Shuliang Liu, Zheng Qi, Jesse Jiaxi Xu, Yibo Yan, Junyan Zhang, He Geng, Aiwei Liu, Peijie Jiang, Jia Liu, Yik-Cheung Tam, Xuming Hu
| Challenge: | Existing text watermarking methods disrupt visual-textual alignment, leaving semantic-critical concepts vulnerable. |
| Approach: | They propose a vision-aligned framework that embeds detectable watermarks into outputs . they combine localized patch affinity, global semantic coherence, contextual attention patterns . |
| Outcome: | The proposed framework shows lower PPL and higher BLEU than conventional methods with near-perfect detection (98.8% AUC). |
LCR-RAG: Enhancing Logical Consistency in Retrieval-Augmented Generation via Neuro-symbolic Reinforcement Learning (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely used to ground large language models in external knowledge and improve factual accuracy. |
| Approach: | They propose a framework that integrates neuro-symbolic verification with reinforcement learning to optimize logical consistency. |
| Outcome: | The proposed framework outperforms strong RAG baselines on hotpotQA, ASQA, and TriviaQA. |
CORD: Bridging the Audio–Text Reasoning Gap via Weighted On-policy Cross-modal Distillation (2026.findings-acl)
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Hu Jing, Danxiang Zhu, Xianlong Luo, Dan Zhang, Shuwei He, Yishu Lei, Shikun Feng, Hai-Tao Zheng, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Large Audio Language Models (LALMs) exhibit a degradation in knowledge and reasoning capabilities . empirical results show that CORD significantly bridges the audio–text performance gap . |
| Approach: | They propose a framework that performs online cross-modal self-distillation to bridge the acoustic-semantic gap between LALMs and text-based models. |
| Outcome: | The proposed framework bridges the acoustic-semantic gap between LALMs and text-based models . it employs on-policy reverse KL divergence with importance-aware weighting . |
ToolGrad: Efficient Tool-use Dataset Generation with Textual “Gradients” (2026.findings-acl)
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| Challenge: | Prior work synthesizes tool-use LLM datasets by first generating a user query, then complex tool-using annotations like DFS. |
| Approach: | They propose an agentic framework that synthesizes user queries and generates valid tool-use chains . they propose a dataset with more complex tool use, lower cost, and almost 100% pass rate . |
| Outcome: | Experiments show that tools trained on ToolGrad outperform expensive baseline datasets and proprietary LLMs. |
Model Composition for Multimodal Large Language Models (2024.acl-long)
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Chi Chen, Yiyang Du, Zheng Fang, Ziyue Wang, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Existing methods for creating versatile MLLMs rely on joint training with paired instruction data, which is resource-intensive and challenging to extend to new modalities. |
| Approach: | They propose a new paradigm for multimodal large language models by reusing modality encoders and merging LLM parameters. |
| Outcome: | The proposed model retains the modal understanding capabilities of each original model. |
MTA:A Merge-then-Adapt Framework for Personalized Large Language Models (2026.acl-long)
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Xiaopeng Li, Yuanjin Zheng, Wanyu Wang, Wenlin Zhang, Pengyue Jia, Yingyi Zhang, Haiying He, Mengyang Ma, Yiqi Wang, Maolin Wang, Xuetao Wei, Xiangyu Zhao
| Challenge: | Personalized Large Language Models (PLLMs) aim to align outputs with individual user preferences . current methods of fine-tuning a separate module for each user are unscalable . |
| Approach: | They propose a Merge-then-Adapt framework for Personalized Large Language Models . they construct a shared Meta-LoRA bank and propose an Adaptive LoRA Fusion stage . |
| Outcome: | The proposed framework outperforms existing SOTA methods on the LaMP benchmark. |
DORE: Document Ordered Relation Extraction based on Generative Framework (2022.findings-emnlp)
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| Challenge: | Existing generative methods do not fit document-level relation extraction tasks where there are multiple entities and relational facts. |
| Approach: | They propose to generate a symbolic and ordered sequence from relation matrix which is easier to learn and introduce several negative sampling strategies to improve the performance with balanced signals. |
| Outcome: | The proposed method can improve the performance of the generative DocRE models on four datasets. |
E2-LLM: Efficient and Extreme Length Extension of Large Language Models (2024.findings-acl)
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Jiaheng Liu, ZhiqiBai ZhiqiBai, Yuanxing Zhang, Chenchen Zhang, YuangZh YuangZh, Ge Zhang, JiakaiWang JiakaiWang, Haoran Que, Yukang Chen, Wenbo Su, Tiezheng Ge, Jie Fu, Wenhu Chen, Bo Zheng
| Challenge: | Existing techniques for extending context capabilities in LLMs require additional training procedures and access to datasets with long context (e.g., sequences of 32K tokens). |
| Approach: | They propose a solution to extend context capabilities in Large Language Models by training a single process over a sequence of 4K tokens. |
| Outcome: | The proposed solution significantly reduces the cost of continual-pretraining or fine-tuning over short sequences and improves robustness to diverse relative positions. |
SHARP: Search-Based Adversarial Attack for Structured Prediction (2022.findings-naacl)
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| Challenge: | SHARP is a new attack method for structured prediction models that solves several challenges. |
| Approach: | They propose a black-box adversarial attack method that uses a search-based optimization problem to attack adversarials. |
| Outcome: | The proposed method performs more potent attack than pioneer arts on two structured prediction tasks. |
All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG (2026.acl-long)
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Dan Wang, Guozhao Mo, Yafei Shi, Cheng Zhang, Bo Zheng, Boxi Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Le Sun
| Challenge: | Existing mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language. |
| Approach: | They propose a language-agnostic utility-driven reranker alignment technique to mitigate language bias during re-ranking. |
| Outcome: | The proposed approach mitigates language bias and consistently improves mRAG performance across languages. |
OceanGPT: A Large Language Model for Ocean Science Tasks (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have transformed the paradigm in ocean science. |
| Approach: | They propose a framework to automatically obtain large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. |
| Outcome: | The proposed framework shows a higher level of knowledge expertise for ocean science tasks and gains preliminary embodied intelligence capabilities in ocean technology. |
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)
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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations. |
| Approach: | They propose a framework that covers 61 risk categories across four modality interactions to address this gap. |
| Outcome: | The proposed framework covers 61 risk categories across four distinct modality interactions. |
GNEG: Graph-Based Negative Sampling for word2vec (P18-2)
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| Challenge: | Generally speaking, negative sampling is the best choice for distributed word representation learning. |
| Approach: | They hypothesize that taking into account global, corpus-level information and generating a different noise distribution for each target word better satisfies the requirements of negative examples for each training word. |
| Outcome: | The proposed approach boosts the word analogy task by about 5% and improves the performance on word similarity tasks by about 11% compared to the baseline. |
Look Twice before You Leap: A Rational Framework for Localized Adversarial Text Anonymization (2026.findings-acl)
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| Challenge: | Existing LLMs rely on remote API services, which creates privacy paradoxes and suboptimal solutions with severe utility collapse. |
| Approach: | They propose a localized and training-free framework with an Attacker-Arbitrator-Anonymizer architecture that allows attackers to filter out ghost leaks. |
| Outcome: | The proposed framework achieves superior privacy-utility trade-off compared to strong baselines. |
Causal-ESC: Reliable Policy Learning for Emotional Support Conversation via Causal Inference (2026.acl-long)
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| Challenge: | Existing approaches to Emotional Support Conversation (ESC) are mechanistically opaque and lacks a causal mechanism between dialogue features and effective empathic strategies. |
| Approach: | They propose a framework that uses Doubly Robust learning to model causal effects of utterance features on strategy selection. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines in empathy and helpfulness and provides a theoretically grounded, interpretable solution to the mechanistic interpretability dilemma in affective computing. |
PunMemeCN: A Benchmark to Explore Vision-Language Models’ Understanding of Chinese Pun Memes (2025.emnlp-main)
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| Challenge: | Pun memes combine wordplay with visual elements to create humor, irony, or other rhetorical effects. |
| Approach: | They propose a benchmark to assess Chinese pun memes' processing capabilities across three progressive tasks: pun meme detection, sentiment analysis, and chat-driven meme response. |
| Outcome: | The proposed model can detect pun memes, analyze sentiments, and respond to chats, while ignoring homophone wordplay. |
LightThinker: Thinking Step-by-Step Compression (2025.emnlp-main)
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Jintian Zhang, Yuqi Zhu, Mengshu Sun, Yujie Luo, Shuofei Qiao, Lun Du, Da Zheng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in Large Language Models have demonstrated their remarkable capabilities in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associated with generating lengthy tokens. |
| Approach: | They propose a method that dynamically compresses verbose thought steps into compact representations and discards original reasoning chains. |
| Outcome: | The proposed method reduces peak memory usage and inference time while maintaining competitive accuracy. |
What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations (2026.acl-short)
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Yujie Luo, Zhuoyun Yu, Xuehai Wang, Yuqi Zhu, Ningyu Zhang, Lanning Wei, Lun Du, Da Zheng, Huajun Chen
| Challenge: | Existing approaches to replicate AI research are limited by insufficient background knowledge and the limitations of retrieval-augmented generation methods. |
| Approach: | They propose a pluggable, paper-centric knowledge base that integrates code snippets and technical insights extracted from scientific literature into a verifiable, executable representation. |
| Outcome: | The proposed knowledge base shows significant performance gains on paperBench when integrated into three agent frameworks with two different LLMs. |
Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System (2023.findings-acl)
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| Challenge: | End-to-end task-oriented dialogue systems are expensive to annotate and lack data in real scenarios. |
| Approach: | They propose to implement dual learning in task-oriented dialogues to exploit the correlation of heterogeneous data. |
| Outcome: | The proposed method improves the effectiveness of end-to-end task-oriented dialogue systems under multiple benchmarks and obtains state-of-the-art results in low-resource scenarios. |
FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs’ Responsiveness to Human Feedback (2025.emnlp-main)
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| Challenge: | Existing research focuses on benchmarking LLMs in single-turn dialogues, neglecting the nuanced nature of human feedback within real-world usage scenarios. |
| Approach: | They propose a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese. |
| Outcome: | The proposed benchmarks show that human feedback can significantly impact LLMs’ responsiveness in real-world usage scenarios. |
AnRe: Analogical Replay for Temporal Knowledge Graph Forecasting (2025.acl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are vital for event prediction, yet current methods face limitations. |
| Approach: | They propose a training-free Analogical Replay reasoning framework that uses LLMs to extract historical contexts and generate analogical reasoning examples as contextual inputs. |
| Outcome: | The proposed model outperforms existing training-free methods on four benchmarks. |
Cross-Lingual Phrase Retrieval (2022.acl-long)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences. |
| Approach: | They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training. |
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction (2023.acl-long)
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| Challenge: | Recent work has introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE) however, in these works AMR is used only implicitly, for instance, as additional features or training signals. |
| Approach: | They propose a novel AMR-based graph structure which uses graph neural networks to find event arguments from unstructured text. |
| Outcome: | The proposed graph structure outperforms the state-of-the-art models by 3.63pt and 2.33pt F1 and reduces inference time by 56%. |
Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
| Outcome: | The proposed approach surpasses the performance of both large and small language models individually, underscoring its effectiveness in diverse scenarios. |
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications (2025.acl-industry)
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| Challenge: | Recent advances in large language models (LLMs) have achieved impressive performance on many language tasks. |
| Approach: | They synthesize a high-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications by reweighting the sample. |
| Outcome: | The proposed model improves on offline evaluation and live A/B testing, given the LLM performance on offline data and scores from a small privacy-preserving on-device language model. |
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)
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Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, Liang Huang
| Challenge: | PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks. |
| Approach: | They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks. |
| Outcome: | The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. |
When Words Smile: Generating Diverse Emotional Facial Expressions from Text (2025.emnlp-main)
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| Challenge: | Existing systems that generate only coarse facial expressions ignore the rich and dynamic nature of face-to-face communication. |
| Approach: | They propose an end-to-end text-to expression model that explicitly focuses on emotional dynamics. |
| Outcome: | The proposed model outperforms baselines on 15,000 text–3D expression pairs on a large-scale dataset. |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning (2024.emnlp-main)
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| Challenge: | Recent advances in memory-efficient zeroth-order methods have limited their widespread adoption due to performance drops and a high risk of divergence. |
| Approach: | They propose a memory-efficient zeroth-order framework to improve performance and convergence of the MeZO methods by using only forward passes. |
| Outcome: | The proposed framework improves performance and convergence of the proposed methods on Roberta-Large and Llama-2-7B models. |
Self-Supervised Position Debiasing for Large Language Models (2024.findings-acl)
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| Challenge: | Existing methods for debiasing large language models require external bias knowledge or annotated non-biased samples, which is lacking for position debiases. |
| Approach: | They propose a self-supervised position debiasing framework that leverages unsupervised responses from pre-trained LLMs for debiazing without external bias knowledge. |
| Outcome: | The proposed framework outperforms existing methods in mitigating three types of position biases on eight datasets and five tasks. |
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective (2022.coling-1)
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| Challenge: | Existing methods to debiase samples with biased features obstructs the model in learning from non-biased parts of the samples. |
| Approach: | They propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective by using Random Fourier Features and weighted re-sampling to decorrelate dependencies between features. |
| Outcome: | The proposed method eliminates spurious correlations in a fine-grained manner from a feature space perspective. |
DNCASR: End-to-End Training for Speaker-Attributed ASR (2025.acl-long)
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| Challenge: | DNCASR is a novel end-to-end trainable system for speaker clustering and automatic speech recognition . it is designed to transcribe long multi-party meetings with speaker-attributed transcriptions . |
| Approach: | They propose a joint neural speaker clustering and automatic speech recognition system . DNCASR uses two separate encoders to independently encode speaker characteristics and local waveform information . |
| Outcome: | The proposed system outperforms a parallel system that does not have links between speaker and ASR decoders. |
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model’s Reasoning Path Aggregation (2025.findings-acl)
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| Challenge: | Existing methods for large language models (LLMs) are limited by step-by-step decision-making on KGs, or require fine-tuning or pre-training on specific KG. |
| Approach: | They propose a framework that harnesses the global planning abilities of large language models (LLMs) for efficient and accurate KG reasoning. |
| Outcome: | Extensive experiments show that the proposed framework achieves state-of-the-art performance in KGQA tasks, delivering both high efficiency and accuracy. |
NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models (2023.acl-long)
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| Challenge: | Existing backdoor attacks against prompt-based learning involve injecting back doors into embedding layers or word embedders. |
| Approach: | They propose a backdoor attack against prompt-based learning that injects backdoors into embedding layers or word embeddable vectors. |
| Outcome: | The proposed backdoor attack outperforms two state-of-the-art models on six NLP tasks and three prompting strategies. |
See Detail Say Clear: Towards Brain CT Report Generation via Pathological Clue-driven Representation Learning (2024.findings-emnlp)
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| Challenge: | Brain CT report generation is important to aid physicians in diagnosing cranial diseases. |
| Approach: | They propose a Pathological Clue-driven Representation Learning model to build cross-modal representations based on pathological clues and adapt them for text generation. |
| Outcome: | The proposed method outperforms previous methods and achieves SoTA performance. |
GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)
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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim
| Challenge: | Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy. |
| Approach: | They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. |
| Outcome: | Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency. |
Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective (2025.naacl-long)
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Shenglai Zeng, Jiankun Zhang, Bingheng Li, Yuping Lin, Tianqi Zheng, Dante Everaert, Hanqing Lu, Hui Liu, Hui Liu, Yue Xing, Monica Xiao Cheng, Jiliang Tang
| Challenge: | Existing studies have shown that LLMs struggle to identify the boundaries of their own knowledge and tend to prioritize external information over internal knowledge learned during pre-training. |
| Approach: | They conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. |
| Outcome: | The proposed classifiers improve performance even when dealing with noisy knowledge databases. |
Real, Fake, or Manipulated? Detecting Machine-Influenced Text (2025.findings-emnlp)
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| Challenge: | Prior work on machine generated text detection focused on identifying whether document was human or machine written, ignoring these fine-grained uses. |
| Approach: | They propose a machine-influenced text detector that learns to separate text samples from four primary types . the detector uses a subcategory guidance module to help separate the fine-grained categories . |
| Outcome: | The proposed detector outperforms the state-of-the-art in five LLMs and six domains. |
Budget-Constrained Tool Learning with Planning (2024.findings-acl)
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| Challenge: | Existing methods for budget-constrained tool learning have been overlooked . et al., 2023b) compared tool learning with other methods to improve performance . |
| Approach: | They propose a method for budget-constrained tool learning by creating a preferable plan under the budget constraint before utilizing the tools. |
| Outcome: | The proposed method reduces the cost of tool learning and reaches competitive Pass Rate. |
Zero-shot Text Classification via Reinforced Self-training (2020.acl-main)
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Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
| Challenge: | Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks. |
| Approach: | They propose a self-training based method to efficiently leverage unlabeled data. |
| Outcome: | The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset. |
Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios (2025.findings-naacl)
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| Challenge: | Long prompts contain redundant information and are sensitive to the position of key information in long context scenarios. |
| Approach: | They propose a training-free prompt compression framework that retains key information at token level while removing distracting tokens. |
| Outcome: | The proposed framework outperforms existing methods on long context benchmarks. |
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)
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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu
| Challenge: | Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations . |
| Approach: | They propose a framework to synthesize complex charts and reliable reasoning data from scratch. |
| Outcome: | Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models . |
Conversation Disentanglement with Bi-Level Contrastive Learning (2022.findings-emnlp)
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| Challenge: | Existing methods focus on pairwise utterance relations but pay inadequate attention to utterant-to-context relation modeling. |
| Approach: | They propose a general disentangle model based on bi-level contrastive learning that brings closer utterances in the same session while encouraging each utterrance to be near its clustered session prototypes in representation space. |
| Outcome: | The proposed model achieves state-of-the-art performance on both settings across public datasets. |
Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)
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| Challenge: | Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections. |
| Approach: | They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening . |
| Outcome: | The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons. |
RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)
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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma
| Challenge: | Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation. |
| Approach: | They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation. |
| Outcome: | The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics. |
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours (2022.emnlp-demos)
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Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang
| Challenge: | Label Sleuth is an open source system for labeling and creating text classifiers which does not require coding skills nor machine learning knowledge. |
| Approach: | *Label Sleuth* is an open source system for labeling and creating text classifiers which does not require coding skills nor machine learning knowledge. |
| Outcome: | *Label Sleuth* is an open source system for labeling and creating text classifiers. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
Wanda++: Pruning Large Language Models via Regional Gradients (2025.findings-acl)
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Yifan Yang, Kai Zhen, Bhavana Ganesh, Aram Galstyan, Goeric Huybrechts, Markus Müller, Jonas M. Kübler, Rupak Vignesh Swaminathan, Athanasios Mouchtaris, Sravan Babu Bodapati, Nathan Susanj, Zheng Zhang, Jack FitzGerald, Abhishek Kumar
| Challenge: | Existing pruning methods suffer from accuracy degradation without full-model sparsity-aware fine-tuning. |
| Approach: | They propose a pruning framework that uses decoder-block-level regional gradients to improve pruning accuracy. |
| Outcome: | The proposed pruning framework outperforms the state-of-the-art pruning frameworks by utilizing decoder-block-level regional gradients. |
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)
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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)
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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai
| Challenge: | Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge. |
| Approach: | They propose a recurrent inductive bias that aligns with the recursive nature of programming logic. |
| Outcome: | The proposed model achieves comparable performance to standard dense models with more parameters. |
A Unified Generative Framework for Various NER Subtasks (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. |
| Approach: | They propose to formulate NER subtasks as entity span sequence generation task . framework can be used to solve all three kinds of NER tasks without tagging schema . |
| Outcome: | The proposed framework achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets. |
Aligning Large Language Models with Implicit Preferences from User-Generated Content (2025.acl-long)
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Zhaoxuan Tan, Zheng Li, Tianyi Liu, Haodong Wang, Hyokun Yun, Ming Zeng, Pei Chen, Zhihan Zhang, Yifan Gao, Ruijie Wang, Priyanka Nigam, Bing Yin, Meng Jiang
| Challenge: | Existing preference learning methods rely heavily on curated data from humans or advanced LLMs, which is costly and difficult to scale. |
| Approach: | They propose a framework that leverages implicit preferences in unlabeled user-generated content to generate preference data. |
| Outcome: | The proposed framework transforms user-generated content into user queries and generates responses from the policy model. |
CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models (2024.findings-acl)
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| Challenge: | Existing certified robustness methods for certifying input-specific text perturbations have shown promise in certifyling UTPs, but masking only adversarial words can eliminate the attack. |
| Approach: | They propose a method to certify a language model’s robustness against UTPs by using random smoothing. |
| Outcome: | The proposed method achieves high certified accuracy under extensive masking and achieves state-of-the-art results in multiple settings. |
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)
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Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang
| Challenge: | Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies . |
| Approach: | They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT . |
| Outcome: | The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. |
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books (2022.acl-long)
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| Challenge: | Existing question answering (QA) techniques are created mainly to answer questions asked by humans, but in educational applications, teachers often need to decide what questions to ask . |
| Approach: | They propose to use a fairytale-themed storybook as input to generate QA pairs that can test a student's comprehension skills. |
| Outcome: | The proposed system outperforms state-of-the-art QAG baseline systems and builds an interactive story-telling application for the future real-world deployment. |
CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers (2022.coling-1)
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| Challenge: | Existing QA datasets only contain unconditional and parallel answers . conditional question answering with hierarchical multi-span answers is challenging for the community to solve . |
| Approach: | They propose a conditional question answering task with hierarchical multi-span answers . they propose CMQA, which contains conditional and hierarchic samples . |
| Outcome: | The proposed task can be used to build more reliable and sophisticated QA systems. |
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset (2023.findings-acl)
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| Challenge: | a cross-domain text-to-SQL task aims to parse user questions into SQL on complete unseen databases . a single-domain task evaluates the performance on identical databases based on the same domain . |
| Approach: | They propose a cross-domain text-to-SQL task that parses user questions into SQL on unseen databases. |
| Outcome: | The proposed system can parse user questions into SQL on complete unseen databases. |
Prompt-based Generation of Natural Language Explanations of Synthetic Lethality for Cancer Drug Discovery (2024.lrec-main)
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| Challenge: | Synthetic lethality (SL) is a genetic interaction where a single gene mutation allows cell survival, but simultaneous mutations in two genes lead to cell death. |
| Approach: | They propose a prompt-based pipeline for generating natural language explanations using a dataset derived from New Bing . |
| Outcome: | The proposed pipeline improves on existing biomedical language models in terms of text quality and explainability. |
Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning (2024.emnlp-main)
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Lu Chen, Rui Zheng, Binghai Wang, Senjie Jin, Caishuang Huang, Junjie Ye, Zhihao Zhang, Yuhao Zhou, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current methods rely on ranking losses to teach reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions. |
| Approach: | They propose a method that incorporates contrastive learning into the reward modeling process to enhance generalization and stabilize the reinforcement learning training process. |
| Outcome: | The proposed method enhances generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences. |
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)
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Hao Zheng, Guozhao Mo, Xinru Yan, Qianhao Yuan, Wenkai Zhang, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing presentation agents rely on predefined workflows and fixed templates to generate presentations. |
| Approach: | They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation. |
| Outcome: | The proposed framework can be used to generate presentations with environmental observations. |
Layer-wise Fusion with Modality Independence Modeling for Multi-modal Emotion Recognition (2023.acl-long)
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Jun Sun, Shoukang Han, Yu-Ping Ruan, Xiaoning Zhang, Shu-Kai Zheng, Yulong Liu, Yuxin Huang, Taihao Li
| Challenge: | Existing studies focus on developing models that exploit the unification of multiple modalities. |
| Approach: | They propose to maintain modality independence by using a multi-modal transformer model that fuses all modalities. |
| Outcome: | The proposed model outperforms state-of-the-art models in multi-modal emotion recognition. |
Governance in Motion: Co-evolution of Constitutions and AI models for Scalable Safety (2025.emnlp-main)
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Chenhao Huang, Ziyu Shen, Yicong Ren, Huiyuan Zheng, Jiazheng Zhang, Mingxu Chai, Ming Zhang, Shihan Dou, Fan Mo, Jie Shi, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to align large language models with human preferences lack flexibility . static alignment preferences lack the ability to correct misaligned behaviors as they emerge . |
| Approach: | They propose a framework that enables dynamic and continuous alignment of large language models with human preferences. |
| Outcome: | The proposed framework improves safety and accuracy of a 7B model with human annotations. |
Federated Learning of Gboard Language Models with Differential Privacy (2023.acl-industry)
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Zheng Xu, Yanxiang Zhang, Galen Andrew, Christopher Choquette, Peter Kairouz, Brendan Mcmahan, Jesse Rosenstock, Yuanbo Zhang
| Challenge: | Using federated learning and differential privacy, we train and deploy language models with federation and DP in Google Keyboard. |
| Approach: | They train and deploy language models with federated learning and differential privacy in Google Keyboard . |
| Outcome: | The proposed algorithm achieves meaningfully formal DP guarantees without uniform sampling of clients. |
Distributed Marker Representation for Ambiguous Discourse Markers and Entangled Relations (2023.acl-long)
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| Challenge: | Discourse markers are natural representations of discourse in our daily language. |
| Approach: | They propose to use unlimited discourse marker data to learn a Distributed Marker Representation by bridging markers with sentence pairs. |
| Outcome: | The proposed model outperforms existing models on the implicit discourse relation recognition task and provides strong interpretability. |
AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse (2026.acl-demo)
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| Challenge: | Existing frameworks for building LLM-based agents treat agent behavior as static-knowledge gained during execution is not preserved for future use. |
| Approach: | They propose a new paradigm that preserves successful task solutions as executable subagent code rather than textual experience. |
| Outcome: | The proposed agent-based agent-driven paradigm preserves successful tasks as executable subagent code rather than textual experience. |
Beyond Noise: Characterizing Creative Potential in Unverifiable LLM Hallucinations (2026.acl-long)
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Yu Yan, Chunhong Zhang, Haiyu Zhao, Ziyang Zeng, Zihao Liu, Yongkang Wu, Jianzhou Diao, YiJie Chen, Shujie Wang, Zheng Hu
| Challenge: | Large Language Models generate outputs that extend beyond established knowledge . prior work does not characterize the unverifiable space as a whole . |
| Approach: | They propose a novelty-verifiability characterization that distinguishes Creative Synthesis from Groundless Fabrication by a conceptual creation task. |
| Outcome: | The proposed model distinguishes Creative Synthesis (Region A) from Groundless Fabrication (Regium B) it shows that Region A is non-negligible and robust, persisting across generation strategies, models, domains, and embedding choices. |
Can We Predict Before Executing Machine Learning Agents? (2026.acl-long)
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| Challenge: | Existing approaches to scientific discovery rely on expensive physical execution . a Generate-Execute-Feedback paradigm is costly and slow . |
| Approach: | They propose to internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. |
| Outcome: | The proposed framework achieves 61.5% accuracy and robust confidence calibration when primed with a Verified Data Analysis Report. |
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models (2025.findings-naacl)
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Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, Lin Yan
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across various domains since the release of ChatGPT . a key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. |
| Approach: | They introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling to efficiently find good responses by promoting diversity. |
| Outcome: | The proposed method enhances inference-time generation quality and benefits training in the alignment stage. |
Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation (2026.acl-long)
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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Yuxi Zhang, Huimin Wang, Yutian Zhao, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu
| Challenge: | Retrieval-Augmented Generation (RAG) provides access to external knowledge, but current research focuses on retrieval quality and 'integration bottleneck' . |
| Approach: | They propose a framework that explicitly decouples reasoning from evidence integration by generating an 'Inner-Answer' and a 'Refer-Aswer" they propose 'a joint decoding mechanism that dynamically fuses the logical coherence of the Inner-Andswer with the factual precision of the Refer-Adswer at the token level' |
| Outcome: | The proposed framework improves accuracy by 12.1% and reduces hallucinations by 16.3% on five QA benchmarks. |
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training (2025.naacl-long)
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Yuchen Zhuang, Jingfeng Yang, Haoming Jiang, Xin Liu, Kewei Cheng, Sanket Lokegaonkar, Yifan Gao, Qing Ping, Tianyi Liu, Binxuan Huang, Zheng Li, Zhengyang Wang, Pei Chen, Ruijie Wang, Rongzhi Zhang, Nasser Zalmout, Priyanka Nigam, Bing Yin, Chao Zhang
| Challenge: | Existing LLMs often rely on complex prompting or extensive fine-tuning to introduce new capabilities while preserving strong generalizability. |
| Approach: | They propose a large-scale pre-training corpus to enhance LLM agents' capabilities . they use 103B agent-specific data encompassing 76,537 APIs . |
| Outcome: | The proposed training corpus outperforms open-source LLMs and commercial LLM agents on three agent benchmarks. |
Model Merging for Knowledge Editing (2025.acl-industry)
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Zichuan Fu, Xian Wu, Guojing Li, Yingying Zhang, Yefeng Zheng, Tianshi Ming, Yejing Wang, Wanyu Wang, Xiangyu Zhao
| Challenge: | Existing knowledge editing approaches struggle with sequential editing scenarios and harm the general capabilities of the model. |
| Approach: | They propose a framework that combines robust supervised fine-tuning and model merging for knowledge editing to combine supervised and supervised learning. |
| Outcome: | The proposed approach outperforms existing methods in sequential editing while preserving the original performance of the model. |
Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax (2026.findings-acl)
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Zeli Su, Ziyin Zhang, Zhou Liu, Xuexian Song, Zhankai Xu, Longfei Zheng, Xiaolu Zhang, Rong Fu, Guixian Xu, Wentao Zhang
| Challenge: | Extending large language models to low-resource languages often incurs an "alignment tax" token-level fine-tuning enforces token-level surface imitation on narrow and biased data distributions. |
| Approach: | They propose a semantic-space alignment paradigm powered by group-level semantic rewards instead of likelihood maximization. |
| Outcome: | The proposed model acquires low-resource capa- bilities while mitigating alignment tax on Tibetan–Chinese machine translation and Ti- betan headline generation. |
DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data (2026.acl-long)
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Shaofan Liu, Guoqiang Zhang, Shihan Dou, Huiyuan Zheng, Yiming Zhou, Junjie Ye, Shaowen Wang, Shichun Liu, Jiazheng Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing methods for training reward models are vulnerable to context neglect and degraded accuracy. |
| Approach: | They propose distribution-aware reward modeling that augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response. |
| Outcome: | The proposed model improves performance in RLHF and improves accuracy in other settings. |
RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)
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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang
| Challenge: | Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed. |
| Approach: | They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting. |
| Outcome: | The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data. |
Hidden States as Early Signals: Step-level Trace Evaluation and Pruning for Efficient Test-Time Scaling (2026.findings-acl)
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| Challenge: | Existing approaches to speed up parallel scaling have relied on similarity-based or confidence-based pruning, but these signals do not reliably indicate trace quality. |
| Approach: | They propose a pruning framework that evaluates reasoning steps using hidden states and dynamically prunes unpromising traces during generation. |
| Outcome: | The proposed framework reduces end-to-end inference latency by 45%–70% on average compared to self-consistency while improving reasoning accuracy. |
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees (2022.emnlp-main)
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| Challenge: | Existing structured reasoning frameworks lack internal decision probability and cannot model the tree as a whole. |
| Approach: | They propose a Reinforcement Learning based Entailment Tree generation framework that is trained using the cumulative signals across the whole tree. |
| Outcome: | The proposed framework offers explicit deductions with entailment steps in a tree structure. |
LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)
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| Challenge: | Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning. |
| Approach: | They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion . |
| Outcome: | The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus (2023.emnlp-main)
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Tianhang Zhang, Lin Qiu, Qipeng Guo, Cheng Deng, Yue Zhang, Zheng Zhang, Chenghu Zhou, Xinbing Wang, Luoyi Fu
| Challenge: | Existing methods for detecting hallucinations in LLMs rely on external knowledge for reference retrieval or require sampling multiple responses for consistency verification. |
| Approach: | They propose a reference-free, uncertainty-based method for detecting hallucinations in Large Language Models that imitates human focus in factuality checking from three aspects: focus on the most informative keywords; focus on unreliable tokens in historical context; focus of token properties such as token type and token frequency. |
| Outcome: | The proposed method achieves state-of-the-art performance across all evaluation metrics and eliminates the need for additional information. |
SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification (2026.acl-long)
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Xin Tong, Weidong Zhang, Jiaang Li, Haibin Chen, Shilei Liu, Langming Liu, Kangtao Lv, Yujin Yuan, Wenbo Su, Bo Zheng
| Challenge: | Existing pipelines rely on expert-crafted heuristic rules, which lack content-aware, fine-grained noise detection. |
| Approach: | They propose a framework that reframes data refinement as a highly efficient token classification task. |
| Outcome: | The proposed framework outperforms existing pipelines on benchmarks and is 2.5x faster at inference. |
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)
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Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots. |
| Approach: | They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios. |
| Outcome: | The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning . |
Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models (2025.findings-acl)
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Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, QingqingLong QingqingLong, Yefeng Zheng, Xian Wu
| Challenge: | Hallucination is a critical challenge for large language models and large vision-language models (LVLMs) however, dedicated research on medical hallucinations remains unexplored. |
| Approach: | They provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes. |
| Outcome: | The proposed models have demonstrated impressive performance on a variety of medical benchmarks. |
Aligning Large Language Models with Human Preferences through Representation Engineering (2024.acl-long)
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Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing methods for achieving this alignment involve employing reinforcement learning from human feedback (RLHF) Existing approaches involve using RLHF to fine-tune LLMs based on human labels . however, RLRF is susceptible to instability during fine- tuning and presents challenges in implementation. |
| Approach: | They propose to use reinforcement learning from human feedback to fine-tune large language models with human preferences to achieve precise control of model behavior. |
| Outcome: | Experiments show that RAHF can be used to capture and manipulate representations to align with a broad spectrum of human preferences or values rather than being confined to a single concept or function. |
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder (2020.emnlp-main)
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| Challenge: | Existing methods for detecting public sentiment drift are not designed for sentiment drift detection. |
| Approach: | They propose a Hierarchical Variational Auto-Encoder model to learn better distribution representation and a new drift measure to directly evaluate distribution changes between historical and new data. |
| Outcome: | The proposed model performs better than three existing state-of-the-art methods. |
MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning (2024.findings-emnlp)
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| Challenge: | Low-rank adaptation and its mixture-of-experts (MOE) methods are highly effective but introduce significant latency in multi-tenant settings due to the LoRA modules and MOE routers added to multiple linear modules. |
| Approach: | They propose a low-rank adaptation variant that considers each LoRA module as an expert and employs a prompt-aware routing mechanism. |
| Outcome: | Extensive analysis on commonsense reasoning tasks and math reasoning tasks show that MiLoRA outperforms strong PEFT baselines with comparable tunable parameter budgets. |
PQR: Improving Dense Retrieval via Potential Query Modeling (2025.acl-long)
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| Challenge: | Existing training data is sparse, with each document associated with one or a few labeled queries. |
| Approach: | They propose a training-free potential query retrieval framework to address this problem . they use a Gaussian mixture distribution to model all potential queries for a document . |
| Outcome: | The proposed method is able to capture comprehensive semantic information from a document with multiple queries. |
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training (2024.acl-long)
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Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Jianan Zhao, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang
| Challenge: | Experimental results demonstrate that ProtLLM achieves superior performance against protein-specialized baselines on protein-centric tasks and induces zero-shot and in-context learning capabilities on protein language tasks. |
| Approach: | They propose a cross-modal large language model (LLM) that can handle protein-centric and protein-language tasks by using a dynamic protein mounting mechanism. |
| Outcome: | The proposed model can predict proteins from a vast pool of candidates and can also predict natural language and biological papers. |
A Multi-Task Embedder For Retrieval Augmented LLMs (2024.acl-long)
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| Challenge: | Existing retrieval methods face limitations in terms of knowledge, memory, and action. |
| Approach: | They propose a retrieval enhancement mechanism that brings in useful information from external sources to augment the LLM. |
| Outcome: | The proposed method significantly improves the LLM’s performance in various downstream tasks while introducing superior retrieval augmentation’s effect over both general and task-specifc retrievers. |
Domain Adaptation for Subjective Induction Questions Answering on Products by Adversarial Disentangled Learning (2024.acl-long)
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| Challenge: | Existing methods to answer subjective questions about products are often imbalanced across product domains. |
| Approach: | They propose a domain-adaptive model that integrates multiple viewpoints into a good answer by integrating these heterogeneous and inconsistent viewpoints. |
| Outcome: | The proposed model integrates multiple viewpoints into a single answer span and is able to integrate them into the answer. |
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting (2024.emnlp-main)
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Zhepeng Wang, Runxue Bao, Yawen Wu, Jackson Taylor, Cao Xiao, Feng Zheng, Weiwen Jiang, Shangqian Gao, Yanfu Zhang
| Challenge: | Pretrained large language models excel in a variety of natural language processing tasks . however, they pose significant security risks due to their tendency to memorize training data . |
| Approach: | They propose a method to estimate LLM memorization using dynamic, prefix-dependent soft prompts. |
| Outcome: | The proposed method can achieve maximum relative improvement of 135.3% and 39.8% over baseline compared to state-of-the-art methods. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)
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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum
| Challenge: | Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. |
| Approach: | They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window. |
| Outcome: | The proposed model scales to multi-million-token effective TTC without exceeding context limits. |
PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models (2026.findings-acl)
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| Challenge: | Existing reward models evaluate empathy from a single perspective, overlooking bidirectional interaction nature of empathy. |
| Approach: | They propose a reward model that evaluates empathy from a single perspective . they propose PERM to integrate a bystander perspective to monitor overall interaction quality . |
| Outcome: | a new reward model outperforms state-of-the-art models on an emotional intelligence benchmark and an industrial daily conversation dataset. |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
Knowledge-aware Attention Network for Medication Effectiveness Prediction (2024.lrec-main)
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| Challenge: | Existing effectiveness prediction methods focus on one specific medicine, one specific disease, or one specific lab test, making it hard to extend to general medicines and diseases in hospital/ICU scenarios. |
| Approach: | They propose to use knowledge enhanced module to incorporate external knowledge about medications and a medical feature learning module to determine the interaction between diagnosis and medications. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a public dataset showing that it significantly outperformed existing models. |
Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks (2023.emnlp-main)
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| Challenge: | Existing models focus on the textual content of the review, while spoiler detection requires putting the review into the context of facts and knowledge regarding movies. |
| Approach: | They propose a network-based spoiler detection model that takes into account external knowledge about movies and user activities on movie review platforms. |
| Outcome: | The proposed model takes into account external knowledge about movies and user activities on movie review platforms while incorporating user networks. |
Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token’s Nature (2026.acl-long)
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| Challenge: | Existing methods that use entropy as a discrete filter or post-hoc regulator are limited in their ability to optimize for reasoning tasks. |
| Approach: | They propose a token-aware algorithm that continuously adapts optimization dynamics based on token-level entropy throughout the entire training process. |
| Outcome: | Extensive experiments on mathematical reasoning, code, and logic tasks across multiple models demonstrate HAPO’s consistent superiority over DAPO. |
Orthogonal Subspace Learning for Language Model Continual Learning (2023.findings-emnlp)
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| Challenge: | Existing methods for continual learning in language models suffer catastrophic forgetting when learning sequential tasks. |
| Approach: | They propose an orthogonal low-rank adaptation approach for continual learning in language models that uses orthogons to learn sequentially. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on continual learning benchmarks and preserves generalization ability of LLMs on unseen tasks. |
An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision (2026.acl-industry)
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Zishuai Zhang, Sihao Yu, null Xiewenyi, Ying Nie, Junfeng Wang, Zhiming Zheng, Dawei Yin, Hainan Zhang
| Challenge: | Existing whole-page reranking methods require large-scale expert annotations to achieve high-quality results. |
| Approach: | They propose a whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross-modal annotations and distilling intra-modality preferences to align relevance scales across modalities. |
| Outcome: | The proposed framework reduces annotation costs by 70-90% while outperforming fully-annotated reranking baselines. |
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
| Approach: | They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. |
| Outcome: | The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. |
Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing jamming attacks on RAG systems typically induce explicit refusals or denial-of-service behaviors. |
| Approach: | They propose a black-box attack framework that exploits safety-aligned behaviors of large language models to trigger soft failures. |
| Outcome: | The proposed framework exploits safety-aligned behaviors of large language models to induce soft failures. |
QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models (2025.emnlp-main)
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Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang
| Challenge: | Large Language Models (LLMs) are quantized to lower precision to reduce memory cost and latency in inference. |
| Approach: | They propose a quantized zeroth-order framework for fine-tuning Large Language Models (LLMs) using low-precision forward passes. |
| Outcome: | The proposed method achieves comparable results to first-order methods in FP8 and superior accuracy in INT8 and INT4 training. |
LM-Cocktail: Resilient Tuning of Language Models via Model Merging (2024.findings-acl)
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| Challenge: | Pre-trained language models are continually fine-tuned to better support downstream applications. however, this operation may result in significant performance degeneration on general perspectives. |
| Approach: | They propose a method which enables pre-trained language models to stay resilient in general perspectives. |
| Outcome: | The proposed model achieves strong empirical performance in the whole scope of general tasks while preserving a superior capacity in its targeted domain. |
Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation (2026.findings-acl)
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Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su
| Challenge: | Existing approaches to improve contextual faithfulness treat the LLM as a black box, generating responses that are inconsistent with the provided context. |
| Approach: | They propose a framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iv) conflict-aware attention to modulate attention heads toward faithful context integration. |
| Outcome: | Experiments show that ProbeRAG significantly improves both accuracy and contextual faithfulness. |
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)
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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. |
| Approach: | They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions. |
| Outcome: | The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks. |
ArkRepoBench: A Repository-Level Code Completion Benchmark for HarmonyOS Development (2026.findings-acl)
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Yanlin Wang, Bowen Zhang, Yanli Wang, Daya Guo, Terry Yue Zhuo, Jiachi Chen, Mingwei Liu, Xingong Zhang, Zibin Zheng
| Challenge: | Despite the maturity of LLM-based code assistance for mainstream languages, the capabilities of ArkTS are largely unexplored. |
| Approach: | They propose to benchmark repository-level code completion for ArkTS using 7,519 samples from 20 official HarmonyOS repositories. |
| Outcome: | The proposed benchmark covers multiple difficulty levels and categorizes completion instances into Single-File, Cross-Filled Independent, and Cross-Filed Dependent settings based on dependency analysis. |
Investigating Multi-Hop Factual Shortcuts in Knowledge Editing of Large Language Models (2024.acl-long)
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| Challenge: | Recent work has demonstrated the power of large language models in recalling knowledge and reasoning. |
| Approach: | They propose to erase shortcut neurons to mitigate the associated risks . 20% of the failures are attributed to shortcuts, they find . |
| Outcome: | The proposed approach reduces failures in multi-hop knowledge editing caused by shortcuts by 20% . |
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension (2022.acl-long)
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Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer
| Challenge: | Existing QA datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. |
| Approach: | They propose to use FairytaleQA to generate 10,580 questions based on 278 children-friendly stories to assess model's fine-grained learning skills. |
| Outcome: | The proposed dataset consists of 10,580 questions derived from 278 children-friendly stories, covering seven types of narrative elements or relations. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
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Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |
IHEval: Evaluating Language Models on Following the Instruction Hierarchy (2025.naacl-long)
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Zhihan Zhang, Shiyang Li, Zixuan Zhang, Xin Liu, Haoming Jiang, Xianfeng Tang, Yifan Gao, Zheng Li, Haodong Wang, Zhaoxuan Tan, Yichuan Li, Qingyu Yin, Bing Yin, Meng Jiang
| Challenge: | Instruction-tuned language models (LMs) are increasingly deployed as interactive services across various applications. |
| Approach: | They propose a benchmark to evaluate models' ability to follow the instruction hierarchy by comparing their models to a set of benchmarks. |
| Outcome: | The proposed benchmark covers 3,538 examples across nine tasks covering cases where instructions in different priorities either align or conflict. |
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving (2025.findings-emnlp)
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| Challenge: | a new study examines the potential of retrieval-augmented generation (RAG) with foundation models to enhance expert-level reasoning. |
| Approach: | They introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics. |
| Outcome: | The proposed model can be used to solve Olympiad-level physics problems. |
Instant Personalized Large Language Model Adaptation via Hypernetwork (2026.acl-long)
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Zhaoxuan Tan, Zixuan Zhang, Haoyang Wen, Zheng Li, Rongzhi Zhang, Pei Chen, Fengran Mo, Zheyuan Liu, Qingkai Zeng, Qingyu Yin, Meng Jiang
| Challenge: | Existing parameter-efficient fine-tuning methods require training a separate adapter for each user, making them computationally expensive and impractical for real-time updates. |
| Approach: | They propose a scalable framework that maps a user's profile directly to a full set of adapter parameters. |
| Outcome: | The proposed framework outperforms prompt-based personalization and OPPU while using substantially fewer computational resources at deployment. |
Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting (2024.emnlp-main)
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| Challenge: | Existing VideoQA models struggle to adapt to new questions or tasks posed by newly available content. |
| Approach: | They propose a continual learning framework that fine-tunes a large language model for a sequence of tasks and integrates specific question constraint prompting, knowledge acquisition prompting and visual temporal awareness prompting. |
| Outcome: | The proposed model achieves 55.14% accuracy on both NExT-QA and DramaQA datasets and 71.24% accuracy for DramaQA. |
Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition (2026.acl-long)
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| Challenge: | Existing LLM benchmarks focus on semantic complexity or quantitative competition, but rarely both simultaneously under economic scarcity. |
| Approach: | They propose a benchmark that evaluates the capabilities of large language models (LLMs) in economically-relevant tasks through economic and trade competition. |
| Outcome: | The proposed model evaluates the capabilities of large language models in economically-relevant tasks through economic and trade competition. |
MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models (2025.emnlp-main)
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Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao, Yifan Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu
| Challenge: | Current approaches focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. |
| Approach: | They propose a benchmark to evaluate how large vision language models understand memes in their original context. |
| Outcome: | The proposed benchmark evaluates how large vision language models understand meme intent in their original context. |
Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement (2023.findings-emnlp)
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Zhiheng Xi, Senjie Jin, Yuhao Zhou, Rui Zheng, Songyang Gao, Jia Liu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing prompting methods have been used to enhance multistep reasoning capabilities of large language models, but they have overlooked the potential of formulating higher-quality problems. |
| Approach: | They propose a method that starts from the problem side and refines problems to be more comprehensible and solvable for models. |
| Outcome: | The proposed method achieves notable and consistent effectiveness on five reasoning benchmarks across different models. |
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)
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| Challenge: | Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. |
| Approach: | They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite . |
| Outcome: | The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query . |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)
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| Challenge: | emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage. |
| Approach: | They propose to use Chinese instruction-tuning LLMs as a cookbook for customizing LLM models that can better respond to Chinese instructions. |
| Outcome: | The proposed LLM can be used to customize Chinese LLMs that can better respond to Chinese instructions. |
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)
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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP. |
| Approach: | They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens. |
| Outcome: | The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. |
Can Public Large Language Models Help Private Cross-device Federated Learning? (2024.findings-naacl)
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| Challenge: | Recent studies have shown that public data can be used to improve privacy-utility trade-offs for large and small language models. |
| Approach: | They propose to use large-scale public data to help differentially private FL training . they propose a distribution matching algorithm with theoretical grounding to sample public data close to private data distribution . |
| Outcome: | The proposed method is efficient and effective for training private models by taking advantage of public data. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
StructuThink: Reasoning with Task Transition Knowledge for Autonomous LLM-Based Agents (2025.findings-emnlp)
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| Challenge: | StructuThink framework enhances LLMs' ability to ground decisions in domain-specific scenarios. |
| Approach: | They propose a knowledge-structured reasoning framework that enhances LLM-based agents with explicit decision constraints. |
| Outcome: | The proposed framework achieves higher task success rates and more efficient action sequences than baseline methods. |
Multi-Programming Language Sandbox for LLMs (2025.acl-demo)
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Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, Xuanjing Huang
| Challenge: | MPLSandbox is an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). |
| Approach: | They propose a multi-programming language sandbox that provides unified feedback from compilers and analysis tools for Large Language Models. |
| Outcome: | The proposed multi-language sandbox can provide comprehensive feedback from compilers and analysis tools for large language models (LLMs). |
Pub-LawBench: Public-Oriented Benchmarking for LegalAI (2026.acl-long)
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| Challenge: | Existing evaluation frameworks focus on legal professionals, not legal professionals. |
| Approach: | They propose a public-oriented LegalAI benchmark grounded in legal functionalism and genre analysis to address this gap. |
| Outcome: | The proposed model evaluates 17 large language models on Pub-LawBench using simple prompts and Chain-of-Thought under a vanilla inference setting. |
GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)
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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng
| Challenge: | Structured product information is a major bottleneck for the efficiency of e-commerce platforms. |
| Approach: | They propose a data-driven approach to generate product structured representations using product metadata. |
| Outcome: | Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms. |
Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)
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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities. |
| Approach: | They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation. |
| Outcome: | The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement. |
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases (2022.naacl-main)
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| Challenge: | Recursive noun phrases have interesting semantic properties, yet it is unknown whether language models have such knowledge. |
| Approach: | They propose a dataset of three textual inference tasks targeting recursive noun phrases . they show that such knowledge is learnable with appropriate data . |
| Outcome: | The proposed model achieves strong zero-shot performance on an extrinsic Harm Detection task. |
AdapterDistillation: Non-Destructive Task Composition with Knowledge Distillation (2023.emnlp-industry)
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| Challenge: | Recent work on learning from multiple tasks has shown that adding an extra fusion layer to implement knowledge composition is non-scalable for some applications. |
| Approach: | They propose a two-stage knowledge distillation algorithm to extract task specific knowledge by using local data to train a student adapter. |
| Outcome: | Experiments on frequently asked question retrieval in task-oriented dialog systems validate the efficiency of AdapterDistillation. |
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression (2026.findings-eacl)
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| Challenge: | Low-rank decomposition methods suffer from accuracy degradation and expensive calibration procedures. |
| Approach: | They propose a fast and accurate, training-free structural compression method based on fine-grained low-rank transformations in the activation space. |
| Outcome: | The proposed method outperforms pruning baselines in generalization and downstream performance while delivering inference speedups. |
A Survey of RAG-Reasoning Systems in Large Language Models (2025.findings-emnlp)
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Yangning Li, Weizhi Zhang, Yuyao Yang, Wei-Chieh Huang, Yaozu Wu, Junyu Luo, Yuanchen Bei, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Chunkit Chan, Yankai Chen, Zhongfen Deng, Yinghui Li, Hai-Tao Zheng, Dongyuan Li, Renhe Jiang, Ming Zhang, Yangqiu Song, Philip S. Yu
| Challenge: | a survey of RAG-based reasoning-based approaches shows that it is not effective for multi-step inferences. |
| Approach: | They map how advanced reasoning optimizes each stage of RAG . they show how retrieved knowledge supply missing premises and expand context for complex inference . |
| Outcome: | The proposed frameworks achieve state-of-the-art across knowledge-intensive benchmarks. |
Adaptive Nearest Neighbor Machine Translation (2021.acl-short)
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| Challenge: | kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Approach: | They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy. |
| Outcome: | The proposed method outperforms the existing model on four benchmark datasets and is open-source. |
An Industry Evaluation of Embedding-based Entity Alignment (2020.coling-industry)
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| Challenge: | Knowledge graphs (KGs) are increasingly important in various applications such as question answering and search engines. |
| Approach: | They propose to use a supervised learning environment with unbiased seed mappings for training and validation to evaluate alignment methods in an industrial context. |
| Outcome: | The proposed methods are evaluated in an industrial context and are compared with DBpedia and Wikidata benchmarks. |
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation (2022.acl-long)
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Yingxiu Zhao, Zhiliang Tian, Huaxiu Yao, Yinhe Zheng, Dongkyu Lee, Yiping Song, Jian Sun, Nevin Zhang
| Challenge: | Building models of natural language processing (NLP) is challenging in low-resource scenarios where limited data are available. |
| Approach: | They propose a memory imitation meta-learning method that enhances the model’s reliance on support sets for task adaptation. |
| Outcome: | The proposed method outperforms baselines on both text classification and generation tasks. |
TALON: A Multi-Agent Framework for Long-Table Exploration and Question Answering (2025.emnlp-main)
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| Challenge: | Existing approaches to query-relevant content retrieval fail to retrieve contextually relevant data. |
| Approach: | They propose a multi-agent framework for table question answering over long tables . TALON features a planning agent that iteratively invokes a tool agent to access tabular data . |
| Outcome: | The proposed framework achieves average accuracy improvements of 7.5% and 12.0% across all language models. |
ProcessBench: Identifying Process Errors in Mathematical Reasoning (2025.acl-long)
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Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
| Challenge: | Existing models fail to generalize to more challenging math problems, authors say . existing benchmarks related to assessing language models' reasoning process are limited . |
| Approach: | They propose a tool to measure language models' ability to identify erroneous steps in reasoning . they use two types of models: process reward models and critic models . |
| Outcome: | The proposed model outperforms existing models in evaluating language models' reasoning process . the best open-source model has demonstrated the critique capability competitive with the proprietary model . |
M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation (2024.findings-acl)
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| Challenge: | In this paper, we introduce a new embedding model for semantic retrieval of more than 100 working languages. |
| Approach: | They propose a new embedding model that supports multi-lingual, cross-lingual and long-document retrieval . they propose integrating relevance scores from different retrieval functionalities into the teacher signal . |
| Outcome: | The proposed model exhibits superior performance on multilingual, cross-lingual, and long-document retrieval benchmarks. |
SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning (2026.findings-acl)
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| Challenge: | Recent large reasoning models have shown exceptional performance on various tasks, but they consume excessive tokens even for simple queries, leading to resource waste and prolonged user latency. |
| Approach: | They propose a self-adaptive reasoning strategy that automatically allocates budgets according to problem complexity and introduces GRPO for reinforcement learning to reduce output length. |
| Outcome: | The proposed model achieves an average response length compression of 61% on math reasoning tasks while maintaining accuracy. |
AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce (2026.findings-acl)
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| Challenge: | Multimodal representation is crucial for E-commerce tasks such as identical product retrieval. |
| Approach: | They propose an approach which leverages the generative power of Multimodal Large Language Models to extract key attributes from product images and text and enhances representation learning through a two-stage training framework. |
| Outcome: | The proposed model achieves state-of-the-art on multiple downstream retrieval tasks, validating the effectiveness of harnessing generative models to advance fine-grained representation learning. |
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)
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| Challenge: | Existing approaches to represent knowledge in the low-dimensional space are to leverage large-scale unsupervised text corpus to train fixed or contextual representations. |
| Approach: | They propose to leverage large-scale unsupervised text corpus to train fixed or contextual language representations and to express knowledge into a knowledge graph (KG) they incorporate distributional representations of a KG onto the representations from pre-trained language models, via simply concatenation or multi-head attention. |
| Outcome: | The proposed models outperform the other models on the COIN: COmmonsense INference in Natural Language Processing (COIN) Workshop datasets. |
Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)
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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang
| Challenge: | Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored. |
| Approach: | They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data. |
| Outcome: | The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models. |
Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)
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| Challenge: | Recent years have witnessed rapid advances in text-to-music generation using large language models. |
| Approach: | They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content . |
| Outcome: | The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio. |
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models (2026.findings-acl)
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Teng Wang, Jiang Zhangyi, Zhenqi He, Hailei Gong, Shenyang Tong, Wenhan Yang, Zeyu Li, Yanan Zheng, Zifan He, Zewen Ye, Shengjie Ma, Jianping Zhang
| Challenge: | Existing Process Reward Models (PRMs) are vulnerable to reward hacking and require expensive, large-scale annotation of reasoning steps. |
| Approach: | They propose a reward model approach which evaluates both individual and consecutive reasoning steps from fine-grained and coarse-grounded level. |
| Outcome: | Empirical results show that the proposed model performs better than existing PRMs and is more robust than existing models. |
Logic: Long-form Outline Generation via Imitative and Critical Self-refinement (2025.findings-emnlp)
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| Challenge: | Existing methods for long-form outline generation have low knowledge density and lack detail . retrieval-augmented approaches struggle to maintain logical coherence across retrieved information . |
| Approach: | They propose a system that mimics human writers' refinement process by mimicking outlines through imitation and critical self-refinement. |
| Outcome: | The proposed system improves on the FreshWiki and WikiOutline datasets and establishes a coherent planning framework and structured knowledge base. |
A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions (2025.acl-industry)
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| Challenge: | Existing work on large language models lacks a realistic environment and parallelized framework to support complex interactions between agents and environments. |
| Approach: | They propose a framework that integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. |
| Outcome: | The proposed framework can support simulations of 30,000 agents faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance increases linearly with the increase of LLM computational resources. |
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data (2020.emnlp-main)
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| Challenge: | Existing research has focused on training open-domain dialogue models using unpaired data. |
| Approach: | They propose a data-level distillation method for training open-domain dialogue models by utilizing unpaired data. |
| Outcome: | The proposed method produces high-quality dialogue pairs with diverse contents, and can improve competitive baselines. |
Token-level Inference-Time Alignment for Vision-Language Models (2026.findings-acl)
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Kejia Chen, Junjun Zheng, Jiawen Zhang, Manxi Lin, Xiao Pan, Jiacong Hu, Jian Lou, Zunlei Feng, Mingli Song
| Challenge: | Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity . despite widespread adoption, VLMs often exhibit a critical failure mode: hallucination . |
| Approach: | They propose a framework for Token-level Inference-Time Alignment that steers the decoding process without updating the base model parameters. |
| Outcome: | The proposed framework improves performance on 13 benchmarks across architectures . it boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score . |
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue (2022.emnlp-main)
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| Challenge: | Existing generative replay methods use only a single task-specific token to control their models. |
| Approach: | They propose a method to capture task-specific distributions with a conditional variational autoencoder, conditioned on natural language prompts to guide the pseudo-sample generation. |
| Outcome: | The proposed method outperforms baselines on natural language understanding tasks of advanced task-oriented dialogue (ToD) systems. |
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding (2021.findings-acl)
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| Challenge: | Existing methods for aligning knowledge graph entities ignore the ontology which contains critical meta information such as classes and membership relationships with entities. |
| Approach: | They propose an ontology-guided method where KGs and ontologies are jointly embedded. |
| Outcome: | Extensive experiments on seven public and industrial benchmarks show the ontology-guided method performs well and is cost-effective. |
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)
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Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang
| Challenge: | Simultaneous translation is notoriously dif- ficult due to word-order differences. |
| Approach: | They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model. |
| Outcome: | The proposed framework achieves low latency and reasonable qual- ity on 4 directions. |
Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods (2026.acl-long)
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Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng, Yiyu Wang, Haocong He, Yuanhuiyi Lyu, Lutao Jiang, Xin Zou, Yuqian Fu, Bin Ren, Linfeng Zhang, Xuming Hu
| Challenge: | Recent efforts to accelerate inference in Multimodal Large Language Models have focused on visual token compression. |
| Approach: | They propose a framework that leverages downsampling as a discriminator to denoise existing benchmarks. |
| Outcome: | The proposed evaluation framework leverages downsampling as a discriminator to denoise existing benchmarks. |
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation (2024.findings-acl)
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| Challenge: | Existing models for speech emotion recognition are not suitable for emotional tasks. |
| Approach: | They propose a universal speech emotion representation model that is pre-trained on open-source emotion data. |
| Outcome: | euphoria2vec outperforms state-of-the-art models and emotion specialist models . it shows consistent improvements among 10 different languages of speech emotion recognition datasets . |
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)
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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao
| Challenge: | Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning . |
| Approach: | They propose a synergistic dual-process framework that integrates reasoning and retrieval. |
| Outcome: | The proposed framework improves answer accuracy and coherence even in smaller-scale models. |
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction (2021.acl-long)
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Hengyi Zheng, Rui Wen, Xi Chen, Yifan Yang, Yunyan Zhang, Ziheng Zhang, Ningyu Zhang, Bin Qin, Xu Ming, Yefeng Zheng
| Challenge: | Recent methods for extracting entities and relations from unstructured texts suffer from limitations, such as redundancy of relation prediction and inefficiency. |
| Approach: | They propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC) they propose overlapping triples for relation prediction and relation-relational alignment . |
| Outcome: | The proposed framework achieves state-of-the-art performance on public benchmarks with higher efficiency and consistent performance gain on complex scenarios of overlapping triples. |
HyperLoRA: Efficient Cross-task Generalization via Constrained Low-Rank Adapters Generation (2024.findings-emnlp)
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| Challenge: | Existing approaches to adapt pre-trained language models (PLMs) to emerging tasks are costly and inefficient. |
| Approach: | They propose a meta-network that generates task-specific weights without any optimization. |
| Outcome: | The proposed approach has flexible generalization ability and superior performance over hypenetworks. |
Uncertainty Aware Learning for Language Model Alignment (2024.acl-long)
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| Challenge: | Existing alignment strategies that focus on diverse and high-quality data often overlook the intrinsic uncertainty of tasks, learning all data samples equally. |
| Approach: | They propose to introduce the sample uncertainty into the alignment of different task scenarios by a simple fashion by setting the label smoothing value of training according to the uncertainty of individual samples. |
| Outcome: | The proposed model outperforms standard supervised fine-tuning on high-entropy tasks and complex low-entropic tasks. |
Building Multi-domain Dialog State Trackers from Single-domain Dialogs (2023.emnlp-main)
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| Challenge: | Existing multi-domain dialog state tracking models require significant manual effort to define domain relations and collect data. |
| Approach: | They propose a divide-and-conquer (DAC) DST paradigm and a multi-domain dialog synthesis framework to build multi- domain DST models from single-domain dialogues. |
| Outcome: | The proposed paradigm makes building multi-domain DST models easier on unseen domain combinations. |
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification (2025.emnlp-main)
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Xuan Zhang, Yongliang Shen, Zhe Zheng, Linjuan Wu, Wenqi Zhang, Yuchen Yan, Qiuying Peng, Jun Wang, Weiming Lu
| Challenge: | Existing tools for ambiguous and incomplete queries are limited by manual construction and lack of error correction mechanisms during multi-turn clarification. |
| Approach: | They propose a framework that exploits the mapping between queries and their tool invocation solutions by removing key parameters from queries while retaining them as ground truth. |
| Outcome: | The proposed framework outperforms existing methods while maintaining high accuracy in tool invocation. |
Enhancing Contrastive Learning with Noise-Guided Attack: Towards Continual Relation Extraction in the Wild (2024.acl-long)
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| Challenge: | Existing methods for continual relation extraction (CRE) excel in preserving old knowledge but falter when confronted with contaminated data streams. |
| Approach: | They propose a noise-resistant contrastive framework for continual relation extraction (CRE) that preserves old knowledge while learning incremental corrupted relations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on various benchmarks with increasing noise rates. |
GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)
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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui
| Challenge: | Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings. |
| Approach: | They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph . |
| Outcome: | The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge. |
Read As Human: Compressing Context via Parallelizable Close Reading and Skimming (2026.acl-long)
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Jiwei Tang, Shilei Liu, Zhicheng Zhang, Qingsong Lv, Runsong Zhao, Tingwei Lu, Langming Liu, Haibin Chen, Yujin Yuan, Hai-Tao Zheng, Wenbo Su, Bo Zheng
| Challenge: | Existing task-aware methods require loading the entire input sequence at once for compression, which suffer from computational inefficiency. |
| Approach: | They propose a framework that adopts an adaptive hybrid reading strategy to reduce computational inefficiency and redundant information in long-context scenarios. |
| Outcome: | Experiments show that RAM outperforms baselines on multiple question answering and summarization benchmarks while delivering up to a 12x speedup on long inputs. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation (2024.acl-srw)
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| Challenge: | Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. Obtaining a large volume of expert-annotated data is costly for most tasks. |
| Approach: | They propose a method that trains the model to prioritize the best responses from a pool of candidates created for a task using ranking metrics. |
| Outcome: | The proposed method is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods. |
GreaterPrompt: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization (2025.acl-demo)
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| Challenge: | Recent advances in prompt optimization have introduced diverse techniques that automatically enhance prompts to better align model outputs with user expectations. |
| Approach: | They propose a framework that unifies different methods under a unified, customizable API while delivering highly effective prompts for different tasks. |
| Outcome: | The proposed framework unifies multiple methods under a unified, customizable API while delivering highly effective prompts for different tasks. |
Neural News Recommendation with Long- and Short-term User Representations (P19-1)
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| Challenge: | Existing news recommendation methods learn a single representation for each user, which may be insufficient. |
| Approach: | They propose a neural news recommendation approach which can learn both long- and short-term user representations by using a news encoder and a user encoder. |
| Outcome: | The proposed approach can learn both long- and short-term user representations on a real-world dataset. |
All That Glitters is Not Gold: Improving Robust Retrieval-Augmented Language Models with Fact-Centric Preference Alignment (2025.findings-acl)
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| Challenge: | Existing methods to learn adaptive retrieval for noisy documents lack prior filtering and may lead to the loss of crucial information. |
| Approach: | They propose a method to improve retrieval performance without prior filtering . they use LLMs self-generated synthetic data as training data without manual annotation . |
| Outcome: | The proposed method performs positive document mining based on factual consistency and uses LLMs self-generated synthetic data as training data without manual annotation. |
Outcome Accuracy is Not Enough: Aligning the Reasoning Process of Reward Models (2026.acl-long)
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Binghai Wang, Yantao Liu, Yuxuan Liu, Tianyi Tang, Shenzhi Wang, Chang Gao, Chujie Zheng, Yichang Zhang, Le Yu, Shixuan Liu, Tao Gui, Qi Zhang, Xuanjing Huang, Bowen Yu, Fei Huang, Junyang Lin
| Challenge: | Recent studies observe a phenomenon where reward models achieve high accuracy on static datasets but fail to generalize effectively during RLHF. |
| Approach: | They propose a method that combines rationale consistency with outcome accuracy to improve performance on RM-Bench and JudgeBench. |
| Outcome: | The proposed method surpasses baselines on RM-Bench and JudgeBench by an average of 5% and improves creative writing tasks by 7%. |
Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)
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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo
| Challenge: | Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity. |
| Approach: | They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability. |
| Outcome: | The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability. |
SAFER: A Controllable Safeguard for LLMs against Backdoor Attacks (2026.findings-acl)
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| Challenge: | Existing inference-time defenses lack explicit control over false acceptance rate (FAR) existing inference time defenses aim to mitigate poisoned inputs but lack explicit FAR control . |
| Approach: | They propose a framework that provides explicit control over false acceptance rate without prior knowledge of backdoor samples. |
| Outcome: | The proposed framework outperforms existing inference-time defenses on three benchmark datasets . it provides explicit and provable control over false acceptance rate without prior knowledge of backdoor samples . |
MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025.acl-long)
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Junjie Zhou, Yongping Xiong, Zheng Liu, Ze Liu, Shitao Xiao, Yueze Wang, Bo Zhao, Chen Jason Zhang, Defu Lian
| Challenge: | despite the growing demand for multimodal retrieval, there is a lack of training data. |
| Approach: | They propose a data synthesis method that leverages vision language models and open-domain images to generate high-quality data. |
| Outcome: | The proposed method outperforms baseline models on 70 more datasets and can scale up. |
MoRI: Learning Motivation-Grounded Reasoning for Scientific Ideation in Large Language Models (2026.acl-long)
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| Challenge: | Existing LLMs emulate human research workflows but lack scientific grounding . empirical results show that MoRI outperforms strong commercial LLM models . |
| Approach: | They propose a framework that explicitly learns scientific reasoning from research motivations to methodologies. |
| Outcome: | The proposed framework outperforms commercial LLMs and agentic baselines in novelty, technical rigor, and feasibility. |
DP3: Differentially Private Prompt Perturbation for Multi-turn LLM Inference (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely used for text understanding and generation . existing methods that assume single-turn interactions break down in multi-turn settings . |
| Approach: | They propose a differentially private prompt perturbation framework for multi-turn LLM inference . DP3 constructs a perturbation mapping table to reuse perturbations for recurring tokens . |
| Outcome: | The proposed framework reduces privacy costs and degrades cross-turn semantic coherence . it also provides a context-aware utility function to maintain semantic consistency across turns . |
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction (2021.naacl-main)
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| Challenge: | Sentiment analysis is a key task in e-commerce to detect fine-to-coarse sentiment polarities. |
| Approach: | They propose to use a large-scale Chinese restaurant review dataset ASAP to investigate the sentiment polarities underlying user reviews. |
| Outcome: | The proposed model outperforms state-of-the-art models on both tasks. |
SciMRC: Multi-perspective Scientific Machine Reading Comprehension (2024.lrec-main)
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| Challenge: | Existing datasets focused on single-perspective question-answer pairs overlooking inherent variation in comprehension levels among different readers. |
| Approach: | They propose a multi-perspective scientific machine reading comprehension dataset . their dataset comprises 741 scientific papers and 6,057 question-answer pairs . |
| Outcome: | The proposed dataset includes questions from beginners, students, and experts. |
A Dual-View Analysis of Multiple Languages in Colonial Newspapers (2026.findings-acl)
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Zhan Su, Xiaoya Chen, Fengran Mo, Ida L. Vos, Prayag Tiwari, Yazhou Zhang, Qian Zheng, Natália da Silva Perez
| Challenge: | Historical newspapers from the colonial period offer valuable evidence of how racializing language evolved over time. |
| Approach: | They propose a contextual question answering and visual question answering task from colonial newspapers . they propose linguistic training for temporal word embedding with a compass to study racialization . |
| Outcome: | The proposed tasks are limited for low-resource tasks, the authors show . the authors compare the results of two QA pairs from colonial newspapers to a compass . |
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)
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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu
| Challenge: | Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning. |
| Approach: | They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
| Outcome: | The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)
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| Challenge: | Prior studies have examined the impact of structured output on LLMs’ generation quality, often presenting one-way findings. |
| Approach: | They propose to derive five potential causal structures characterizing the influence of structured output on LLMs’ generation using one assumed and two guaranteed constraints. |
| Outcome: | The proposed pipeline can be extended to other modules and is not limited to structured output but can be used in industrial applications. |
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)
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Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu
| Challenge: | Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding. |
| Approach: | They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning . |
| Outcome: | The proposed model can encode words into fine-grained representations without modification of production pipelines. |
PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models lack the capacity to formulate global strategies due to latency and availability constraints. |
| Approach: | They propose a framework to internalize the strategic oversight of large models into intrinsic Latent Guidance by synthesizing a query-conditioned Latent Guide. |
| Outcome: | The proposed framework outperforms strong baselines on mathematical and coding benchmarks with negligible inference latency. |
UECA-Prompt: Universal Prompt for Emotion Cause Analysis (2022.coling-1)
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| Challenge: | Existing methods adopt fine-tuning paradigm to solve certain types of ECA tasks. Existing models suffer from dataset bias. |
| Approach: | They propose a universal prompt tuning method to solve different ECA tasks in a unified framework and a sequential learning module to ease the dataset bias. |
| Outcome: | The proposed method achieves competitive performance on the ECA datasets. |
EA-Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment (2026.acl-long)
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| Challenge: | Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object. |
| Approach: | They propose to use large language models to integrate semantic knowledge into EA to identify entities across different knowledge graphs that refer to the same object. |
| Outcome: | The proposed agent outperforms existing methods and achieves state-of-the-art performance on three benchmark datasets. |
Detecting Stealthy Backdoor Samples based on Intra-class Distance for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing detectors use classifier-style probability signals or rely on rewriting, which can degrade quality and introduce new triggers. |
| Approach: | They propose to efficiently remove poisoned examples before or during fine-tuning . |
| Outcome: | The proposed method outperforms prior detectors on two machine translation datasets and one QA dataset. |
From Conversation to Evaluation: Benchmarking LLMs on Development Knowledge via SimpleDevQA (2026.findings-acl)
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Jing Zhang, Lianghong Guo, Yanlin Wang, Terry Yue Zhuo, Yong Wang, Mingwei Liu, Jiachi Chen, Ensheng Shi, Yuchi Ma, Hongyu Zhang, Zibin Zheng
| Challenge: | Existing Dev Knowledge QA benchmarks are limited in development knowledge scope and often not built from real user queries. |
| Approach: | They conduct preliminary analysis of real user–LLM dialogues from WildChat to investigate the importance of Dev Knowledge QA in AI-assisted software development scenarios. |
| Outcome: | The proposed benchmark is based on real user–LLM dialogues from WildChat. |
Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)
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Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth
| Challenge: | Open domain question answering systems often rely on information retrieved from large collections of text to answer questions. |
| Approach: | They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts . |
| Outcome: | The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training . |
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition (2025.coling-main)
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Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Current Large Language Models struggle with complex entity taxonomies in open domains and lack NER capabilities. |
| Approach: | They propose a dataset to guide LLMs' generalization in Open NER under a universal entity taxonomy. |
| Outcome: | The proposed model outperforms GPT-4 in 3 out-of-domain benchmarks across 15 datasets and 6 languages. |
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)
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| Challenge: | Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities. |
| Approach: | They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model. |
| Outcome: | The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs. |
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs (2025.emnlp-main)
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| Challenge: | Existing work focuses on domain-specific enhancements during fine-tuning, the challenge of which lies in catastrophic forgetting of knowledge across other domains. |
| Approach: | They propose a data composition framework that allows LLMs to enhance their multi-domain capabilities during supervised fine-tuning. |
| Outcome: | The proposed framework improves multi-domain fostering performance by 29.77% compared to uniform weights. |
OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents (2025.findings-acl)
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| Challenge: | Existing efforts to build GUI agents focused on the autonomous mode have failed to address the problem of over-execution. |
| Approach: | They propose an adaptive GUI agent that predicts confidence levels at each interaction step and elicits adaptive interaction. |
| Outcome: | The proposed GUI agent outperforms existing models on a complex dataset and on established benchmarks. |
Improving Consistency for Text Summarization with Energy Functions (2023.findings-emnlp)
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Qi Zeng, Qingyu Yin, Zheng Li, Yifan Gao, Sreyashi Nag, Zhengyang Wang, Bing Yin, Heng Ji, Chao Zhang
| Challenge: | Current abstractive summarization models generate inconsistent content due to the inherently noisy dataset and the discrepancy between maximum likelihood estimation based training objectives and consistency measurements. |
| Approach: | They propose a new consistency taxonomy that categorizes inconsistent content into faithfulness, factuality, and self-supportiveness. |
| Outcome: | Experiments on XSUM and CNN/DM datasets show that EnergySum mitigates the trade-off between accuracy and consistency. |
Reinforcement Learning on Pre-Training Data (2026.acl-long)
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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang
| Challenge: | Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings. |
| Approach: | They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data. |
| Outcome: | Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base. |
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning (2026.acl-long)
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Zhicong Li, Lingjie Jiang, Yulan Hu, Xingchen Zeng, Yixia Li, Xiangwen Zhang, Guanhua Chen, Zheng Pan, Xin Li, Yong Liu
| Challenge: | Current methods for training Large Language Model agents rely on static or offline critic models, which fail to adapt as the policy evolves. |
| Approach: | They propose a framework that integrates a critique and a policy to optimize the policy and critic through a synchronized co-evolutionary loop. |
| Outcome: | The proposed framework yields more stable training and higher long-horizon task success across open-world environments. |
MixRED: A Mix-lingual Relation Extraction Dataset (2024.lrec-main)
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| Challenge: | Existing research focuses on monolingual relation extraction, but there is a significant gap in understanding relation extraction in the mix-lingual scenario. |
| Approach: | They propose a task of considering relation extraction in the mix-lingual scenario . they construct a human-annotated dataset to support the task . |
| Outcome: | The proposed task evaluates state-of-the-art supervised models and large language models on the human-annotated dataset MixRED. |
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding (2025.findings-emnlp)
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Wenqi Zhou, Kai Cao, Hao Zheng, Yunze Liu, Xinyi Zheng, Miao Liu, Per Ola Kristensson, Walterio W. Mayol-Cuevas, Fan Zhang, Weizhe Lin, Junxiao Shen
| Challenge: | Existing benchmark datasets focus on short to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings. |
| Approach: | X-LeBench is a benchmark dataset designed to evaluate long egocentric video recordings . it uses a life-logging pipeline to produce realistic, coherent daily plans . |
| Outcome: | X-LeBench is a new benchmark dataset designed to evaluate long-form egocentric video understanding . the approach produces realistic, coherent daily plans aligned with real-world video data . |
DSCD: Large Language Model Detoxification with Self-Constrained Decoding (2025.emnlp-main)
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| Challenge: | Existing methods for decoding large language models (LLMs) are based on external constraints and require additional resource overhead and loss of generation fluency. |
| Approach: | They propose a method for LLMs detoxification without parameter fine-tuning that strengthens the inner token distribution while weakening that of hallucination and toxic layer during output generation. |
| Outcome: | Extensive experiments on open-source LLMs and public datasets demonstrate DSCD's state-of-the-art (SOTA) performance in detoxification and generation fluency, with superior efficiency compared to existing methods. |
Adversarial Knowledge Stimulated Contrastive Prompting for Few-shot Language Learners (2023.findings-acl)
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| Challenge: | Prompt-based fine-tuning has boosted performance of Pre-trained language models on few-shot Natural Language Understanding (NLU) tasks by employing task-specific prompts. |
| Approach: | They propose a Cloze-driven prompt framework for prompt tuning that implicitly stimulates knowledge from pre-trained language models. |
| Outcome: | The proposed framework outperforms state-of-the-art for prompt-based fine-tuning on few-shot NLU tasks. |
Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection (2026.acl-long)
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| Challenge: | Existing methods for manipulation detection and grounding focus on manipulator type classification under result-oriented supervision. |
| Approach: | They propose a reasoning-driven framework that shifts learning from outcome fitting to process modeling. |
| Outcome: | The proposed framework achieves state-of-the-art with superior generalization on large-scale datasets. |
MMUIE: Massive Multi-Domain Universal Information Extraction for Long Documents (2026.findings-eacl)
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| Challenge: | Existing document-level information extraction systems operate at the sentence level or within narrow domains due to annotation constraints. |
| Approach: | They propose a large-scale universal dataset for multi-domain, document-level information extraction from long texts. |
| Outcome: | The proposed dataset integrates traditional knowledge bases with large language models to extract fine-grained entities, aliases, and relation triples across 34 domains. |
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation (2020.coling-main)
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| Challenge: | a large-scale, general-domain dataset is needed for knowledge graph-to-text generation . data collection is expensive and data-intensive, making it difficult to get good annotation . |
| Approach: | They propose to use a large-scale, general-domain dataset to generate unsupervised text from knowledge graphs. |
| Outcome: | The proposed dataset has 1.3M text and graph examples, and is a benchmark for future research . good annotation is expensive and difficult to get, and it's difficult to check quality . |
Exploiting Abstract Meaning Representation for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Existing work attempts to address these challenges using Pretrained Language Models (PLMs) but the diversity of surface form expressions can hinder the model’s ability to capture accurate correlations, especially when the context is lengthy and complex. |
| Approach: | They propose a method known as Graph-as-Token (GST) to incorporate AMRs into PLMs to assist the model in understanding complex semantic information. |
| Outcome: | The proposed method outperforms existing methods and significantly improves performance on both Natural Questions and TriviaQA. |
MovieChats: Chat like Humans in a Closed Domain (2020.emnlp-main)
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| Challenge: | Currently, open-domain chatbots are far from satisfactory. |
| Approach: | They propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. |
| Outcome: | The proposed approach outperforms commercial systems replying on complex rules on static and interactive tests and shows that the results are remarkably good. |
AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)
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Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng
| Challenge: | Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content. |
| Approach: | They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories . |
| Outcome: | The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources. |
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels (2024.acl-long)
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| Challenge: | Existing supervised learning methods rely on human annotations, but multi-label tasks pose challenges due to the specific domain knowledge and large class sets. |
| Approach: | They propose a framework that can be used to annotate a subset of positive classes from a multi-label dataset. |
| Outcome: | The proposed framework is generalized and effective across multiple tasks. |
FloorPlan-LLaMa: Aligning Architects’ Feedback and Domain Knowledge in Architectural Floor Plan Generation (2025.acl-long)
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| Challenge: | Existing evaluation methods for floor plan generation rely on statistical metrics like FID, GED, and PSNR, which fail to evaluate using domain knowledge. |
| Approach: | They propose to use a first floor plan dataset to train a floor plan generation model based on a multi-dimensional preference score and a textual analysis to integrate architects’ professional expertise and preferences. |
| Outcome: | The proposed model outperforms baseline models in text-conditional and class-condition tasks and is more rational and aligns better with human preferences. |
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021.findings-emnlp)
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| Challenge: | Large pre-trained language models (LMs) have a surprising ability to perform zero-shot learning. |
| Approach: | They propose to fine-tune pre-trained language models to optimize the zero-shot learning objective by aggregating 43 existing datasets and annotating 441 label descriptions in a question-answering format. |
| Outcome: | The proposed model outperforms a same-sized QA model and the previous SOTA zero-shot learning system on unseen tasks. |
Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization (2024.lrec-main)
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| Challenge: | Lip reading is a process of interpreting silent speech from visual lip movements . but lip reading in cross-speaker scenarios poses a challenging problem due to inter-speech variability . |
| Approach: | They propose to exploit lip landmark-guided visual clues instead of mouth-cropped images as input features. |
| Outcome: | Experimental results show that the proposed approach reduces speaker-specific appearance characteristics in cross-speaker scenarios. |
Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models (2021.acl-short)
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| Challenge: | Experimental results show that a sequence-to-sequence learning framework with neural networks can be effective for Chinese Spelling Correction (CSC) |
| Approach: | They propose a sequence-to-sequence learning framework with neural networks that generates more valuable training instances and adds task-specific examples to enhance the model. |
| Outcome: | The proposed method improves generalization and robustness of multiple CSC models across three datasets. |
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)
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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi
| Challenge: | Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information. |
| Approach: | They propose a compiler feedback-based code generation approach that leverages static analysis to identify mismatches between the generated code and the project's context. |
| Outcome: | The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models. |
PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026.acl-long)
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Xiangfeng Wang, Hangyu Guo, Yanlin Lai, Mitt Huang, Liang Zhao, Chengyuan Yao, Yinmin Zhang, Qi Han, null Xiaoxiaoren, Chun Yuan, Tong Xu, Zheng Ge, Xiangyu Zhang, Daxin Jiang
| Challenge: | Current outcome-centric verification paradigms neglect potential errors in the derivation process. |
| Approach: | They propose a process-aware RLVR training paradigm utilizing verifiers selected via **PRIME**. |
| Outcome: | The proposed approach outperforms the baseline verification paradigm on AIME24, AIME25, and Beyond-AIME models. |
Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization (2023.findings-acl)
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| Challenge: | Existing methods for minimizing the worst-case loss of annotated groups are lacking in practice due to expensive annotations and privacy issues. |
| Approach: | They propose a distributionally robust optimization framework that relaxes group identification into direct parameterization by using an interactive training mode. |
| Outcome: | The proposed method outperforms state-of-the-art methods on synthetic and real-world text classification tasks. |
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset (2020.tacl-1)
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| Challenge: | Despite the significant contributions to the community, there is still a gap between existing dialogue corpora and real-life human dialogue data. |
| Approach: | They propose to develop Chinese cross-domain wizard-of-oz task-oriented dataset CrossWOZ with rich annotations of dialogue states and dialogue acts on both user and system sides. |
| Outcome: | The proposed dataset contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. |
Eureka: Neural Insight Learning for Knowledge Graph Reasoning (2022.coling-1)
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| Challenge: | Existing knowledge embedding methods have limited performance on knowledge graph reasoning tasks . eureka is empowered to learn seen relations with sufficient training triples . |
| Approach: | They propose a neural insight learning framework called Eureka to bridge the “seen” to “unsea” gap . Eureca is empowered to learn seen relations with sufficient training triples while providing flexibility to learn unseen relations given only one trigger . |
| Outcome: | The proposed framework outperforms state-of-the-art models on seen and unseen relations . it can learn seen and unseen relationships with sufficient training triples . |
Introducing Compiler Semantics into Large Language Models as Programming Language Translators: A Case Study of C to x86 Assembly (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) can be used to translate high-level programming languages to machine instructions. |
| Approach: | They propose two methods to solve a problem known as neural compilation by using a 13B model with a behavioral accuracy of over 91%. |
| Outcome: | The proposed approach outperforms the larger model by over 50% and achieves a behavioral accuracy of over 91% while outperforming the GPT-4 Turbo model. |
Scaling LLMs’ Social Reasoning: Sprinkle Cognitive “Aha Moment” into Fundamental Long-thought Logical Capabilities (2025.findings-acl)
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| Challenge: | Existing studies have examined how large language models’ social reasoning capabilities evolve during model size scaling or reasoning tokens scaling. |
| Approach: | They propose to optimize evaluation of Large Language Models from both data and model perspectives and to analyze their reasoning trajectories to identify notable cognitive "Aha Moments" |
| Outcome: | The proposed model outperforms the o1-preview model by 19.0 points in the evaluation of large language models. |
Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026.findings-acl)
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Bang Zhang, Ruotian Ma, Qingxuan Jiang, Peisong Wang, Jiaqi Chen, Zheng Xie, Xingyu Chen, Yue Wang, Fanghua Ye, Jian Li, Yifan Yang, Zhaopeng Tu, Xiaolong Li
| Challenge: | Large language models (LLMs) have evolved from statistical sequence predictors to sophisticated autonomous agents capable of reasoning, planning, and sustaining multi-turn conversa-tions. |
| Approach: | They propose a system that instantiates a "Sentient Agent" that simulates human-like emotional changes and inner thoughts to provide a more realistic evaluation of the model in multi-turn conversations. |
| Outcome: | The proposed framework measures the agent's higher-order social cognition in multi-turn conversations. |
Multimodal Dialogue Response Generation (2022.acl-long)
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Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang
| Challenge: | Existing studies focus on multimodal dialogue models but neglect generation methods. |
| Approach: | They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain. |
| Outcome: | Experiments show that the proposed model can generate informative text and high-resolution image responses. |
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models (2024.acl-demos)
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| Challenge: | Efficient fine-tuning of large language models requires non-trivial efforts to implement these methods on different models. |
| Approach: | They propose a framework that democratizes the fine-tuning of large language models by integrating a suite of efficient training methods into one framework. |
| Outcome: | The proposed framework is able to scale to 100+ LLMs without coding and receives over 25,000 stars and 3,000 forks. |
DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)
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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng
| Challenge: | Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages. |
| Approach: | They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation . |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on two benchmark datasets. |
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods to translate sentences to other languages using heuristics are challenging. |
| Approach: | They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them. |
| Outcome: | The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics. |
DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models (2026.findings-acl)
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Lifan Zheng, Xue Yang, Jiawei Chen, Chenyan WU, Jingyuan Zhang, Fanheng Kong, Xinyi Zeng, Xiang Chen, Yu Tian
| Challenge: | Current methods for editing personality traits in large language models can change personalities but reduce performance. |
| Approach: | They propose a novel paradigm for personality editing that locates and edits LLM neurons and enables competitive personality control at inference time. |
| Outcome: | Experiments on LLaMA-3-8B-Instruct and Qwen2.5-7B-instruct show that the proposed approach can improve performance and improve performance. |
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (2023.acl-long)
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| Challenge: | Existing empathetic dialogue models only consider the affective aspect of empathy, which limits the capability of emotional response generation. |
| Approach: | They propose a model that aligns the user's cognition and affection at both the coarse-grained and fine-grounded levels and then automatically and manually evaluates the model. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates more empathetic and informative responses. |
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation (2025.acl-long)
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Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen, Wen Wang, Siqi Zheng, Jiaqing Liu, Hai Yu, Chao-Hong Tan, Zhihao Du, ShiLiang Zhang
| Challenge: | Full-duplex spoken dialogue systems allow simultaneous bidirectional communication . low latency and natural interactions in full-duplice systems remains a challenge . |
| Approach: | They propose a multi-stage post-training scheme that adapts a text large language model into a speech-text dialogue LLM. |
| Outcome: | The proposed model can model human conversation behaviors with low latency and natural interactions with low delay. |
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples. |
| Approach: | They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen. |
| Outcome: | The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters. |
Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals (2024.lrec-main)
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| Challenge: | Existing models that extract discrete inputs into fixed-length representations are vulnerable to adversarial attacks that place perturbations on clean inputs to fool DNNs. |
| Approach: | They propose to inspect the subspaces of sample features through spectral analysis to better understand adversarial attacks. |
| Outcome: | The proposed strategy enables the model to inherently suppress adversaries, which boosts model robustness and motivates new directions of effective adversarial defense. |
SMEC:Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression (2025.emnlp-main)
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| Challenge: | Large language models generate high-dimensional embeddings that capture rich semantic and syntactic information. |
| Approach: | They propose a training framework to reduce dimensionality and complexity of large language models. |
| Outcome: | Experiments on image, text, and multimodal datasets show that the proposed training framework reduces dimensionality while maintaining performance. |
Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)
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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang
| Challenge: | Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials. |
| Approach: | They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located. |
| Outcome: | The proposed method outperforms previous detection methods by large margins on three text classification tasks. |
Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning (2026.findings-acl)
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| Challenge: | Large Language Model (LLM)-based agents extend the utility of LLMs by interacting with dynamic environments. |
| Approach: | They propose a parameter fusion framework based on directional consensus evaluation that disentangles knowledge updates through a two-stage process. |
| Outcome: | The proposed framework disentangles knowledge updates through a two-stage process with minimal computational overhead and parameter updates. |
SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)
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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen
| Challenge: | Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge. |
| Approach: | They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance. |
| Outcome: | Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points. |
Fast Nearest Neighbor Machine Translation (2022.findings-acl)
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| Challenge: | Fast kNN-MT uses the entire corpus as the datastore for the nearest neighbor search . knn-MT is two-orders slower than vanilla MT models . |
| Approach: | They propose a fast kNN-MT model that uses the entire corpus as the datastore for nearest neighbor search. |
| Outcome: | The proposed model is two-orders faster than kNN-MT and is only two times slower than the standard model. |
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning (2025.findings-acl)
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| Challenge: | Active Learning (AL) allows users to provide focused annotations to integrate human preferences and domain knowledge into machine learning models. |
| Approach: | They propose a counterfactual data augmentation approach inspired by Variation Theory to generate targeted variations along key conceptual dimensions. |
| Outcome: | The proposed approach achieves significantly higher performance when there are fewer annotated data, showing it can address the cold start problem in Active Learning. |
SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis (2022.naacl-main)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) aims to predict sentiment polarity towards aspects in sentences . a novel model for ABSA is proposed, but how to harness it is still a challenge . |
| Approach: | They propose a syntactic and semantic enhanced Graph Convolutional Network (SSEGCN) model for ABSA task using aspect-aware attention mechanism and self-attention. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmark datasets. |
Finding Influential Instances for Distantly Supervised Relation Extraction (2022.coling-1)
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| Challenge: | Distant supervision models suffer from high label noise and are not reliable for DS. |
| Approach: | They propose a model-agnostic instance sampling method for relation extraction (RE) by influence function, namely REIF. |
| Outcome: | The proposed method reduces the computational complexity from O(mn) to O(1), with analyzing its robustness on the selected sampling function. |
Plan Dynamically, Express Rhetorically: A Debate-Driven Rhetorical Framework for Argumentative Writing (2025.emnlp-main)
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| Challenge: | Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives. |
| Approach: | They propose a debate-driven rhetorical framework for argumentative writing that integrates Bitzer’s rhetorical situation theory to improve logical depth, argumentative diversity, and rhetorical persuasiveness. |
| Outcome: | The proposed framework improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models. |
All Information is Valuable: Question Matching over Full Information Transmission Network (2022.findings-naacl)
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| Challenge: | Existing methods for question matching only transmit one kind of information while failing to utilize both kinds of information simultaneously. |
| Approach: | They propose a question matching network that can transmit both representation and interactive information together in a simultaneous fashion. |
| Outcome: | The proposed approach outperforms strong baseline models on two standard benchmarks. |
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)
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| Challenge: | Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results. |
| Approach: | They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
| Outcome: | The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark (2024.findings-acl)
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Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, Kai Chen
| Challenge: | Recent advances in large language models have showcased significant improvements in mathematics, but traditional benchmarks like GSM8k offer a unidimensional perspective. |
| Approach: | MathBench is a benchmark that rigorously assesses the mathematical capabilities of large language models. |
| Outcome: | MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark (2025.acl-long)
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Xiang Yue, Tianyu Zheng, Yuansheng Ni, Yubo Wang, Kai Zhang, Shengbang Tong, Yuxuan Sun, Botao Yu, Ge Zhang, Huan Sun, Yu Su, Wenhu Chen, Graham Neubig
| Challenge: | Recent advances in multimodal large language models have led to progress in tackling complex reasoning tasks that combine textual and visual information. |
| Approach: | They introduce a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. |
| Outcome: | The proposed model performs lower on MMMU-Pro than on the previous benchmark, ranging from 16.8% to 26.9%. |
Weight Perturbation as Defense against Adversarial Word Substitutions (2022.findings-emnlp)
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| Challenge: | Existence and pervasiveness of textual adversarial examples have raised serious concerns to security-critical applications. |
| Approach: | They propose to perform weight perturbations in the parameter space rather than the input feature space to improve adversarial robustness of NLP models. |
| Outcome: | The proposed method improves adversarial robustness of models by performing weight perturbations in the parameter space rather than the input feature space. |
Connectivity Patterns are Task Embeddings (2023.findings-acl)
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Zhiheng Xi, Rui Zheng, Yuansen Zhang, Xuanjing Huang, Zhongyu Wei, Minlong Peng, Mingming Sun, Qi Zhang, Tao Gui
| Challenge: | Existing methods for predicting inter-task transferability are sparse and task-specific. |
| Approach: | They propose a method that uses connectivity patterns of neurons as a unique identifier associated with a task. |
| Outcome: | The proposed method outperforms baselines in predicting inter-task transferability across data regimes and transfer settings while keeping high efficiency in computation and storage. |
ESPRIT: Explaining Solutions to Physical Reasoning Tasks (2020.acl-main)
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Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Neural networks lack the ability to reason about qualitative physics and cannot generalize to scenarios and tasks unseen during training. |
| Approach: | They propose a framework for reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. |
| Outcome: | The proposed framework generates explanations of how the physical simulation will causally evolve so that an agent or a human can reason about a solution using interpretable descriptions. |
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions (2025.emnlp-main)
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| Challenge: | Existing approaches for optimizing human annotation efforts are limited . et al., 2015) suggest that densely annotated image captions improve vision-language alignment . |
| Approach: | They propose an AI-in-the-loop methodology to maximize the number of annotated samples and improve their comprehensiveness under fixed budget constraints. |
| Outcome: | The proposed method improves annotation speed and retrieval performance over the parallel method. |
Debt Collection Negotiations with Large Language Models: An Evaluation System and Optimizing Decision Making with Multi-Agent (2025.findings-acl)
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| Challenge: | Debt collection negotiations (DCN) are vital for managing non-performing loans (NPLs) prior systems lacking dynamic negotiation and real-time decision-making capabilities. |
| Approach: | They propose a framework for debt negotiation that incorporates planning and judging modules to improve decision rationality. |
| Outcome: | The proposed framework improves decision rationality and integrates planning and judging modules to improve decision rationalness. |
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |
Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)
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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values. |
| Approach: | They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values . |
| Outcome: | The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets. |
CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)
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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen
| Challenge: | Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected. |
| Approach: | They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans . |
| Outcome: | The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test . |
Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake? (2023.findings-emnlp)
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Yuwei Bao, Keunwoo Yu, Yichi Zhang, Shane Storks, Itamar Bar-Yossef, Alex de la Iglesia, Megan Su, Xiao Zheng, Joyce Chai
| Challenge: | despite advances in AI, it remains a challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans in various tasks. |
| Approach: | They propose to use a multimodal benchmark dataset to study whether interactive task guidance systems can be quickly adapted to perceptually enabled tasks. |
| Outcome: | The proposed models demonstrate fair performances in some cases with no training . the results will provide a stepping stone for future work on situated task guidance . |
The Lessons of Developing Process Reward Models in Mathematical Reasoning (2025.findings-acl)
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Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
| Challenge: | a recent study shows that process reward models can make mistakes, leading to wrong conclusions. |
| Approach: | They propose a consensus filtering mechanism that integrates MC estimation with LLM-as-a-judge to improve model performance and data efficiency. |
| Outcome: | The proposed model outperforms existing open-source alternatives and provides practical guidelines for future research. |
Characterizing the Impacts of Instances on Robustness (2023.findings-acl)
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Rui Zheng, Zhiheng Xi, Qin Liu, Wenbin Lai, Tao Gui, Qi Zhang, Xuanjing Huang, Jin Ma, Ying Shan, Weifeng Ge
| Challenge: | Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances. |
| Approach: | They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training. |
| Outcome: | The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training. |
FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure (2026.acl-demo)
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Fan Zhang, Mingzi Song, Rania Elbadry, Yankai Chen, Shaobo Wang, Yixi Zhou, Xunwen Zheng, Yueru He, Yuyang Dai, Georgi Nenkov Georgiev, Ayesha Gull, Muhammad Usman Safder, Fan Wu, Liyuan Meng, Fengxian Ji, Junning Zhao, Xueqing Peng, Jimin Huang, YU Chen, Xue Liu, Preslav Nakov, Zhuohan Xie
| Challenge: | FinReporting is an agentic workflow for localized cross-jurisdiction financial reporting . existing approaches assume a single-market setting and overlook structural differences across jurisdictions . |
| Approach: | They propose a workflow that decomposes financial reporting into auditable stages . they use Large Language Models to extract and summarize corporate disclosures . |
| Outcome: | The proposed system decomposes reporting into auditable stages . it improves consistency and reliability under heterogeneous reporting regimes. |
Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)
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Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao
| Challenge: | Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs . |
| Approach: | They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained . |
| Outcome: | The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency. |
Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel at generating code for high-resource programming languages (HRPLs) however, they struggle significantly with low-resourced programming languages such as D, exacerbating the digital divide. |
| Approach: | They propose a method to generate LRPL data using LLM's general knowledge, HRPL proficiency, and in-context learning capabilities. |
| Outcome: | The proposed method improves on R, D, Racket, and Bash, while maintaining the same quality. |
ModSCAN: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities (2024.emnlp-main)
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| Challenge: | Large vision-language models have been widely used but stereotypical biases are unexplored. |
| Approach: | They propose a framework to SCAN stereotypical bias within large vision-language models . they examine stereotype biases with respect to gender and race in three scenarios . |
| Outcome: | The proposed framework can reduce stereotypical biases in large vision-language models . the currently popular models show significant stereotype biase . |
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format (2023.emnlp-demo)
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Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, Minlie Huang
| Challenge: | Existing tools for building TOD systems often lack a user-friendly interface . a toolkit with advanced, easily integrable modules is needed to bridge this gap . |
| Approach: | They propose a multifaceted dialogue system toolkit that integrates diverse datasets and models with a streamlined training process and in-depth evaluation tools. |
| Outcome: | The proposed toolkit combines RL and transfer learning to support the rapid development and evaluation of robust dialogue policies. |
ScEdit: Script-based Assessment of Knowledge Editing (2025.findings-acl)
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Xinye Li, Zunwen Zheng, Qian Zhang, Dekai Zhuang, Jiabao Kang, Liyan Xu, Qingbin Liu, Xi Chen, Zhiying Tu, Dianhui Chu, Dianbo Sui
| Challenge: | Knowledge Editing (KE) has gained increasing attention, yet current evaluation frameworks do not integrate KE into real-world application scenarios. |
| Approach: | They propose a script-based benchmark which encompasses both counterfactual and temporal edits and integrates token-level and text-level evaluation methods. |
| Outcome: | The proposed method combines token-level and text-level evaluation methods with a new fact-based evaluation framework. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection (2026.acl-long)
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| Challenge: | Existing models that assume users to be static, rational agents with fixed preferences fail to capture rich behavioral heterogeneity in real-world debt collection scenarios. |
| Approach: | They propose a public persona-enriched debt collection benchmark that highlights behavioral heterogeneity in negotiation. |
| Outcome: | The proposed benchmark outperforms existing models in realistic scenarios using 16 state-of-the-art LLMs. |
G-Cap: A Game Character Caption Generator (2026.acl-long)
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| Challenge: | Existing studies on Large Vision-Language Models (LVLMs) primarily focus on real-world scenarios, leaving surreal, highly stylized, and semantically hybrid virtual-world situations significantly underexplored. |
| Approach: | They propose to use a manually annotated benchmark to evaluate LVLMs' ability to perceive and describe game character from the virtual-world. |
| Outcome: | The proposed task evaluates LVLMs’ ability to perceive and describe game character from the virtual-world. |
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis (2024.findings-acl)
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| Challenge: | Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects . previous studies have proposed using fixed examples for instruction tuning . |
| Approach: | They propose an instruction learning method with retrieval-based example ranking for ABSA tasks. |
| Outcome: | The proposed method is superior to existing models on three ABSA subtasks. |
A Unified Generative Framework for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing complicated ABSA models focus on subtasks, which leads to complicated solutions . et al., j. c. d. r., and j dr. s. v. present a unified approach to solve seven subtask tasks in one framework. |
| Approach: | They redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexe . they exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to end framework. |
| Outcome: | The proposed framework achieves substantial performance gain and provides a real unified solution for the whole ABSA subtasks. |
CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks (2025.findings-emnlp)
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| Challenge: | Existing work on multi-agent collaborative tasks in Minecraft is limited due to inefficiency and limited fault tolerance. |
| Approach: | They propose a framework that incorporates causality to manage dependencies among subtasks. |
| Outcome: | The proposed framework achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. |
Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization on Multi-party Conversation (2025.acl-long)
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Luyao Cheng, Hui Wang, Chong Deng, Siqi Zheng, Yafeng Chen, Rongjie Huang, Qinglin Zhang, Qian Chen, Xihao Li, Wen Wang
| Challenge: | Mainstream speaker diarization systems rely only on acoustic information, making it challenging in complex aural environments. |
| Approach: | They propose a multimodal approach that integrates audio, visual, and semantic cues to enhance speaker diarization. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on multi-party conversations . it integrates audio-visual-semantic cues into the clustering process for acoustic speaker embeddings . |
Learning Algebraic Recombination for Compositional Generalization (2021.findings-acl)
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Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, Dongmei Zhang
| Challenge: | Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. |
| Approach: | They propose an end-to-end neural model to learn algebraic recombination for compositional generalization. |
| Outcome: | The proposed model is based on two realistic and comprehensive compositional generalization benchmarks. |
Sugar-Coated Poison: Benign Generation Unlocks Jailbreaking (2025.findings-emnlp)
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| Challenge: | Existing methods to jailbreak large language models rely on black-box manipulation of prompt templates, resulting in high costs and poor generalizability. |
| Approach: | They propose a sugar-coated poison attack paradigm that uses a "semantic reversal" strategy to induce the model into a safety response mode. |
| Outcome: | The proposed attack paradigm outperforms baselines in the study. |
LM-Searcher: Cross-domain Neural Architecture Search with LLMs via Unified Numerical Encoding (2025.emnlp-main)
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Yuxuan Hu, Jihao Liu, Ke Wang, Jinliang Zheng, Weikang Shi, Manyuan Zhang, Qi Dou, Rui Liu, Aojun Zhou, Hongsheng Li
| Challenge: | Recent advances in Large Language Models have opened new avenues for solving complex optimization problems, including Neural Architecture Search (NAS). |
| Approach: | They propose a framework that leverages LLMs for cross-domain neural architecture optimization without extensive domain-specific tuning. |
| Outcome: | The proposed framework achieves competitive performance in both in-domain and out-of-domain tasks. |
Knowledge-Centric Hallucination Detection (2024.emnlp-main)
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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang
| Challenge: | Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate. |
| Approach: | They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference. |
| Outcome: | The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. |
Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language (2026.findings-acl)
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Peijie Wang, Ming-Liang Zhang, Jun Cao, Chao Deng, Dekang Ran, Pi Bu, Hongda Sun, Xuan Zhang, Yingyao Wang, Jun Song, Bo Zheng, Fei Yin, Cheng-Lin Liu
| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across various vision reasoning tasks. |
| Approach: | They propose a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. |
| Outcome: | The proposed language achieves state-of-the-art parsing performance and significantly boosts MLLMs’ capabilities for downstream geometry reasoning tasks. |
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)
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Jiaqi Weng, Han Zheng, Hanyu Zhang, Ej Zhou, Qinqin He, Jialing Tao, Hui Xue, Zhixuan Chu, Xiting Wang
| Challenge: | Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored. |
| Approach: | They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror . |
| Outcome: | The proposed framework reduces interpretation cost by 55% and improves safety-critical features. |
Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023.findings-emnlp)
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| Challenge: | Existing approaches ignore relationships between medical items and statuses in the multi-turn doctor-patient dialogue. |
| Approach: | They propose a task to extract structured medical information from free text dialogues . they propose 'Dialogue Medical Information Extraction' to model relationships between items . |
| Outcome: | The proposed model outperforms previous models and achieves state-of-the-art performance on the public benchmark data set. |
Scaling Laws for Code: A More Data-Hungry Regime (2026.acl-long)
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Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, Rongyi Zhang, Houyi Li, Siming Huang, YuanTao Fan, Wanxiang Che
| Challenge: | Code Large Language Models (LLMs) are revolutionizing software engineering, but scaling laws are primarily analyzed on Natural Language (NL). |
| Approach: | They fit Chinchilla law and Farsser law to test scaling laws for code . they find code is more data-hungry and requires higher data-to-parameter ratio . |
| Outcome: | The proposed scaling laws show that the more expressive Farsser law offers greater accuracy and scales with model size. |
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision (2021.emnlp-main)
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| Challenge: | Sequence labeling aims to predict fine-grained sequences of labels for text, but lack of token-level annotated data hinders the effectiveness of supervised methods. |
| Approach: | They propose a Meta Teacher-Student (MetaTS) Network to alleviate data scarcity by leveraging large multilingual unlabeled data. |
| Outcome: | The proposed meta learning method alleviates data scarcity by leveraging large multilingual unlabeled data. |
Reverse Chain: A Generic-Rule for LLMs to Master Multi-API Planning (2024.findings-naacl)
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| Challenge: | Existing approaches to enable large language models to implement function calling are limited in their tool-use capabilities. |
| Approach: | They propose a controllable, target-driven approach to empower LLMs to operate external APIs only via prompts. |
| Outcome: | The proposed approach limits LLMs to executing simple tasks, e.g., API Selection and Argument Completion. |
Hidden Ghost Hand: Unveiling Backdoor Vulnerabilities in MLLM-Powered Mobile GUI Agents (2025.findings-emnlp)
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Pengzhou Cheng, Haowen Hu, Zheng Wu, Zongru Wu, Tianjie Ju, Daizong Ding, Zhuosheng Zhang, Gongshen Liu
| Challenge: | MLLM-powered GUI agents expose multiple interaction-level triggers, causing backdoor attacks . backdoor injection maximizes feature difference across sample classes, improving flexibility . |
| Approach: | They propose a framework for red-teaming backdoor attacks using MLLMs . they construct composite triggers by combining goal and interaction levels . |
| Outcome: | The proposed framework is effective and stealthy for red-teaming backdoor attacks. |
Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models (2026.findings-acl)
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| Challenge: | Existing safety-related methodologies for large language models are lacking . despite advances in safety alignment techniques, safeguarding LLMs during adaptation to various tasks remains a challenge. |
| Approach: | They propose a framework to quantify how different parameters affect LLM safety . they propose two targeted intervention paradigms for safety enhancement and preservation . |
| Outcome: | The proposed framework reveals safety-critical patterns across different LLM architectures. |
Retrieval-Augmented Language Models are Mimetic Theorem Provers (2025.findings-emnlp)
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Wenjie Yang, Ruiyuan Huang, Jiaxing Guo, Zicheng Lyu, Tongshan Xu, Shengzhong Zhang, Lun Du, Da Zheng, Zengfeng Huang
| Challenge: | Large language models often fail to provide rigorous proof-based reasoning for research-level mathematics. |
| Approach: | They propose a simple yet effective RAG framework that augments retrieved proofs with queries and document contexts to improve retrieval performance. |
| Outcome: | The proposed framework improves retrieval performance by 34.19% . dual RAG can be used to prove research-level theorems in theoretical machine learning . |
PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026.findings-acl)
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Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Jun Dai, Xiaoyan Sun, Ping Chen
| Challenge: | Existing defense mechanisms lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content. |
| Approach: | They propose a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts. |
| Outcome: | Experiments show that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods. |
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Large Multimodal Models (LMMs) can process text, images, and audio, but they introduce privacy vulnerabilities. |
| Approach: | They propose a compositional structured prompt attack to exploit MRAG privacy vulnerabilities . they show that LMMs can generate outputs resembling retrieved content . |
| Outcome: | The proposed approach generates outputs resembling retrieved content and exposes sensitive information. |
LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation (2022.emnlp-main)
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| Challenge: | Recent advances struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. |
| Approach: | They propose to train different MMT models to support translations between different languages. |
| Outcome: | The proposed model is able to handle the above issues by providing a shared semantic space for multiple languages. |
Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
| Approach: | They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search. |
| Outcome: | The proposed method improves on previous work on adversarial robustness evaluation. |
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems (2024.emnlp-industry)
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| Challenge: | Differential privacy (DP) and federated learning (FL) are used for language models training in production mobile keyboard applications. |
| Approach: | They propose a variant of DP-FTRL that uses a correlated noise mechanism to train on-device language models. |
| Outcome: | The proposed method improves privacy-utility trade-off and memory efficiency over existing FL methods while simplifying usage requirements and reducing memory. |
MuBench: Assessment of Multilingual Capabilities of Large Language Models Across 61 Languages (2026.findings-acl)
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| Challenge: | Existing evaluation datasets lack cross-lingual alignment, leaving assessments of multilingual capabilities fragmented in both language and skill coverage. |
| Approach: | They propose to use multilingual consistency as a complementary metric to assess performance bottlenecks and guide model improvement. |
| Outcome: | The proposed model lacks cross-lingual alignment and language coverage gaps between state-of-the-art models. |
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs? (2024.naacl-long)
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Yan Zeng, Hanbo Zhang, Jiani Zheng, Jiangnan Xia, Guoqiang Wei, Yang Wei, Yuchen Zhang, Tao Kong, Ruihua Song
| Challenge: | Recent advances in GPT-4V have demonstrated remarkable multi-modal capabilities in processing image inputs and following open-ended instructions. |
| Approach: | They propose a plug-and-play technique to enhance multi-modal LLMs . they propose 'lynx' to train multi-modal LLM models . |
| Outcome: | The proposed training strategy improves understanding accuracy and instruction-following proficiency of multi-modal models. |
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have seen remarkable progress for medical decision-making, however, they are designated for specific classification or generative tasks and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing. |
| Approach: | They propose a framework that tackles discriminative and generative multimodal medical tasks using multimodal alignment, instruction tuning and routing. |
| Outcome: | The proposed model can achieve superior performance to or on par with state-of-the-art baselines while only requiring 30%-50% of activated model parameters. |
T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering (2025.emnlp-main)
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Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, Xian Wu
| Challenge: | Existing efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions but add human bias to the reasoning process. |
| Approach: | They propose a framework that dynamically adapts reasoning depth based on question complexity. |
| Outcome: | Experimental results show that the proposed framework achieves higher accuracy than baseline methods and reduces computational overhead by up to 25.2%. |
Learning Goal-oriented Dialogue Policy with opposite Agent Awareness (2020.aacl-main)
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| Challenge: | Existing approaches for goal-oriented dialogue policy learning focus on the target agent policy and treat the opposite agent policy as part of the environment. |
| Approach: | They propose a framework for policy learning in goal-oriented dialogues that uses the opposite agent's policy estimation to improve the target agent by regarding it as part of the target policy. |
| Outcome: | The proposed framework shows superior performance over state-of-the-art models on cooperative and competitive dialogue tasks. |
Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing RAG methods focus on enhancing LLM robustness to low-quality retrieval, but neither address permutation sensitivity. |
| Approach: | They propose a method that exploits permutation sensitivity to mitigate hallucinations in Large Language Models. |
| Outcome: | The proposed model improves answer accuracy, reasoning consistency, and generalization across datasets, retrievers, and input lengths compared with strong baselines. |
Investigating Glyph-Phonetic Information for Chinese Spell Checking: What Works and What’s Next? (2023.findings-acl)
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| Challenge: | Pre-trained Chinese language models have shown impressive performance on a wide range of NLP tasks, but the generalization ability of these models has not been well understood. |
| Approach: | They propose to use glyph-phonetic information to improve Chinese spell checking models . they propose a new, more challenging, and practical setting for testing the generalizability of CSC models. |
| Outcome: | The proposed model incorporates glyph-phonetic information and is more challenging and practical. |
GRAPHIA: Harnessing Social Graph Data to Enhance LLM-Based Social Simulation (2026.acl-long)
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| Challenge: | Social graphs provide high-quality supervision signals that encode local interactions and global network structure, yet they remain underutilized for LLM training. |
| Approach: | They propose a general LLM-based social graph simulation framework that leverages graph data as supervision for LLM training. |
| Outcome: | The proposed framework improves micro-level alignment by 6.1% on three real-world networks compared to the strongest baseline. |
ORTicket: Let One Robust BERT Ticket Transfer across Different Tasks (2024.lrec-main)
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| Challenge: | Pretrained language models are susceptible to subtle perturbations and require multiple adversarial training during fine-tuning to improve their robustness. |
| Approach: | They propose a novel adversarial defense method ORTicket that fine-tunes a model for downstream tasks. |
| Outcome: | The proposed method achieves comparable robustness to other defense methods while maintaining the efficiency of fine-tuning. |
Toward Optimal LLM Alignments Using Two-Player Games (2025.findings-emnlp)
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Rui Zheng, Hongyi Guo, Zhihan Liu, Xiaoying Zhang, Yuanshun Yao, Xiaojun Xu, Zhaoran Wang, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang, Yang Liu, Hang Li
| Challenge: | Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values. |
| Approach: | They propose an alignment method based on a two-agent game consisting of an adversarial agent and a defensive agent. |
| Outcome: | The proposed method improves on a two-agent game with an adversarial agent and a defensive agent. |
Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)
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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao
| Challenge: | In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation. |
| Approach: | They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns. |
| Outcome: | The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy. |
Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction (2025.naacl-long)
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| Challenge: | Existing approaches involve models iterating and improving their previous responses based on internal reflection ability or external feedback. |
| Approach: | They propose a reflection framework that leverages meta-thoughts and self-consistency to enhance the iterative reflection capability of Large LanguageModels. |
| Outcome: | The proposed framework achieves an average improvement of 10.1% over established baselines in mathematical and commonsense reasoning tasks, highlighting its efficacy and applicability. |
Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization (2024.findings-emnlp)
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| Challenge: | Existing methods to steer LLMs towards human preference suffer from noisy positive-negative training pairs. |
| Approach: | They propose a distributional preference optimization method which maximizes discrepancy between dispreferred responses and generated non-negative ones. |
| Outcome: | The proposed method achieves comparable generation quality and surpasses the latest strong baselines in producing less harmful and more informative responses with better training stability and faster convergence. |
CASN:Class-Aware Score Network for Textual Adversarial Detection (2023.acl-long)
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| Challenge: | Existing approaches to counteract adversarial attacks can be divided into two directions, adversarials defense and adversarially detection. |
| Approach: | They propose a score-based generative method to implicitly model the data distribution using a log-density distribution and supervised contrastive learning to guide the estimation using label information. |
| Outcome: | The proposed method improves on three text classification tasks on four advanced attack algorithms. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency (2023.acl-long)
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Zhenyu Lei, Herun Wan, Wenqian Zhang, Shangbin Feng, Zilong Chen, Jundong Li, Qinghua Zheng, Minnan Luo
| Challenge: | Existing methods to identify bots rely on text or networks alone . text-graph interactions and semantic consistency are essential improvements to combat bot evolution. |
| Approach: | They propose to combine text-graph interaction and semantic Consistency to model Twitter bots' behavior based on attention weights and a text-graphic interaction module to enable information exchange across modalities in the learning process. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two widely adopted datasets and the results are consistent with previous work. |
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)
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Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu
| Challenge: | Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation. |
| Approach: | They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus. |
| Outcome: | The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks. |
EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models (2026.acl-long)
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Xingrun Xing, Zheng Liu, Shitao Xiao, Boyan Gao, Yiming Liang, Haokun Lin, Xianlin Zeng, Guoqi Li, Jiajun Zhang
| Challenge: | Large language models (LLMs) driven by scaling laws can be developed in large model sizes. |
| Approach: | They propose a pruning-aware pretraining approach that decouples LLM pruning from direct pretraining. |
| Outcome: | The proposed model outperforms pretraining models with 100M 1B parameters in commen sense benchmarks. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models (2026.findings-acl)
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| Challenge: | Experiments show that reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs) but requires a key prerequisite: the model must already be able to generate high-utility reasoning paths with non-negligible probability. |
| Approach: | They propose a framework that uses answer-conditioned reasoning as a variational surrogate for question-only reasoning. |
| Outcome: | Experiments on 11 benchmarks and 3 models show that RAVR reduces hesitation, strengthens conclusion consolidation, and promotes problem-specific strategies in reasoning. |
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture (2025.naacl-long)
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| Challenge: | balancing the training budget, downstream performance, and general capabilities of large language models remains a challenge in many applications. |
| Approach: | They propose a mixture of expert framework based on Soft LoRA and Identity Mixture . SLIM allows dynamic routing between LoRA adapters and identity layers . |
| Outcome: | The proposed framework reduces training cost while maintaining general capabilities . it can be open-sourced upon publication. |
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation (2025.findings-acl)
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| Challenge: | Existing work on 3D radiograph report generation focuses on 2D images, but 3D medical images provide more comprehensive diagnostic information. |
| Approach: | They propose a comprehensive training recipe for building high-performing VLMs for 3DRRG using a publicly available 3D CT-report dataset. |
| Outcome: | The proposed model achieves superior performance across different model sizes and input 3D medical image resolutions. |
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)
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Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang
| Challenge: | Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks. |
| Approach: | They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration. |
| Outcome: | The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks. |
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens (2023.emnlp-main)
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Chen Chen, Bowen Zhang, Liangliang Cao, Jiguang Shen, Tom Gunter, Albin Jose, Alexander Toshev, Yantao Zheng, Jonathon Shlens, Ruoming Pang, Yinfei Yang
| Challenge: | State-of-the-art contrastive learning models like CLIP and ALIGN are less interpretable and suffer from inferior accuracy than dense representations. |
| Approach: | They extend CLIP and ALIGN models to build a sparse semantic representation that is interpretable and easy to integrate with existing retrieval systems. |
| Outcome: | The proposed model outperforms CLIP and ALIGN models on image and text retrieval tasks with a 4.9% and +4.3% improvement on COCO-5k textimage and imagetext retrieval respectively. |
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)
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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun
| Challenge: | Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts. |
| Outcome: | The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs. |
MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for text classification fail to generalize to unseen classes with very few labeled text instances per class. |
| Approach: | They propose a meta-learning method which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning. |
| Outcome: | Experiments show that the proposed method outperforms existing methods under both the standard and generalized FSL settings. |
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown significant potential in code generation, but they also present challenges regarding the protection of Intellectual Property (IP) related to model architectures, weights, and training data. |
| Approach: | They propose a multi-bit watermarking technique that embeds additional information to preserve provenance details, such as the vendor ID of an LLM. |
| Outcome: | The proposed technique preserves provenance details while maintaining syntactical correctness of generated code. |
Efficient Adversarial Training with Robust Early-Bird Tickets (2022.emnlp-main)
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| Challenge: | Existing methods to improve the robustness of pre-trained language models are expensive because of the need to generate adversarial examples via gradient descent. |
| Approach: | They propose an adversarial optimization method that searches for robust tickets with structured sparsity in the early stage and fine-tunes tickets in the remaining time. |
| Outcome: | The proposed method achieves up to 7 13 training speedups while maintaining comparable or even better robustness compared to the most competitive state-of-the-art methods. |
SegTune: Structured and Fine-Grained Control for Song Generation (2026.acl-long)
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Yuejiao Wang, Zihao Ji, Pengfei Cai, Xu Li, Haorui Zheng, Zewen Song, Zhongliang Liu, Chen Zhang, Pengfei Wan
| Challenge: | Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. |
| Approach: | They propose a framework that allows users to specify local musical descriptions aligned to song segments. |
| Outcome: | The proposed framework outperforms baselines in musicality and controllability. |
Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction. |
| Approach: | They propose to use a test suite to evaluate the impact of shortcuts on LLMs' performance. |
| Outcome: | The proposed test suite incorporates six shortcut types, five evaluation metrics, and four prompting strategies. |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |
How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities (2026.acl-long)
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Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng
| Challenge: | Large language models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors pose significant risks. |
| Approach: | They propose a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. |
| Outcome: | The proposed framework offers a principled and interpretable framework for safe and controllable LLM behavior serving as a foundation for future research. |
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)
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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang
| Challenge: | Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning. |
| Approach: | They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. |
| Outcome: | Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks. |
Raw Pointer Rewriting with LLMs for Translating C to Safer Rust (2026.findings-acl)
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| Challenge: | C2Rust is a system programming language that enforces strict memory and type safety guarantees. |
| Approach: | They propose a raw pointer rewriting technique that lifts raw pointers in individual functions to appropriate Rust data structures. |
| Outcome: | The proposed technique eliminates 18.57% of local raw pointers and improves memory safety on 28 real-world C projects. |
Beyond the Surface: A Solution-Aware Retrieval Model for Competition-level Code Generation (2025.findings-emnlp)
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| Challenge: | Existing retrieval models emphasize surface-level semantic similarity, neglecting deeper solution-level logical similarities. |
| Approach: | They propose a solution-aware ranking model empowered by synthetic data for competitive programming tasks. |
| Outcome: | The proposed ranking model outperforms existing retrieval models in precision and recall metrics. |
Dual Cache for Long Document Neural Coreference Resolution (2023.acl-long)
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| Challenge: | Recent studies show the effectiveness of cache-based neural coreference resolution models on long documents. |
| Approach: | They propose a hybrid cache that integrates two eviction policies to capture global and local entities separately and improves F1 score of coreference by 0.7 5.7pt. |
| Outcome: | The proposed model outperforms existing models on four benchmarks while saving up to 83% of inference time. |
FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)
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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen
| Challenge: | Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks. |
| Approach: | They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings. |
| Outcome: | The proposed framework achieves the strongest overall performance across all models. |
Dialogue Meaning Representation for Task-Oriented Dialogue Systems (2022.findings-emnlp)
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| Challenge: | Existing work on dialogue meaning representations is limited in scalability for complex expressions. |
| Approach: | They propose a pliable and easily extendable representation for task-oriented dialogue . they propose an inheritance hierarchy mechanism focusing on domain extensibility . |
| Outcome: | The proposed representation can be easily extended to a task-oriented dialogue dataset. |
Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)
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| Challenge: | Existing methods for multi-role dialogue summarization favor surface-level imitation of references rather than genuine gains in faithfulness or alignment with human preferences. |
| Approach: | They propose a framework that couples explicit cognitive-style reasoning with reward-based optimization for multi-role dialogue summarization. |
| Outcome: | The proposed framework matches strong baselines on ROUGE and BERTScore, while in-depth analysis on SAMSum shows clear gains in factual faithfulness and model-based preference alignment. |
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)
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| Challenge: | In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks. |
| Approach: | They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models. |
| Outcome: | The proposed method analyzes captions generated by five popular VLP models to reveal how well they align with visual words and how well these align with images. |
GRAG: Graph Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and is not suitable for networked documents. |
| Approach: | They propose a novel divide-and-conquer strategy that retrieves optimal subgraph structure in linear time. |
| Outcome: | The proposed approach outperforms current state-of-the-art methods on graph reasoning benchmarks. |
FedCSR: A Federated Framework for Multi-Platform Cross-Domain Sequential Recommendation with Dual Contrastive Learning (2025.coling-main)
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Dongyi Zheng, Hongyu Zhang, Jianyang Zhai, Lin Zhong, Lingzhi Wang, Jiyuan Feng, Xiangke Liao, Yonghong Tian, Nong Xiao, Qing Liao
| Challenge: | Existing federated frameworks for cross-domain sequential recommendation rely on user alignment, which increases communication costs and privacy risks. |
| Approach: | They propose a federated cross-domain sequential recommendation framework that eliminates the need for user alignment between platforms. |
| Outcome: | The proposed framework eliminates the need for user alignment between platforms. |
Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)
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| Challenge: | Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa . |
| Approach: | They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance. |
| Outcome: | The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes. |
CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026.acl-long)
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Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, JingBo Zhu, Wenbo Su, Bo Zheng
| Challenge: | a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity is a major barrier to long-context processing. |
| Approach: | They propose a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity. |
| Outcome: | The proposed architecture can handle arbitrarily long sequences with constant memory usage and linear time complexity. |
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)
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| Challenge: | Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs. |
| Approach: | They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models. |
| Outcome: | The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models. |
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction (2022.findings-emnlp)
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Shirong Ma, Yinghui Li, Rongyi Sun, Qingyu Zhou, Shulin Huang, Ding Zhang, Li Yangning, Ruiyang Liu, Zhongli Li, Yunbo Cao, Haitao Zheng, Ying Shen
| Challenge: | Chinese Grammatical Error Correction (CGEC) is a challenging NLP task and a common application in human daily life. |
| Approach: | They propose a linguistic rules-based approach to construct large-scale CGEC training corpora with automatically generated grammatical errors. |
| Outcome: | The proposed method improves performance of existing CGEC models and the benchmark is excellent resource for further development. |
Boosting Long-Context Information Seeking via Query-Guided Activation Refilling (2025.acl-long)
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| Challenge: | Existing methods for processing long contexts are ineffective due to their inherent context window limitations and the computational burden of extensive key-value activations. |
| Approach: | They propose a method for processing long context information-seeking tasks via query-guided ACtivation REfilling (ACRE) a bi-layer KV Cache is constructed where the layer-1 cache compactly captures global information and the layer-2 cache provides detailed, localized information. |
| Outcome: | The proposed method achieves significant improvements in both performance and efficiency on a variety of long-context information-seeking datasets. |
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition (2025.acl-long)
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Kehua Feng, Keyan Ding, Tan Hongzhi, Kede Ma, Zhihua Wang, Shuangquan Guo, Cheng Yuzhou, Ge Sun, Guozhou Zheng, Qiang Zhang, Huajun Chen
| Challenge: | Existing methods for evaluation of large language models are inefficient and inefficient due to inaccuracy of standard metrics in human perception of text quality and inefficiency in sampling informative test examples. |
| Approach: | They propose a sample-efficient human evaluation method for large language models based on the principle of MAximum Discrepancy (MAD) competition. |
| Outcome: | The proposed method achieves the “golden” ranking of LLMs with a minimum set of input instructions, which in turn reveal their relative strengths and weaknesses. |
Me-Agent: A Personalized Mobile Agent with Two-Level User Habit Learning for Enhanced Interaction (2026.findings-acl)
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Shuoxin Wang, Chang Liu, Gowen Loo, Lifan Zheng, Kaiwen Wei, Huanqian Yan, Xinyi Zeng, Jingyuan Zhang, Yu Tian
| Challenge: | Existing Large Language Model (LLM)-based mobile agents follow explicit user instructions without personalized needs. |
| Approach: | They propose a user preference learning strategy enhanced with a Personal Reward Model to improve personalization performance. |
| Outcome: | The proposed agent achieves state-of-the-art performance while maintaining competitive instruction execution performance. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Self-Supervised Quality Estimation for Machine Translation (2021.emnlp-main)
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Yuanhang Zheng, Zhixing Tan, Meng Zhang, Mieradilijiang Maimaiti, Huanbo Luan, Maosong Sun, Qun Liu, Yang Liu
| Challenge: | Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. |
| Approach: | They propose a self-supervised method to evaluate machine-translated sentences without references by recovering masked target words. |
| Outcome: | The proposed method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains. |
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024.findings-acl)
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Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han
| Challenge: | Existing studies suggest augmenting LLMs with external text corpora to alleviate hallucination problems. |
| Approach: | They propose to augment large language models with text units retrieved from external knowledge corpora to alleviate the issue. |
| Outcome: | The proposed framework outperforms baselines on GRBench with three LLMs and shows that iterative reasoning outperformed the baselines. |
Open Schrödinger’s Closed Box: Identifying Retrieval Augmented Generation in API-Accessible Large Language Model Services (2026.acl-long)
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| Challenge: | Large language models (LLMs) are powerful at question-answering but prone to hallucinations due to limited domain-specific or up-to-date knowledge. |
| Approach: | They propose a framework for IDentifying RAG properties in LLM services that integrates LLMs with retrieval systems and adds an external retriever and knowledge database to mitigate hallucinations. |
| Outcome: | The proposed framework detects RAG-enhanced LLMs with 99.97% accuracy with partial or no optional knowledge and nearly 100% when the LLM and database are known. |
Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval (2025.emnlp-main)
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| Challenge: | a large-scale visionlanguage pre-training framework is limited by the scarcity of large-sized annotated vision-language data . noise-resistant data construction pipeline is needed to filter and caption web-sourced images . noisy text tokens can be a problem for fine-grained representation learning . |
| Approach: | They develop a noise-resistant data construction pipeline that leverages in-context learning capabilities of MLLMs to automatically filter and caption web-sourced images. |
| Outcome: | The proposed framework improves cross-modal alignment by masking noisy textual tokens based on the gradient-attention similarity score. |
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science. |
| Approach: | They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks. |
| Outcome: | The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times. |
IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters (2026.acl-industry)
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Hongwei Zheng, Weiqi Wu, Zhengjia Wang, Guanyu Jiang, Haoming Li, Tianyu Wu, Yongchun Zhu, Jingwu Chen, Feng Zhang
| Challenge: | Existing efforts focus on activation within ongoing dialogues, while overlooking a key real-world bottleneck. |
| Approach: | They propose a conversation starter generation system that generates personalized starters to guide users into conversation without explicit user intent. |
| Outcome: | The proposed system improves user active days by +1.84 and click-through rate by +94.25 and has been deployed in production. |
Improving the Adversarial Robustness of NLP Models by Information Bottleneck (2022.findings-acl)
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| Challenge: | Existing studies have shown that adversarial examples can be directly attributed to the presence of non-robust features. |
| Approach: | They propose to capture task-specific robust features while eliminating non-robust ones . they show that models can achieve significant improvement in robust accuracy . |
| Outcome: | The proposed method outperforms all defense methods on SST-2, AGNEWS and IMDB datasets while achieving no performance drop. |
EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs (2024.lrec-main)
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| Challenge: | Existing frameworks for leveraging background knowledge of narratives are limited. |
| Approach: | They propose a framework to ground free-texts to eventuality-centric KGs for narrative reasoning . their framework is based on a set of probabilistic probabilistic models that are grounded in the real world . |
| Outcome: | The proposed framework outperforms baseline models while providing interpretable evidence. |
Learning to Select In-Context Demonstration Preferred by Large Language Model (2025.findings-acl)
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| Challenge: | In-context learning (ICL) enables large language models to perform tasks with only a few examples as demonstrations. |
| Approach: | They propose a generative preference learning framework that leverages LLM feedback to directly optimize demonstration selection for ICL. |
| Outcome: | Experiments on 19 datasets across 11 task categories show that GenICL achieves superior performance than existing methods in selecting the most effective demonstrations. |
Synergizing Multimodal Temporal Knowledge Graphs and Large Language Models for Social Relation Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have limited capacity to model complex graph-structured relationships. |
| Approach: | They propose a low-coupling method synergizing multimodal temporal Knowledge Graphs and Large Language Models for social relation reasoning. |
| Outcome: | The proposed method exhibits state-of-the-art performance in social relation recognition . it bridges the gap between KGs and LLMs and will be released after acceptance . |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios. |
| Approach: | They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks. |
| Outcome: | The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering. |
Advancing Parameter Efficiency in Fine-tuning via Representation Editing (2024.acl-long)
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Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters. |
| Approach: | They propose a new approach to fine-tuning neural models that scales and biases the representation produced at each layer. |
| Outcome: | The proposed approach reduces the number of trainable parameters by a factor of 25,700 compared to full parameter fine-tuning and by . 32 compared with LoRA. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs? (2025.findings-acl)
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Qingyuan Liang, Zhao Zhang, Zeyu Sun, Zheng Lin, Qi Luo, Xiao Yueyi, Yizhou Chen, Yuqun Zhang, Haotian Zhang, Lu Zhang, Chenbin Chenbin, Yingfei Xiong
| Challenge: | Existing research demonstrates the effectiveness of grammar-based code representations in small-scale models, showing their ability to reduce syntax errors and enhance performance. |
| Approach: | They develop a series of billion-scale grammar-based code representations that incorporate grammar rules into the code generation process. |
| Outcome: | Experiments on HumanEval and MBPP show that grammar-based representations reduce syntax errors and improve performance even in billion-scale models. |
Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)
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| Challenge: | Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways. |
| Approach: | They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods. |
| Outcome: | The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets. |
ACE-M3: Automatic Capability Evaluator for Multimodal Medical Models (2025.coling-main)
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| Challenge: | Existing metrics for multimodal large language models only focus on token overlap and may not align with human judgment. |
| Approach: | They propose an open-source model that assesses the question answering abilities of multimodal large language models. |
| Outcome: | Experiments show that the ACE-M3 model performs better than existing models and is more reliable than existing metrics. |
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)
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Hao Zheng, Xinyan Guan, Hao Kong, Wenkai Zhang, Jia Zheng, Weixiang Zhou, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
| Challenge: | Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence. |
| Approach: | They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides. |
| Outcome: | The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides . |
Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows (2026.findings-acl)
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Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, Yilin Cheng, Mingzhe Lu, Adina Yakefu, Shuxin Zheng
| Challenge: | FinWorkBench evaluates real-world enterprise-grade finance and accounting workflows . a human evaluation of GPT 5.1 Pro passes only 38.4% of workflows, a study finds . |
| Approach: | They propose a workflow construction process that combines LLM-assisted mining and expert annotation to build 172 composite workflows. |
| Outcome: | The proposed process combines expert annotation with LLM-assisted mining of workflows from authentic enterprise environments. |
Evidence Retrieval is almost All You Need for Fact Verification (2024.findings-acl)
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| Challenge: | Existing evidence retrieval methods adopt a trivial retrieval strategy, resulting in task-irrelevant evidence and undesirable performance. |
| Approach: | They propose a framework for evidence retrieval and joint fact verification that integrates two modules. |
| Outcome: | The proposed framework improves evidence retrieval and claims verification on a FEVER dataset. |
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks (2022.coling-1)
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| Challenge: | Neural networks are vulnerable to adversarial examples, i.e., under a black-box scenario. |
| Approach: | They propose a word-level search algorithm that searches for subareas under dynamic search space following the subarea importance. |
| Outcome: | The proposed algorithm can achieve comparable success rates to complex search methods while saving numerous queries and time. |
Multi-modal Contrastive Representation Learning for Entity Alignment (2022.coling-1)
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| Challenge: | Existing studies focus on how to utilize information from different modalities, but it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. |
| Approach: | They propose a Multi-modal Contrastive Learning based Entity Alignment model which learns multiple individual representations from multiple modalities and performs contrastive learning to jointly model inter-modal and inter-modal interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on public datasets under both supervised and unsupervised conditions. |
Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis (2022.emnlp-main)
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| Challenge: | Existing approaches to speaker diarization treat speaker dependency and overlaps as multi-label classification problems. |
| Approach: | They propose to reformulate overlapped speaker diarization task as a single-label prediction problem via power set encoding (PSE) to overcome the disadvantages, they propose a speaker overlap-aware neural diarisation model which incorporates a context-independent scorer and a contextual-dependent score. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on speaker voice activity detection and improves relative diarization error reduction by 6.30%. |
Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)
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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang
| Challenge: | Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions. |
| Approach: | They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency . |
| Outcome: | The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo . |
RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs (2025.emnlp-main)
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| Challenge: | Knowledge graph question answering (KGQA) aims to answer natural language questions using knowledge graphs. |
| Approach: | They propose a framework that retrieves refined reasoning paths and evaluates their sufficiency. |
| Outcome: | The proposed framework outperforms existing baselines while enabling small open-source LLMs to achieve competitive results without fine-tuning LLM. |
Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction (2024.emnlp-industry)
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| Challenge: | Large language models (LLMs) enhanced with retrieval augmentation have shown great performance in many applications, but their computational overhead and additional retrieval step limit their effectiveness in real-time tasks. |
| Approach: | They propose a system that combines a cloud-based LLM with a smaller client-side model through retrieval augmented memory to provide real-time text prediction. |
| Outcome: | The proposed system can generate better responses from the cloud-based model while maintaining low latency. |
PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference (2025.acl-long)
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Jiaming Ji, Donghai Hong, Borong Zhang, Boyuan Chen, Josef Dai, Boren Zheng, Tianyi Alex Qiu, Jiayi Zhou, Kaile Wang, Boxun Li, Sirui Han, Yike Guo, Yaodong Yang
| Challenge: | Using large-scale annotation data, large language models can generate noise, errors and biases, leading to unexpected behaviours. |
| Approach: | They propose a dataset to promote safety alignment in large language models . they separate helpfulness and harmlessness annotations for question-answering pairs . |
| Outcome: | The proposed dataset provides 44.6k prompts and 265k question-answer pairs with safety meta-labels for 19 harm categories and three severity levels, with answers generated by Llama-family models. |
Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)
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| Challenge: | Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents. |
| Approach: | They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface. |
| Outcome: | The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. |
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang
| Challenge: | Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data. |
| Approach: | They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data. |
| Outcome: | The proposed approach preserves key contextual information from the original data while reducing privacy risks. |
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions (2023.tacl-1)
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| Challenge: | Existing studies on the ability of a model to make consistently correct predictions in the presence of perturbations have not been conducted in open-domain question answering (OpenQA). |
| Approach: | They propose a query-side contrastive loss to improve the dense passage retriever (DPR) to improve DPR training. |
| Outcome: | The proposed approach improves the density of the dense passage retriever (DPR) training set without sacrificing accuracy on standard test sets. |
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations (2024.findings-emnlp)
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| Challenge: | Existing research focuses on developing powerful large language models for mathematical reasoning within monolingual languages. |
| Approach: | They propose to use translation to build powerful multilingual math reasoning models . they propose different training strategies to build xMR LLMs that outperform open-source LLM . |
| Outcome: | The proposed model outperforms open-source LLMs and surpasses ChatGPT in few-shot scenarios. |
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have excellent performance in evaluation benchmarks, but struggle in complex reasoning tasks. |
| Approach: | They propose a tool-augmented chain-of-thought reasoning framework for chat-based LLMs . they model chain- of-thoughting reasoning as multi-turn conversations to utilize tools . |
| Outcome: | The proposed framework can outperform state-of-the-art models on complex reasoning tasks. |
Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval (2023.emnlp-main)
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| Challenge: | Inverted file structure is a common technique for accelerating dense retrieval, but its lossy nature degrades it. |
| Approach: | They propose a hybrid index where embedding clusters and salient terms work collaboratively to accelerate dense retrieval. |
| Outcome: | The proposed method achieves lossless retrieval quality with competitive efficiency across index settings. |
CoLAKE: Contextualized Language and Knowledge Embedding (2020.coling-main)
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| Challenge: | Existing models for integrating factual knowledge into pre-trained language models are shallow, static, and separately pre-train entities. |
| Approach: | They propose a method which integrates knowledge contexts from large-scale knowledge bases into a unified data structure. |
| Outcome: | The proposed model outperforms existing models on knowledge-driven tasks and knowledge probing tasks. |
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures. |
| Approach: | They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity. |
| Outcome: | Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost. |
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)
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| Challenge: | Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data. |
| Approach: | They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space . |
| Outcome: | The proposed method can learn weights for words to achieve fine-grained adaptation. |
From Discrimination to Generation: Low-Resource Intent Detection with Language Model Instruction Tuning (2024.findings-acl)
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| Challenge: | Existing studies fine-tune discriminative models on specific defined intent classes, preventing them from being directly adopted to new intent domains. |
| Approach: | They propose to use a pre-trained generative intent model to detect new intents from different domains with no parameter updates. |
| Outcome: | The proposed model outperforms baselines that need further fine-tuning or domain-specific samples. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)
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| Challenge: | Existing attempts to apply large language models to BioEL have revealed difficulties . |
| Approach: | They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities . |
| Outcome: | Extensive experiments show that the framework outperforms existing LLMs. |
PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning (2026.findings-acl)
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Langming Liu, Kangtao Lv, Haibin Chen, Weidong Zhang, Yejing Wang, Shilei Liu, Xin Tong, Yujin Yuan, Yongwei Wang, Wenbo Su, Bo Zheng
| Challenge: | Large language models suffer from factual hallucinations where they generate verifiable falsehoods. |
| Approach: | They propose a framework that integrates reinforcement learning into the pretraining phase to consolidate factual knowledge. |
| Outcome: | The proposed framework significantly alleviates factual hallucinations and outperforms state-of-the-art methods. |
A Dataset and Baselines for Multilingual Reply Suggestion (2021.acl-long)
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| Challenge: | Reply suggestion models help users process emails and chats faster. |
| Approach: | They present a multilingual reply suggestion dataset with ten languages . they build a generation model and a retrieval model as baselines for MRS . |
| Outcome: | The proposed model complements existing benchmarks for cross-lingual generalization . the model has different strengths in the English monolingual setting and requires different strategies to generalize across languages. |
Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)
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| Challenge: | Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation. |
| Approach: | They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline. |
| Outcome: | The proposed framework surpasses open-source RMs by an average of 8.2%. |
Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA (2026.acl-long)
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Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai
| Challenge: | Existing OCR-free approaches to document visual question answering are brittle and passive. |
| Approach: | They propose an OCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation. |
| Outcome: | The proposed framework outperforms open-source and proprietary models in five benchmarks and improves out-of-domain performance by 47.9% over baseline. |
From Logical to Computational Sparsity: Structure-Aware Block-Sparse Attention for Long-Code Completion (2026.acl-long)
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| Challenge: | Existing sparse attention methods for long-context generation pose high latency . general sparsity methods cause excessive accuracy degradation without considering code structure . |
| Approach: | They propose a training-free **S**tructure-**a**ware **b**lock-spa**r**s**e** attention mechanism that bridges the gap between logical and computational sparsity. |
| Outcome: | The proposed method reduces TTFT by 45-55% while maintaining accuracy within 3% of dense attention. |
More than Text: Multi-modal Chinese Word Segmentation (2021.acl-short)
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| Challenge: | Currently, word segmentation is performed in many languages without word delimiters. |
| Approach: | They propose to combine the multi-modality to perform Chinese word segmentation . they propose a time-dependent multi-module interactive model to integrate multi-modality information . |
| Outcome: | The proposed model integrates multi-modal information for word sequence labeling with Chinese language as target . the proposed model performs well on three training sets on Chinese and other languages without word delimiters. |
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation (2023.findings-acl)
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| Challenge: | Crowdsourced dialogue corpora are limited in scale and topic coverage due to the expensive cost of data curation. |
| Approach: | They construct an augmented dataset for the emotional support conversation task using large language models for dialogue augmentation. |
| Outcome: | The proposed approach outperforms baselines of dialogue augmentation and improves the model's generalization ability to open-domain topics. |
Star-Transformer (N19-1)
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| Challenge: | Existing models with fully-connected attention connections are heavy and require large training data. |
| Approach: | They propose a lightweight alternative to the Transformer by sparsifying the fully-connected structure with a star-shaped topology. |
| Outcome: | The proposed model achieves significant performance improvements on 22 datasets on four tasks. |
CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data (2026.acl-long)
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| Challenge: | Existing datasets are small, domain-limited, and lack diversity, constraining LLM progress. |
| Approach: | They propose a knowledge Graph retrieval tool that can translate natural language questions into structured queries. |
| Outcome: | Extensive experiments show that CypherSmith achieves state-of-the-art performance. |
DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect (2023.findings-acl)
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| Challenge: | Existing methods for binaural audio synthesis are limited in phase estimation, which is crucial for spatial hearing. |
| Approach: | They propose a method to explicitly address the Doppler effect of the moving speaker . it calculates the radial relative velocity of the speaker in spherical coordinates . |
| Outcome: | The proposed method improves the representative WarpNet and BinauralGrad backbones in phase error metric and reaches a new state of the art (SOTA) it is compared with the current method which is limited in phase estimation . |
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models (2025.acl-long)
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| Challenge: | Large language models (LLMs) have prioritized expanding the context window from which they can incorporate more information. |
| Approach: | They propose a data augmentation strategy to enable large language models to gain long-context capabilities without the need to modify existing data mixture. |
| Outcome: | The proposed model outperforms existing models on 20 billion tokens and achieves 75% and 84.5% accuracy on RULER at 128K context length. |
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Quality Estimation (QE) is an essential role in applications of Machine Translation (MT). |
| Approach: | They propose to fuse uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. |
| Outcome: | The proposed method achieves state-of-the-art on the datasets of WMT 2020 QE shared task. |
VIB-Probe: Detecting and Mitigating Hallucinations in Vision-Language Models via Variational Information Bottleneck (2026.acl-long)
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| Challenge: | Existing hallucination detection methods rely on external verification tools . however, entanglement of visual-linguistic syntax and noise makes it difficult to detect hallucis . |
| Approach: | They propose a hallucination detection framework that leverages the Variational Information Bottleneck theory to detect hallucinic heads and to infer hallucication mitigation strategies. |
| Outcome: | The proposed framework outperforms baselines in hallucinations and noise detection environments. |
PsyPath: Psychologically-guided Self-Exploration for Personality Detection (2026.findings-acl)
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| Challenge: | Personality detection aims to label traits via identifying linguistic cues from written text. |
| Approach: | They propose a framework that allows large language models to generate and answer psychologically meaningful questions and a hybrid scoring mechanism to evaluate the generated nodes in the reasoning paths. |
| Outcome: | The proposed framework outperforms baselines on two benchmark datasets and significantly improves performance and interpretability in downstream tasks. |
Noise Robust Named Entity Understanding for Voice Assistants (2021.naacl-industry)
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Deepak Muralidharan, Joel Ruben Antony Moniz, Sida Gao, Xiao Yang, Justine Kao, Stephen Pulman, Atish Kothari, Ray Shen, Yinying Pan, Vivek Kaul, Mubarak Seyed Ibrahim, Gang Xiang, Nan Dun, Yidan Zhou, Andy O, Yuan Zhang, Pooja Chitkara, Xuan Wang, Alkesh Patel, Kushal Tayal, Roger Zheng, Peter Grasch, Jason D Williams, Lin Li
| Challenge: | Named Entity Recognition and Entity Linking are challenging for voice assistants . utterances are relatively short, so there is not much context to help disambiguate . |
| Approach: | They propose a Named Entity Understanding system that combines NER and EL in a joint reranking module. |
| Outcome: | The proposed framework improves NER accuracy by up to 3.13% and EL accuracy by 3.6% in F1 score . it also leads to better accuracies in other natural language understanding tasks . |
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)
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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu
| Challenge: | Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering. |
| Approach: | They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow. |
| Outcome: | The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow. |
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models (2025.findings-emnlp)
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Ryan Solgi, Kai Zhen, Rupak Vignesh Swaminathan, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann, Zheng Zhang
| Challenge: | Low-rank tensor compression techniques are used for over-parameterized neural networks, but their applications to compress pre-trained LLMs for downstream tasks remain challenging due to the high-rank nature of pre-training data. |
| Approach: | They propose sparse augmented tensor networks to enhance low-rank tenorized LLMs . they also propose a framework that enables full model compression . |
| Outcome: | The proposed framework improves accuracy and efficiency in tensorized language models. |
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)
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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang
| Challenge: | ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets. |
| Approach: | They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation. |
| Outcome: | The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models (2025.emnlp-main)
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Zhen Zhang, Yifan Yang, Kai Zhen, Nathan Susanj, Athanasios Mouchtaris, Siegfried Kunzmann, Zheng Zhang
| Challenge: | Large language models (LLMs) have demonstrated exceptional capabilities across diverse tasks, but their fine-tuning requires significant memory, posing challenges for resource-constrained environments. |
| Approach: | They propose a ZO-based framework that eliminates the need for backpropagation and provides a memory-efficient alternative to backprograming. |
| Outcome: | The proposed framework surpasses first-order methods in performance and accuracy. |
StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)
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Shihan Dou, Yan Liu, Haoxiang Jia, Enyu Zhou, Limao Xiong, Junjie Shan, Caishuang Huang, Xiao Wang, Xiaoran Fan, Zhiheng Xi, Yuhao Zhou, Tao Ji, Rui Zheng, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective. |
| Approach: | They propose a framework that integrates reinforcement learning and compiler feedback to enhance code generation quality. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches in corresponding benchmarks and integrates reinforcement learning with compiler feedback to improve code generation quality. |
mABC: Multi-Agent Blockchain-inspired Collaboration for Root Cause Analysis in Micro-Services Architecture (2024.findings-emnlp)
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Wei Zhang, Hongcheng Guo, Jian Yang, Zhoujin Tian, Yi Zhang, Yan Chaoran, Zhoujun Li, Tongliang Li, Xu Shi, Liangfan Zheng, Bo Zhang
| Challenge: | Root cause analysis (RCA) in Micro-services architectures with escalating complexity is challenging due to fault propagation and circular dependencies among nodes. |
| Approach: | They propose a framework where multiple agents follow Agent Workflow and collaborate in blockchain-inspired voting to ensure the reliability of root cause analysis. |
| Outcome: | The proposed framework reduces the number of steps and standardizes task processing through Agent Workflow. |
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (2022.coling-1)
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| Challenge: | Existing parameter-efficient methods focus on reducing trainable parameters but neglect the inference speed, which limits the ability to deploy PLMs. |
| Approach: | They propose to use a hypernetwork-assisted inter-layer connector to enhance inference efficiency by tuning parameters inside a linear connector between two Transformer layers. |
| Outcome: | The proposed model reduces model parameters to 11.75% while preserving performance degradation to less than 5%. |
Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering (2022.findings-emnlp)
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| Challenge: | Existing methods for Few-Shot Text Classification are limited by their global knowledge-shared mechanisms. |
| Approach: | They propose a self-supervised hierarchical task clustering method to address task heterogeneity . they use prior knowledge from historical tasks to leverage prior knowledge . |
| Outcome: | The proposed method can learn a classifier efficiently with few examples . it disentangles the underlying relations between tasks to improve interpretability . |
Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have advanced mathematical reasoning, but they still struggle with out-of-distribution (OOD) issues. |
| Approach: | They propose a framework to evaluate the logical validity of reasoning steps . they retrieves semantically similar questions and steps for PRM as a warmup . |
| Outcome: | The proposed framework outperforms baseline models on multiple real-world datasets. |
RealBehavior: A Framework for Faithfully Characterizing Foundation Models’ Human-like Behavior Mechanisms (2023.findings-emnlp)
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Enyu Zhou, Rui Zheng, Zhiheng Xi, Songyang Gao, Xiaoran Fan, Zichu Fei, Jingting Ye, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing studies on human-like behaviors in foundation models do not verify their faithfulness . a simple application of psychological tools cannot faithfully characterize all human-type behaviors . |
| Approach: | They propose a framework to characterize humanoid behaviors in foundation models . they argue that a simple application of psychological tools cannot faithfully characterize all human-like behaviors . |
| Outcome: | The proposed framework assesses the faithfulness of results based on reproducibility, internal consistency, and generalizability. |
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
| Approach: | They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints. |
| Outcome: | The proposed framework outperforms existing rule-based frameworks on three DocRE datasets. |
Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)
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| Challenge: | Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data. |
| Approach: | They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding. |
| Outcome: | The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art. |
Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics (2024.lrec-main)
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| Challenge: | Recent models for visual question localized-answering (VQLA) lack the ability to relate these answers to their localization at an instance level. |
| Approach: | They propose a model which introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets on surgical scenes and surgical instruments. |
WRP: Weight Recover Prune for Structured Sparsity (2024.acl-long)
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| Challenge: | Existing pruning and quantization algorithms could compress LLMs to 4 bits without retraining. |
| Approach: | They propose a method to prune models using a minimal set of critical weights . they compare the method to a 2:4 structured sparsity method . |
| Outcome: | The proposed method outperforms other methods on LLAMA2 and OPT models while maintaining the efficiency of the compression. |
Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing (2026.findings-acl)
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Zhenghai Chen, Senbin Xu, Jiaxi Tan, Xinhua Wu, Yan Zhang, Xiawu Zheng, Shengchuan Zhang, Ke Li, Sicheng Zhao, Liujuan Cao, Rongrong Ji
| Challenge: | Existing knowledge editing methods rely on strict orthogonal projection to preserve previously edited knowledge, but this constraint limits gradient expressiveness, resulting in degradation of model generalization and overall performance as the number of edits increases. |
| Approach: | They propose a method that leverages Singular Value Decomposition to identify critical gradient subspaces and introduces a dual mechanism comprising "accumulated importance" and "projection importance" |
| Outcome: | Extensive experiments on five mainstream LLMs show that the proposed method achieves an average comprehensive performance improvement of 10.36% and effectively maintains the model’s general capabilities on downstream tasks. |
Clear Up Confusion: Iterative Differential Generation for Fine-grained Intent Detection with Contrastive Feedback (2025.coling-main)
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| Challenge: | Recent studies on fine-grained intent detection have focused on collecting large-scale and high-quality samples via crowdsourcing resulting in data scarcity. |
| Approach: | They propose an iterative differential generation framework with contrastive feedback to generate high-quality pseudo samples and accurately capture the crucial nuances in target class distribution. |
| Outcome: | The proposed framework generates high-quality pseudo samples and captures crucial nuances in target class distribution. |
A Comprehensive Survey of Process Reward Models: Data Generation, Model Construction, and Usage (2026.acl-long)
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Congmin Zheng, Jiachen Zhu, Zhuoying Ou, Yuxiang Chen, Kangning Zhang, Rong Shan, Zeyu Zheng, Mengyue Yang, Jianghao Lin, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have advanced reasoning ability, yet conventional alignment remains dominated by outcome reward models that judge only final answers. |
| Approach: | They summarize applications across math, code, text, multimodal reasoning, robotics, and agents . goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment. |
| Outcome: | The proposed model enables finer credit assignment, richer diagnostics, and improved robustness. |
LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization (2025.emnlp-main)
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Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, Junbo Zhao
| Challenge: | Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL). |
| Approach: | They propose a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. |
| Outcome: | The proposed framework can boost LLMs’ reasoning ability by integrating external knowledge sources through retrieval-augmented generation (RAG) The proposed model can mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. |
ECON: On the Detection and Resolution of Evidence Conflicts (2024.emnlp-main)
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Cheng Jiayang, Chunkit Chan, Qianqian Zhuang, Lin Qiu, Tianhang Zhang, Tengxiao Liu, Yangqiu Song, Yue Zhang, Pengfei Liu, Zheng Zhang
| Challenge: | Recent studies have shown that AI generated content is more likely to dominate search results, making it difficult to detect when compared to human-produced content. |
| Approach: | They propose a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. |
| Outcome: | The proposed method enables the detection of conflicting information in real-world scenarios and shows that weaker models struggle with similar answer conflicts while stronger models show robust performance. |
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing. |
| Approach: | They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances. |
| Outcome: | Empirical results show that the proposed model improves against baselines and can be scaled to a large extent. |
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)
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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng
| Challenge: | ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets. |
| Approach: | They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models. |
| Outcome: | The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies. |
LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles (2024.lrec-main)
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| Challenge: | Existing evaluation benchmarks, such as MMLU, C-Eval, and GSM8K, evaluate models by posing a variety of problems, including problems about mathematics, science, law, and general knowledge. |
| Approach: | They propose a benchmark which assesses the model’s lateral thinking within an interactive framework. |
| Outcome: | The proposed evaluation benchmark assesses the model’s lateral thinking within an interactive framework. |
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings (2022.acl-long)
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| Challenge: | Contextualized embeddings are expensive and resource-demanding, hence environmentally unfriendly. |
| Approach: | They propose a method to convert contextualized embeddings from pre-trained models into static embeddables using synonym knowledge and weighted vector distribution. |
| Outcome: | The proposed method outperforms baseline embeddings by a large margin through extrinsic and intrinsic tasks. |
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts (2023.emnlp-main)
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| Challenge: | Existing methods for large language models (LLMs) have been used to prompt different reasoning thoughts, such as Chain of Thought and Program of Though. |
| Approach: | They propose a framework that prompts large language models with diverse reasoning thoughts by iterating between different prompting methods. |
| Outcome: | The proposed framework is able to generate multiple reasoning thoughts in 10 popular math reasoning datasets and is orthogonal to recent work that makes improvements on single reasoning methods and can generalise to logical reasoning domain. |
MADAWSD: Multi-Agent Debate Framework for Adversarial Word Sense Disambiguation (2025.emnlp-main)
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Kaiyuan Zhang, Qian Liu, Luyang Zhang, Chaoqun Zheng, Shuaimin Li, Bing Xu, Muyun Yang, Xinxiao Qiao, Wenpeng Lu
| Challenge: | Word sense disambiguation (WSD) is a fundamental yet challenging task in natural language processing. |
| Approach: | a novel multi-agent Debate framework for adversarial word Sense disambiguation is proposed . the framework simulates a real-world debate environment where multiple agents engage in discussions about ambiguous words in the context of adversarials. |
| Outcome: | The proposed framework integrates with existing LLMs and improves models in Chinese language . it shows that it can be used to improve models in the Chinese language and improve performance . |
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design (2026.findings-acl)
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Yutang Ge, Guojiang Zhao, Sihang Li, Zheng Cheng, Zifeng Zhao, Hanchen Xia, Guolin Ke, Linfeng Zhang, Zhifeng Gao, Yu Guang Wang
| Challenge: | Recent deep generative models have already shown encouraging * Equal contribution. |
| Approach: | They propose to use generic instruction-tuned LLMs as direct text-to-sequence generators to achieve this goal. |
| Outcome: | Recent studies show that reflection improves sequence quality and alignment while maintaining competitive foldability. |
Multi-Document Event Extraction Using Large and Small Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
| Outcome: | The proposed framework outperforms existing methods and provides new insights into collaborative reasoning to tackle the complexities of multi-document event extraction. |
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods for parameter-efficient fine-tuning are limited by the growing number of trainable parameters with the rapid deployment of Large Language Models (LLMs). |
| Approach: | They propose a parameter-efficient framework that reduces trainable parameters through tensor-train decomposition. |
| Outcome: | The proposed methods achieve comparable or better performance than most widely used methods with up to 100 fewer parameters on the LLaMA-2-7B models. |
Look and Think: Efficient Multimodal Reasoning via Modality-Decoupled Compression (2026.findings-acl)
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| Challenge: | Multimodal large language models have strong performance on visual question answering benchmarks . however, their inference efficiency is severely constrained by the rapidly growing context . |
| Approach: | They propose a modality-decoupled compression method that enables efficient multimodal inference . they propose to evict visual tokens whenever visual grounding is unnecessary . |
| Outcome: | The proposed method reduces the average context length by up to 57% while maintaining comparable performance to the standard MLLM baseline. |
Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)
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| Challenge: | Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. |
| Approach: | They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios. |
| Outcome: | The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities. |
PerSphere: A Comprehensive Framework for Multi-Faceted Perspective Retrieval and Summarization (2025.acl-long)
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| Challenge: | Experimental results show that the main challenge lies in long context and perspective extraction. |
| Approach: | They propose a benchmark to facilitate multi-faceted perspective retrieval and summarization . they propose measurable metrics to evaluate the comprehensiveness of the retrieval pipeline . |
| Outcome: | The proposed system breaks free from information silos by combining two opposing claims . it can be used to extract multiple perspectives and improve performance on the platform . |
Watermarking PLMs on Classification Tasks by Combining Contrastive Learning with Weight Perturbation (2023.findings-emnlp)
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Chenxi Gu, Xiaoqing Zheng, Jianhan Xu, Muling Wu, Cenyuan Zhang, Chengsong Huang, Hua Cai, Xuanjing Huang
| Challenge: | Large pre-trained language models (PLMs) are highly valuable intellectual property due to their expensive training costs. |
| Approach: | They propose to embed backdoors that can be triggered by specific inputs into models by model watermarking. |
| Outcome: | The proposed method can be used to protect the intellectual property of large pre-trained language models without knowledge about downstream tasks. |
VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models (2024.emnlp-main)
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| Challenge: | Existing metrics, such as CLIP, measure the semantic alignment between single prompts and their corresponding images, but they fail to evaluate a model’s generalizability across a broad spectrum of textual inputs. |
| Approach: | They propose a metric that leverages the power of Large Language Models to sample from the visual text domain and assess its generalizability. |
| Outcome: | The proposed metric evaluates the generalizability of T2I models and provides valuable insights during the finetuning process. |
Mitigating Hallucinations in VLMs: Enhancing Visual Attention via Head-Wise Perturbation (2026.findings-acl)
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| Challenge: | Vision–Language Models (VLMs) have demonstrated strong capabilities in tasks that require joint understanding of text and images. |
| Approach: | They propose a strategy that incorporates head-wise attention perturbation via continuous multiplicative noise coupled with a visual-guided loss focused on vision-sensitive text tokens to promote a more balanced attention distribution. |
| Outcome: | The proposed approach outperforms baseline models on three benchmarks and consistently outperformed the baseline model. |
BeamLoRA: Beam-Constraint Low-Rank Adaptation (2025.acl-long)
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Naibin Gu, Zhenyu Zhang, Xiyu Liu, Peng Fu, Zheng Lin, Shuohuan Wang, Yu Sun, Hua Wu, Weiping Wang, Haifeng Wang
| Challenge: | Low-Rank Adaptation (LoRA) is one of the most efficient parameter-efficient fine-tuning methods. |
| Approach: | They propose to conceptualize each LoRA module as a beam where each rank corresponds to a potential sub-solution. |
| Outcome: | The proposed method improves performance on three base models and 12 datasets. |
Modality-Aware Integration with Large Language Models for Knowledge-Based Visual Question Answering (2024.acl-long)
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| Challenge: | Existing methods to integrate multimodal knowledge in a modality-agnostic manner can be sub-optimal. |
| Approach: | They propose a modality-aware integration with large language models (LLMs) that leverages multimodal knowledge for both image understanding and knowledge reasoning. |
| Outcome: | The proposed model is able to bridge a tight inter-modal exchange while preserving insightful intra-modal learning. |
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)
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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen
| Challenge: | Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data. |
| Approach: | They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments. |
| Outcome: | The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments. |
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)
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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu
| Challenge: | Recent VSE models combine simple pooling methods with hard triplet loss to improve performance. |
| Approach: | They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods. |
| Outcome: | The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
| Challenge: | Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns. |
| Approach: | They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users. |
| Outcome: | The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks. |
How Do In-Context Examples Affect Compositional Generalization? (2023.acl-long)
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| Challenge: | In-context learning paradigms that focus on large corpus are limiting compositional generalization performance. |
| Approach: | They propose a test suite to investigate in-context compositional generalization . they propose to use examples that are structurally similar to the test case . |
| Outcome: | The proposed test suite investigates in-context compositional generalization performance . it finds that the performance can be affected by the selection of in-const examples . |
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)
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| Challenge: | Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs . |
| Approach: | They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique. |
| Outcome: | The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias. |
GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents (2026.acl-long)
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Yunzhe Wang, Runhui Xu, Kexin Zheng, Tianyi Zhang, Jayavibhav Niranjan Kogundi, Soham Hans, Volkan Ustun
| Challenge: | Multimodal Large Language Models are emerging as a backbone for autonomous agents in 3D environments. |
| Approach: | They propose a framework for evaluating agentic-centric perception and reasoning through video understanding. |
| Outcome: | The proposed framework evaluates agentic-centric perception and reasoning through video understanding. |
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models (2025.emnlp-demos)
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Ziwen Xu, Shuxun Wang, Kewei Xu, Haoming Xu, Mengru Wang, Xinle Deng, Yunzhi Yao, Guozhou Zheng, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) have demonstrated extraordinary capabilities, however, they may still generate unreliable or unsafe outputs. |
| Approach: | They propose a framework that allows plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. |
| Outcome: | The framework is designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. |
M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)
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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng
| Challenge: | Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities . |
| Approach: | They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models. |
| Outcome: | The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree. |
Decompose, Prioritize, and Eliminate: Dynamically Integrating Diverse Representations for Multimodal Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing research on multi-modal Named Entity Recognition (MNER) does not integrate all multi-modal representations to provide rich contextual information to improve NER. |
| Approach: | They propose an iterative reasoning framework that integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate" . they propose to use hierarchically connected fusion layers to prioritize transitions from "easy-to-hard" and "coarse-to fine" |
| Outcome: | The proposed framework integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate". |
Beyond Surface Features: Advancing Medical Vision-Language Alignment via Dynamic Evidence-Guided Preference Optimization (2026.acl-long)
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Zixuan Huang, Zhihong Zhu, Xiaolong Liu, Yanchao Hao, Manman Zhang, Zheng Wei, Bowen Xing, Xian Wu, Ye Li, Fen Miao, Yefeng Zheng
| Challenge: | Existing preference-based methods for medical large vision-Language Models face limitations in medical settings . existing methods are limited by overfitting to superficial cues and pseudo convergence of the preference signal. |
| Approach: | They propose a framework that enables evidence-aware and adaptive preference learning for Med-LVLMs. |
| Outcome: | The proposed framework improves evidence-aware and adaptive preference learning for Med-LVLMs. |
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)
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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
| Challenge: | a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages. |
| Approach: | They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models. |
| Outcome: | The proposed benchmarks show that the current models perform worse than the human ceiling. |
Seeing More, Saying More: Lightweight Language Experts are Dynamic Video Token Compressors (2025.emnlp-main)
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| Challenge: | Existing methods for converting visual tokens into tokens are limited by their high volume . et al., 2023; Zheng e.t., 2023): a revolution in video understanding. |
| Approach: | They propose a language-aware dynamic token compression system that converts video clips into soft caption tokens as visual representations. |
| Outcome: | The proposed method reduces FLOPs by 49% while maintaining competitive performance. |
Non-Autoregressive Chinese ASR Error Correction with Phonological Training (2022.naacl-main)
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| Challenge: | Existing methods to correct ASR errors focus on fixed-length corrections, but rarely consider variable-length ones. |
| Approach: | They propose a non-autoregressive method to correct Chinese ASR errors . they use phonological tokens to extend the source sentence for variable-length correction . |
| Outcome: | The proposed method improves word error rate and speeds up inference by 6.2 times compared with the autoregressive model. |
Beyond the Answer: Advancing Multi-Hop QA with Fine-Grained Graph Reasoning and Evaluation (2025.acl-long)
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| Challenge: | Existing evaluations of multi-hop question answering systems focus on comparing final answers of reasoning method and given ground-truths. |
| Approach: | They propose a "Planner-Executor-Reasoner" architecture that evaluates reasoning . they propose PER-DP and PER QA architectures that provide ground-truths . |
| Outcome: | The proposed model improves the performance of multi-hop question answering systems. |
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)
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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations. |
| Approach: | They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference. |
| Outcome: | The proposed method significantly mitigates object hallucinations across various model architectures. |
GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
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| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)
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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li
| Challenge: | Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training. |
| Approach: | They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks. |
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples (2022.findings-acl)
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| Challenge: | Existing defense methods improve the adversarial robustness by making models adapt to training set augmented with some adversarials. |
| Approach: | They propose to introduce a reweighting mechanism to calibrate the training distribution to obtain robust models. |
| Outcome: | The proposed method minimizes the loss of validation set mixed with clean examples and adversarial ones in an online learning manner. |
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)
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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang
| Challenge: | Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored. |
| Approach: | They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes. |
| Outcome: | The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks. |
Biomedical Entity Linking as Multiple Choice Question Answering (2024.lrec-main)
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| Challenge: | Existing methods for biomedical entity linking are discriminative and disambiguative . Existing models for bioMEDical entity linking use a BERT-based encoder to encode mentions and entities into the same embedding space and dissociate mentions by nearest neighbors. |
| Approach: | They propose a model that treats biomedical entity linking as Multiple Choice Question Answering. |
| Outcome: | The proposed model outperforms state-of-the-art models on several datasets. |
Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization (2023.findings-acl)
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| Challenge: | Current speaker diarization systems consider only acoustic information, resulting in performance degradation when encountering adverse acustic environment. |
| Approach: | They propose methods to extract speaker-related information from conversational semantics in multi-party meetings. |
| Outcome: | The proposed method improves on AISHELL-4 and AliMeeting datasets on speakers diarization and speaker-turn detection. |
Value of Information: A Framework for Human–Agent Communication (2026.acl-long)
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| Challenge: | Existing approaches to large language model (LLM) agents fail to account for stakes of different decisions. |
| Approach: | They propose a framework that balances task risk, query ambiguity, user effort . they use a value-of-information framework to dynamically weigh the expected utility gain . |
| Outcome: | The proposed model matches or exceeds the best manually-tuned baselines in four domains . it explicitly balances task risk, query ambiguity, and user effort . |
UNIVID: Unified Vision-Language Model for Video Moderation (2026.acl-industry)
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Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang, Kenan Xiao
| Challenge: | Existing video moderation systems rely on fragmented black-box classification models that are difficult to maintain and lack transparency. |
| Approach: | They propose a Unified Vision-Language model for Video Moderation that generates policy-aware captions that serve as an interpretable intermediate representation. |
| Outcome: | The proposed model reduces violation leakage and overkill rate by 42.7% while reducing maintenance costs. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |
Causally Modeling the Linguistic and Social Factors that Predict Email Response (2025.naacl-long)
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Yinuo Xu, Hong Chen, Sushrita Rakshit, Aparna Ananthasubramaniam, Omkar Yadav, Mingqian Zheng, Michael Jiang, Lechen Zhang, Bowen Yi, Kenan Alkiek, Abraham Israeli, Bangzhao Shu, Hua Shen, Jiaxin Pei, Haotian Zhang, Miriam Schirmer, David Jurgens
| Challenge: | a key intent behind many emails is to get a reply from the recipient. |
| Approach: | They propose to model the intents, expectations, and responsiveness in email exchanges by using a dataset containing 1800 emails annotated with nuanced types of intents and expectations. |
| Outcome: | The proposed model is based on 1800 emails annotated with nuanced types of intents and expectations . it shows that social status, argumentation, and strength of social connection influence email response rates . |
NavA3: Understanding Any Instruction, Navigating Anywhere, Finding Anything (2026.acl-long)
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Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang, Haoxiang Fu, Xinyu Zheng, Pengwei Wang, Zhongyuan Wang, Wenbo Ding, Shanghang Zhang
| Challenge: | Existing embodied navigation methods struggle with such tasks due to their limitations in comprehending high-level human instructions and localizing objects with an open vocabulary. |
| Approach: | They propose a hierarchical framework for long-horizon navigation that integrates human instructions with 3D scene views. |
| Outcome: | The proposed model achieves SOTA results and can complete long-horizon navigation tasks across different robot embodiments in real-world environments. |
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation (2025.emnlp-main)
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Ziyue Liu, Ruijie Zhang, Zhengyang Wang, Mingsong Yan, Zi Yang, Paul D. Hovland, Bogdan Nicolae, Franck Cappello, Sui Tang, Zheng Zhang
| Challenge: | Large foundation models have become huge, but they consume computational resources in pretraining. |
| Approach: | They propose to replace full-size layers with compute-efficient auto-encoders that enforce low-rank activations throughout training. |
| Outcome: | The proposed method reduces the computing cost by 2pmbtimes and improves training throughput by 1.86pmtime. |
LIME: Less Is More for MLLM Evaluation (2025.findings-acl)
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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang
| Challenge: | Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs. |
| Approach: | They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding. |
| Outcome: | The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities. |
Investigating Human and LLMs’ Decisions in Unverifiable Environments: A Case Study with GitHub Activity Overview (2026.findings-acl)
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| Challenge: | examining the behaviors of Large Language Models as artificial social actors is underexplored, especially in unverifiable scenarios where conventional benchmarking has little to help improve their abilities. |
| Approach: | They propose a method to collect, compare, and reason about human and LLMs' decisions in an unverifiable scenario and use it to examine their behaviors. |
| Outcome: | The proposed method compared human and LLM decisions in an unverifiable scenario on GitHub and found that proprietary LLMs behave more like humans than open-source LLM systems. |
A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)
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| Challenge: | Existing single-cell foundation language models are based on pre-trained and large language models. |
| Approach: | They review the development of single-cell foundation language models . they discuss data tokenization strategies and pre-training paradigms . |
| Outcome: | The proposed models have shown remarkable performance in a variety of single-cell data analysis tasks. |
Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations (2023.emnlp-main)
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| Challenge: | Existing approaches to generate SQL from natural language are still making many mistakes . a new interaction mechanism allows users to edit a step-by-step explanation of a query to fix errors. |
| Approach: | They propose a mechanism that allows users to edit a step-by-step explanation of a query to fix errors. |
| Outcome: | The proposed approach can achieve better performance than multiple SOTA approaches on multiple datasets and 24 participants. |
ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs (2026.acl-long)
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Hongxin Ding, Baixiang Huang, Yue Fang, Weibin Liao, Xinke Jiang, Jinyang Zhang, Yinghao Zhu, Zheng Li, Liantao Ma, Junfeng Zhao, Yasha Wang
| Challenge: | Existing medical Large Language Models (LLMs) follow a reactive paradigm, risking diagnostic errors by answering before seeking sufficient details. |
| Approach: | They propose a reinforcement learning framework that transitions LLMs toward a proactive paradigm, enabling them to ask clinically valuable questions before decision-making. |
| Outcome: | Experiments on partial-information medical benchmarks show that ProMed outperforms state-of-the-art methods by 6.29% on average and delivers a 54.45% gain over the reactive paradigm. |
Verifiable Format Control for Large Language Model Generations (2025.findings-naacl)
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| Challenge: | Existing methods focus on benchmarking general instruction following while overlooking how to improve specific format following ability for small LLMs. |
| Approach: | They propose to synthesize massive datasets to improve LLMs' format following abilities by using a verifiable format following feature. |
| Outcome: | The proposed method improves the format following ability of small LLMs with about 7B parameters. |
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model (2024.emnlp-main)
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| Challenge: | Existing large language models (LLMs) use large amounts of public data and massive parameters, but private data is often stored in isolated data silos. |
| Approach: | They propose a Federated Learning framework for large language models which offloads most training parameters to the server while training embedding and output layers locally. |
| Outcome: | The proposed framework achieves comparable metrics to centralized chatGLM model on NLU and generation tasks. |
Any Information Is Just Worth One Single Screenshot: Unifying Search With Visualized Information Retrieval (2025.acl-long)
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| Challenge: | Existing multimodal retrieval models are lacking in visual representations of multimodal data. |
| Approach: | They propose a visualized information retrieval paradigm where multimodal information is represented by a unified visual format called Screenshots for various retrieval applications. |
| Outcome: | The proposed model is based on a large dataset of screenshots from diverse sources . it is compared with existing models and lays a solid foundation for the new model . |
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning (2023.findings-acl)
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| Challenge: | Current language models have shown impressive capability of generating fluent and grammatical text, but they often produce behaviors misaligned with human expectations. |
| Approach: | They propose a new language model called Leo for controllable text generation which employs a contrastive loss on sequence likelihood which fundamentally decreases the generation probability of negative samples. |
| Outcome: | The proposed model outperforms baselines on language detoxification, sentiment steering, and repetition reduction tasks. |
A Unified Dialogue User Simulator for Few-shot Data Augmentation (2022.findings-emnlp)
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| Challenge: | Existing methods to augment large-scale task-oriented dialogues rely on annotated data. |
| Approach: | They propose to build a unified dialogue user simulation model by pre-training on publicly available datasets. |
| Outcome: | The proposed model can be tuned on a target domain with few-shot data. |
Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning (2025.emnlp-main)
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Mingyuan Wu, Jize Jiang, Haozhen Zheng, Meitang Li, Zhaoheng Li, Beitong Tian, Bo Chen, Yongjoo Park, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt
| Challenge: | Recent Vision Language Models (VLMs) have shown tremendous promise in a wide range of realworld applications, but their size has made at-scale deployment and operation challenging due to high consumption of cloud computing resource, high latency, and expensive API calls. |
| Approach: | They propose a master–apprentice framework for collaborative inference between large and small vision language models. |
| Outcome: | The proposed framework improves reasoning performance on widely-recognized and challenging general reasoning benchmarks and specifically boosts reasoning of apprentice VLMs by 36.6%. |
Improving Adversarial Text Generation by Modeling the Distant Future (2020.acl-main)
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Ruiyi Zhang, Changyou Chen, Zhe Gan, Wenlin Wang, Dinghan Shen, Guoyin Wang, Zheng Wen, Lawrence Carin
| Challenge: | Recent work has shown excellent performance on text generation tasks by combining reinforcement learning (RL) and generative models. |
| Approach: | They propose a model-based imitation-learning approach to improve text generation performance by focusing on a long horizon. |
| Outcome: | The proposed model improves on a number of text-generation tasks and provides intermediate rewards for generator optimization. |
ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)
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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao
| Challenge: | ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
| Approach: | They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments. |
| Outcome: | The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Existing RAG systems require uploading local documents to the cloud, resulting in inference latency and poor generalization on out-of-distribution (OOD) inputs. |
| Approach: | They propose a generalizable knowledge-distilled parametric RAG model aligned with standard RAG in document structure and parameter activation. |
| Outcome: | The proposed model outperforms baselines in accuracy and generalizes well on out-of-distribution (OOD) data. |
The Stackelberg Speaker: Optimizing Persuasive Communication in Social Deduction Games (2026.acl-long)
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| Challenge: | Existing approaches focus on information processing and strategy selection, overlooking the significance of persuasive communication in social deduction games. |
| Approach: | They propose a reinforcement learning framework that trains agents to optimize influential utterances for persuasive impact by formalizing turn-based dialogue as a Stackelberg competition . |
| Outcome: | The proposed framework outperforms baselines across four social deduction benchmarks and shows that it is effective in persuasive communication. |
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words (2022.coling-1)
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Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu
| Challenge: | Pre-trained models perform poorly with limited data and rare biomedical words. |
| Approach: | They propose to use prompt to fine-tune pre-trained models for biomedical domain tuning with a simple approach. |
| Outcome: | The proposed method achieves up to 6% improvement in biomedical natural language inference task without any extra parameters or training steps using few-shot vanilla prompt settings. |
Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training (2024.lrec-main)
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| Challenge: | Graph neural networks excel at anomaly detection, but exhibit vulnerability to attacks . novel mechanism for graph adversarial training designed to bolster anomaly detectors . |
| Approach: | They propose a mechanism for graph adversarial training to bolster anomaly detection systems against potential poisoning attacks. |
| Outcome: | The proposed method bolsters GNN-based anomaly detection systems against poisoning attacks. |
Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)
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| Challenge: | Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios. |
| Approach: | They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity. |
| Outcome: | The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods. |
The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)
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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi
| Challenge: | Recent studies show that malicious prompt instructions could solicit objectionable content from LLMs. |
| Approach: | They compare how state-of-the-art LLMs respond to malicious prompts in different languages . they find that LLM's generate unsafe responses more often when a prompt is written in a lower-resource language . |
| Outcome: | The proposed model can generate unsafe responses more often when a malicious prompt is written in a lower-resource language, and less irrelevant responses when written in lower-source languages. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces (2024.lrec-main)
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| Challenge: | Existing approaches to offline reinforcement learning (RL) focus on learning value functions or policy gradients, but they view it as a sequence modeling task. |
| Approach: | They propose a method that integrates multimodal and pre-trained language models to transform offline reinforcement learning into a supervised learning task by integrating state information derived from images and action-related data obtained from text. |
| Outcome: | The proposed approach outperforms baselines on Atari and OpenAI Gym environments while promoting long-term strategic thinking. |
Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning? (2025.acl-long)
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Yancheng He, Shilong Li, Jiaheng Liu, Weixun Wang, Xingyuan Bu, Ge Zhang, Z.y. Peng, Zhaoxiang Zhang, Zhicheng Zheng, Wenbo Su, Bo Zheng
| Challenge: | Recent advances in o1-like models have generated long Chain-of-Thought reasoning steps to improve the reasoning abilities of existing Large Language Models (LLMs). |
| Approach: | They propose a DeltaBench to analyze the quality and effectiveness of o1-like models and measure their ability to detect errors in long COT reasoning. |
| Outcome: | The proposed model can detect errors in long COT reasoning. |
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages (2020.emnlp-main)
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| Challenge: | Existing mPLM-based methods focus on designing costly model pre-training while ignoring equally crucial downstream adaptation. |
| Approach: | They propose a meta graph learning method that extracts meta-knowledge from historical CLT experiences to learn to cross-lingual transfer. |
| Outcome: | The proposed method can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks) it can also capture intrinsic language relationships to explicitly guide cross-linguistic transfer. |
LOKA: Conflict-Aware LLM Knowledge Update with Adaptive Knowledge Memory (2026.acl-long)
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| Challenge: | Existing approaches that tackle unlearning and learning separately encounter *task conflicts* and *knowledge management issues* when applied to comprehensive knowledge updates. |
| Approach: | They propose a conflict-aware framework for Large language mOdel Knowledge updAtes that integrates updated knowledge across multiple memory units during training and integrates it with original LLM. |
| Outcome: | The proposed framework is based on theoretical analysis and empirical evidence and validates the proposed framework with empirical and theoretical evidence. |
Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios (2026.findings-acl)
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Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang, Zirui Zheng, Takashi Isobe, Dezhuang Li, Huchuan Lu, You He, Xu Jia
| Challenge: | Existing benchmarks focus on evaluating MLLMs’ pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning. |
| Approach: | They propose a benchmark designed for visual clue-driven reasoning in daily scenarios that combines rigorous grounding in authentic daily activities and challenging query design that necessitates more than surface-level perception. |
| Outcome: | The proposed benchmark identifies visual clues and their ability to provide robust reasoning in daily scenarios. |
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2024.findings-acl)
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| Challenge: | OpenCodeInterpreter-33B provides a high level of performance for code generation, executing, and iterative refinement. |
| Approach: | They propose a family of open-source code systems for generating, executing, and iteratively refining code. |
| Outcome: | The OpenCodeInterpreter-33B performs well on humanEval, MBPP, and EvalPlus benchmarks. |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)
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Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing federated learning frameworks require substantial data and computational resources to develop large language models. |
| Approach: | They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs. |
| Outcome: | The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. |
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods treat three modal features equally, without distinguishing the importance of different modalities. Existing models split the video into frames, leading to missing the global acoustic information. |
| Approach: | They propose a global Acoustic feature enhanced Modal-Order-Aware network to address these problems. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public datasets. |
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling (2024.lrec-main)
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| Challenge: | Chain of thought (CoT) is used for complex reasoning problems, but hallucinations are a problem in multimodal CoT. |
| Approach: | They propose a method to generate soft negative samples with different semantics to mitigate hallucinations in multimodal CoT. |
| Outcome: | The proposed method mitigates hallucinations in multimodal CoT by using soft negative sampling. |
Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness (2021.emnlp-main)
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| Challenge: | a recent study shows that inappropriate language can cause models to output profanity . authors propose a training framework to prevent such outputs from hurting the usability of models . |
| Approach: | proposed training framework eliminates the causes that trigger the generation of profanity . authors propose a framework that leverages a short list of profans to prevent this . |
| Outcome: | a proposed training framework can prevent models from generating profanity . the proposed framework leverages a short list of profanities examples . |
Example Quality Matters: Multi-Aspects Example Augmentation for Private Library Programming (2026.acl-long)
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Yuhao Li, Haifeng Sun, Xuesong Zhang, Shu Yao, Haoyu Zheng, Yvchuan Wang, Huazheng Wang, Zirui Zhuang, Qi Qi, Jianxin Liao, Jingyu Wang
| Challenge: | Existing approaches to code generation fail to consider the quality of retrieved examples. |
| Approach: | They propose a retrieval-augmented generation method that combines existing API examples to improve complexity and readability. |
| Outcome: | The proposed method achieves up to 22% accuracy improvement over baseline methods. |
Tracing Training Footprints: A Calibration Approach for Membership Inference Attacks Against Multimodal Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to improve difficulty calibration for Multimodal Large Language Models only consider text input . visual embeddings in training data reduce effectiveness of these methods . |
| Approach: | They propose a method to detect member samples in poorly generalized local manifolds by visual embeddings. |
| Outcome: | The proposed method surpasses existing methods. |
Confusion is the Final Barrier: Rethinking Jailbreak Evaluation and Investigating the Real Misuse Threat of LLMs (2025.findings-emnlp)
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Yu Yan, Sheng Sun, Zhe Wang, Yijun Lin, Zenghao Duan, Zhifei Zheng, Min Liu, Zhiyi Yin, Jianping Zhang
| Challenge: | Large Language Models have been developed to deal with real-world crimes, but it remains unclear whether they internalize authentic knowledge or are forced to simulate toxic language patterns. |
| Approach: | They construct knowledge-intensive Q&A to investigate misuse threats of Large Language Models in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. |
| Outcome: | The findings raise concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM . |
M-MAD: Multidimensional Multi-Agent Debate for Advanced Machine Translation Evaluation (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have shown their potential to deliver human-like judgments. |
| Approach: | They propose a systematic LLM-based multi-agent framework for advanced LLM as-a-judge MT evaluation that integrates dimension-specific results into a final evaluation judgment. |
| Outcome: | The proposed framework outperforms existing LLM-as-a-judge methods and competes with state-of-the-art automatic metrics even when powered by a suboptimal model like GPT-4o mini. |
Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training (2026.acl-long)
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Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Yue Cao, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng
| Challenge: | Existing approaches to training agents for visual-language models trap them in local optima, hindering exploration and error correction with the environment. |
| Approach: | They propose a hierarchical training recipe that bridges atomic action execution and strategic task completion. |
| Outcome: | The proposed training recipe bridges atomic action execution and strategic task completion. |
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step (2024.acl-long)
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Zehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu, Jiangning Liu, Miao Zheng, Jingming Zhuo, Songyang Zhang, Dahua Lin, Kai Chen, Feng Zhao
| Challenge: | Existing studies evaluate the tool utilization ability of large language models based on the final output or only consider the single-step tool calling. |
| Approach: | They propose a new approach to evaluate the tool utilization capability of large language models (LLMs) they decompose the tool usage into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. |
| Outcome: | The proposed model exhibits consistency with the outcome-oriented evaluation and provides a more fine-grained analysis of the capabilities of LLMs. |
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc. |
| Approach: | They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval. |
| Outcome: | The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation. |
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging (2024.emnlp-main)
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| Challenge: | Existing studies suggest that the order of training samples can affect model performance, but this is not the case. |
| Approach: | They propose to merge supervised fine-tuning models with different data orders to mitigate this imbalance by parameter merging. |
| Outcome: | The proposed method outperforms the weighted-average method on five datasets. |