Papers by Mao Zhang
Full-Step-DPO: Self-Supervised Preference Optimization with Step-wise Rewards for Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals. |
| Approach: | They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step. |
| Outcome: | The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
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| Challenge: | Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon . |
| Approach: | They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier . |
| Outcome: | The proposed dataset contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcatic texts. |
Dynamic Routing Transformer Network for Multimodal Sarcasm Detection (2023.acl-long)
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| Challenge: | Existing methods for multimodal sarcasm detection rely on fixed architectures to capture cross-modal incongruity. |
| Approach: | They propose a method that uses dynamic paths to activate different routing transformer modules with hierarchical co-attention adapting to cross-modal incongruity. |
| Outcome: | The proposed method is compared to state-of-the-art methods on a public dataset. |
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
| Approach: | They propose a framework for conversational machine reading comprehension based on shared parameter mechanism . experimental results show the proposed framework achieves new state-of-the-art results on the ShARC leaderboard . |
| Outcome: | The proposed framework achieves state-of-the-art on the ShARC leaderboard with the BLEU-4 score of 55.2. |
Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
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| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
| Outcome: | The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency . |
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models (2024.emnlp-main)
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| Challenge: | Recent research has focused on pushing weight-only quantization to extremely low-bit due to numerical representation limitations. |
| Approach: | They propose a vector-based quantization approach that pushes LLMs to extremely low-bit . they propose scalar-based weight quantization that reduces memory requirements and optimizes storage costs . |
| Outcome: | The proposed method reduces model quantization perplexity by 0.01-0.34 on LLaMA-2, 0.38-0.68 on mistral-7B, 4.41-7.34, on llaMA-3 on QA tasks on average. |
An Untold Story of Preprocessing Task Evaluation: An Alignment-based Joint Evaluation Approach (2024.lrec-main)
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Eunkyul Leah Jo, Angela Yoonseo Park, Grace Tianjiao Zhang, Izia Xiaoxiao Wang, Junrui Wang, MingJia Mao, Jungyeul Park
| Challenge: | a preprocessing task such as tokenization and sentence boundary detection (SBD) has been considered as a solution to many NLP challenges . however, the low error rates of current methods are mainly specific to certain tasks and rule-based tokenization can be difficult to use across different systems. |
| Approach: | They propose an evaluation algorithm that combines both tokenization and SBD results to improve evaluation reliability. |
| Outcome: | The proposed evaluation algorithm improves the reliability of evaluations by reevaluating the counts of true positive cases for F1 measures in both preprocessing tasks jointly. |
Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (2023.emnlp-main)
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| Challenge: | Existing studies on stance detection were conducted mainly in English due to the low-resource problem in most non-English languages. |
| Approach: | They propose to use a cross-lingual teacher and a teacher to transfer knowledge from source to target language to bridge the discrepancy between languages. |
| Outcome: | The proposed framework bridges the discrepancy between languages and generalizes the knowledge to unseen targets in target language. |
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression (2020.coling-main)
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Yihuan Mao, Yujing Wang, Chufan Wu, Chen Zhang, Yang Wang, Quanlu Zhang, Yaming Yang, Yunhai Tong, Jing Bai
| Challenge: | Existing models that use knowledge distillation are memory-intensive and latency-prohibitive . Existing solutions that use this knowledge distilling framework are expensive . |
| Approach: | They propose a solution that uses weight pruning, matrix factorization and knowledge distillation to learn a smaller model. |
| Outcome: | The proposed model reduces the training overheads by an order of magnitude on public datasets while preserving state-of-the-art accuracy. |
The APVA-TURBO Approach To Question Answering in Knowledge Base (C18-1)
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| Challenge: | Existing query languages for question answering over knowledge bases are not capable of processing queries presented in human language directly. |
| Approach: | They advocate a new model architecture that includes a verification mechanism for checking the correctness of predicted relations. |
| Outcome: | The proposed approach dramatically improves the question answering performance. |
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. |
LAFaCT: Attribution-based Localization and Focused Sequential Analysis of Fact-Critical Tokens for Hallucination Detection (2026.acl-long)
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| Challenge: | Large Language Models suffer from hallucinations, severely undermining their reliability. |
| Approach: | They propose a framework that localizes fact-critical tokens and performs sequential analysis on their hidden states. |
| Outcome: | The proposed framework localizes fact-critical tokens using Factual Criticality . it then performs a focused sequential analysis on their hidden states . |
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)
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Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, Zijian Zhang, Yifei Tao, Dongxing Mao, Yang Wan, Jingru Tan, Min Zeng, Min Li, Alex Jinpeng Wang
| Challenge: | Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Approach: | They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models. |
| Outcome: | The proposed benchmark is the first to scale task complexity while capturing diverse scenarios. |
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but are vulnerable to backdoor attacks. |
| Approach: | They propose a chain-of-scrutiny approach which leverages LLMs’ unique reasoning abilities to mitigate backdoor attacks. |
| Outcome: | The proposed model is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. |
Feature-Adaptive and Data-Scalable In-Context Learning (2024.acl-long)
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| Challenge: | In-context learning (ICL) is a popular way to stimulate LLM capabilities for downstream tasks due to context length constraints. |
| Approach: | They propose a feature-adaptive and data-scalable in-context learning framework which leverages task-adaptives to promote inference on the downstream task. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on 10 datasets under different data settings and LLM scale. |
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
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| Challenge: | Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities. |
| Approach: | They propose a framework that integrates emotion correlation learning, utilization, and supervising. |
| Outcome: | The proposed framework improves empathetic perception and expression on a humanized dialogue dataset. |
Youling: an AI-assisted Lyrics Creation System (2020.emnlp-demos)
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Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu, Yadong Xi, Changjie Fan, Minlie Huang
| Challenge: | Recent studies have focused on a single pass of lyrics generation with little human intervention. |
| Approach: | They propose an AI-assisted lyrics creation system that supports one pass full-text generation and interactive generation modes. |
| Outcome: | The proposed system supports full-text generation and interactive generation modes . it also provides a revision module which enables users to revise undesired lyrics repeatedly. |
An Element is Worth a Thousand Words: Enhancing Legal Case Retrieval by Incorporating Legal Elements (2024.findings-acl)
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| Challenge: | Existing methods for legal case retrieval lack the definition of relevance for legal cases . however, the definition goes beyond the common semantic relevance of ad-hoc retrieval. |
| Approach: | They propose a legal element dataset that incorporates legal elements into a semi-automatic method . they propose two models to enhance legal search using legal elements . |
| Outcome: | The proposed models outperform existing methods in enhancing legal search using legal elements. |
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering (2022.coling-1)
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Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yajuan Lyu
| Challenge: | Existing models of biomedical question answering are limited in their ability to predict answers . a new model improves the performance of existing models, but the code will be released after the paper is published. |
| Approach: | They propose a hierarchical representation-based dynamic reasoning network to solve biomedical problems. |
| Outcome: | The proposed model significantly improves on three mainstream biomedical datasets . the code will be released after the paper is published . |
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations (2025.naacl-long)
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Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, Zhihan Zhang, Jingbo Shang, Xian Li, Trishul Chilimbi
| Challenge: | Existing benchmarks for recommendation explanation evaluation lack item diversity and user preferences data. |
| Approach: | They propose a model-agnostic recommendation explanation evaluation benchmark based on Amazon e-commerce categories with implicit preferences . they propose two novel automatic evaluators that enable scalable and human-preference aligned evaluation of explanations . |
| Outcome: | The proposed model-agnostic evaluation benchmark outperforms existing methods in a variety of domains. |
IDEATE: Detecting AI-Generated Text Using Internal and External Factual Structures (2024.lrec-main)
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| Challenge: | Existing methods to detect AI-generated text rely on internal evidences, but external evidences are not considered. |
| Approach: | They propose a hierarchical graph network that utilizes internal and external factual structures to detect AI-generated text. |
| Outcome: | The proposed network outperforms current state-of-the-art methods on four datasets. |
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. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Text Style Transfer with Contrastive Transfer Pattern Mining (2023.acl-long)
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| Challenge: | Existing methods for text style transfer only focus on the transformation between styles, yet they do not take into account that this transformation can be achieved via different hidden transfer patterns. |
| Approach: | They propose a novel approach which automatically mines hidden transfer patterns to improve TST . they use a clustering module to automatically discover hidden transfer pattern from the data . |
| Outcome: | The proposed method can be applied in a plug-and-play manner to enhance other methods to further improve their performance. |
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. |
DropMix: A Textual Data Augmentation Combining Dropout with Mixup (2022.emnlp-main)
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| Challenge: | Existing methods to overcome overfitting in text learning do not consider dimensionality . dimensionalization is important for deep neural networks to overcome the problem . |
| Approach: | They propose a saliency map-based approach to overcome overfitting in text learning . they propose augmentation regularization methods such as Dropout and Mixup to improve regularization . |
| Outcome: | Empirical results show that the proposed approach overcomes overfitting in text learning . dropout and mixup methods are effective in enhancing regularization . |
LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)
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| Challenge: | prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios. |
| Approach: | They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework. |
| Outcome: | The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data. |
Learning VAE-LDA Models with Rounded Reparameterization Trick (2020.emnlp-main)
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| Challenge: | No reparameterization form of Dirichlet distributions is known to date for topic models . |
| Approach: | They propose a method to reparameterize Dirichlet distributions for the learning of VAE-LDA models by using a latent Dirichlets prior. |
| Outcome: | The proposed method outperforms existing neural topic models on benchmark datasets and on a synthetic dataset. |
Text Style Transferring via Adversarial Masking and Styled Filling (2022.emnlp-main)
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| Challenge: | Existing models for text style transfer suffer from two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure lack diversity and semantic consistency. |
| Approach: | They propose a style transfer model with adversarial masking and styled filling techniques to solve these challenges. |
| Outcome: | The proposed model performs well on two benchmark text style transfer data sets. |
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023.acl-long)
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| Challenge: | Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversation scenes. |
| Approach: | They propose a one-stage end-to-end framework to bridge the information gap between decision-making and question generation in a global understanding manner. |
| Outcome: | The proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. |
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization (2022.acl-long)
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Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, Dragomir Radev
| Challenge: | Existing models struggle with summarizing long text due to high memory complexity of the full self-attention. |
| Approach: | They propose a dynamic latent extraction approach for abstractive long-input summarization that treats extracted text snippets as latent variables and allows dynamic attention weights during decoding. |
| Outcome: | The proposed method outperforms existing methods on GovReport, QMSum, and arXiv while yielding strong results on arX. |
Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method (2024.findings-emnlp)
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Xinshu Shen, Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai
| Challenge: | Existing GEC datasets in Chinese fail to consider specific grammatical error types and overlook cross-sentence grammamatical errors. |
| Approach: | They propose to use Chinese essay fluency assessment to assess essay fluencies along with coarse and fine-grained errors and corrections to improve explainability. |
| Outcome: | The proposed dataset encapsulates essay fluency scores along with both coarse and fine-grained errors and corrections. |
LaTeX2Solver: a Hierarchical Semantic Parsing of LaTeX Document into Code for an Assistive Optimization Modeling Application (2023.acl-demo)
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Rindra Ramamonjison, Timothy Yu, Linzi Xing, Mahdi Mostajabdaveh, Xiaorui Li, Xiaojin Fu, Xiongwei Han, Yuanzhe Chen, Ren Li, Kun Mao, Yong Zhang
| Challenge: | Existing systems that translate optimization formulas manually are cumbersome and time-consuming. |
| Approach: | They propose a system that converts optimization formulas from TeX document to solver language. |
| Outcome: | The proposed system helps operations research practitioners convert optimization formulations into solver modeling languages. |
Knowledge Rumination for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing studies have shown that pre-trained language models lack the capacity to handle knowledge-intensive tasks alone. |
| Approach: | They propose a new paradigm to help pre-trained language models utilize latent knowledge without retrieving it from external corpus. |
| Outcome: | The proposed paradigm can be applied to pre-trained language models without retrieving external knowledge from the corpus. |
Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding (2022.emnlp-main)
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| Challenge: | Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in an image using only caption-image annotations. |
| Approach: | They propose a novel contrastive learning framework that adaptively refines the target prediction by using only caption-image annotations. |
| Outcome: | The proposed framework outperforms existing methods on two widely used benchmarks, Flickr30K Entities and RefCOCO+. |
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks (2022.findings-naacl)
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| Challenge: | Experimental results show that Transformer Encoder model can't automatically capture word order, so explicit position embeddings are required to be fed into the target model. |
| Approach: | They propose a Transformer-based language model DecBERT that uses a causal attention mask to capture word order. |
| Outcome: | The proposed model improves on the GLUE language understanding benchmark and accelerates the pre-training process. |
Curriculum Learning for Natural Language Understanding (2020.acl-main)
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| Challenge: | Pre-trained language models can be fine tuned to perform NLU tasks in a straightforward manner. |
| Approach: | They propose a pretrain-finetune paradigm for natural language understanding (NLU) they propose 'a cross-trainset' approach that allows users to distinguish easy from difficult examples . |
| Outcome: | The proposed approach achieves significant performance improvements on a wide range of NLU tasks. |
Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)
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| Challenge: | Traditional neural network models represent word senses as vectors that are uninterpretable for humans. |
| Approach: | They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions. |
| Outcome: | The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels. |
S2ynRE: Two-stage Self-training with Synthetic data for Low-resource Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for relation extraction suffer from the inadequacy of large-scale annotated data. |
| Approach: | They propose a framework for two-stage self-training with synthetic data for relation extraction . |
| Outcome: | The proposed framework is based on two-stage self-training with synthetic data . it is able to synthesize large quantities of training data and iteratively and alternately learn from synthetic and golden data together. |
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. |
Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)
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| Challenge: | Text-based knowledge graph completion methods neglect knowledge contexts in inferring process. |
| Approach: | They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. |
| Outcome: | The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets. |
MELA: Multilingual Evaluation of Linguistic Acceptability (2024.acl-long)
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| Challenge: | Existing benchmarks on linguistic acceptability have been used to evaluate language models' ability to distinguish between acceptable and unacceptable sentences. |
| Approach: | They present the largest benchmark to date on linguistic acceptability: MELA . they establish LLM baselines on this benchmark and investigate cross-lingual transfer in acceptability judgements with XLM-R. |
| Outcome: | The proposed model outperforms open-source models on cross-lingual transfer in acceptability judgements. |
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. |
SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)
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| Challenge: | Existing process annotation approaches are computationally expensive. |
| Approach: | They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree. |
| Outcome: | The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench. |
Fewer is More: Boosting Math Reasoning with Reinforced Context Pruning (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities, yet they struggle with math reasoning. |
| Approach: | They propose a coarse-to-fine pruner that prunes unimportant tokens to fit the context window. |
| Outcome: | The proposed approach outperforms prompting baselines across various LLMs and 5 math datasets and achieves 4.55% absolute improvements without any fine-tuning. |
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres (2023.acl-long)
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| Challenge: | Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks. |
| Approach: | They propose to model complex dependency among event structured components with energy-based energy-modeling and represent event classes with simple but effective hyperspheres. |
| Outcome: | Experiments on two unified-annotated event datasets show that SPEECH is predominant in event detection and event-relation extraction tasks. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
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 Joint Learning Framework for Restaurant Survival Prediction and Explanation (2022.emnlp-main)
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| Challenge: | Recent advances in deep learning have various models that research reviews and interactions for different kinds of tasks, such as predicting restaurant survival. |
| Approach: | They propose a joint learning framework for explainable restaurant survival prediction based on multi-modal data of user-restaurant interactions and users’ textual reviews. |
| Outcome: | The proposed framework improves on two datasets showing that it can model restaurant interactions and users’ textual reviews. |
Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration (2024.emnlp-main)
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| Challenge: | Existing methods for generating large language models have been criticized for their complexity and instability. |
| Approach: | They propose a value-based calibration method to better align Large Language Models with human preferences. |
| Outcome: | The proposed method surpasses existing methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and diversity in different settings. |
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input (2022.acl-demo)
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| Challenge: | Existing systems based on attributes or keywords render lyrics generation very limited . previous studies focused on generating lyrics based only on attributes and keywords . |
| Approach: | They propose to use Chinese passage-level text as input for lyrics generation . they initialize parameters with custom pretrained Chinese GPT-2 model and adopt a two-step process to fine-tune the model for better alignment between passage- level text and lyrics. |
| Outcome: | The proposed system is conditioned on passage-level text rather than attributes or keywords, rendering limited control over the content of the lyrics. |
SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction (2022.naacl-main)
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| Challenge: | Existing methods for relation extraction only implicitly learn to model relevant contexts and entity types while being trained for RE. |
| Approach: | They propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for RE. |
| Outcome: | The proposed method outperforms the runner-up method on three benchmarks by 5.04% . textual contexts and entity types are the major information sources that lead to the success of previous approaches. |
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing methods for Named Entity Recognition (NER) use a similarity metric to measure semantic similarity between test samples and referents, but their performance is limited due to the label scarcity. |
| Approach: | They propose a novel approach to learn a similarity metric for measuring the semantic similarity between test samples and referents, where each referent represents an entity class. |
| Outcome: | The proposed approach outperforms state-of-the-art models with a significant margin in most cases. |
Eliminating Out-of-Domain Recommendations in LLM-based Recommender Systems: A Unified View (2026.findings-acl)
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Hao Liao, Jiwei Zhang, Jianxun Lian, Wensheng Lu, Mingqi Wu, null Shuowangg, Yong Zhang, Yitian Huang, Mingyang Zhou, Rui Mao
| Challenge: | Existing approaches to reduce OOD recommendations fall into three grounding paradigms: retrieval, constrained generation and discrete item tokenizer generation. |
| Approach: | They propose a framework that instantiates three grounding paradigms under a single architecture . embedding-based retrieval, constrained generation and discrete item-tokenizer methods are implemented . |
| Outcome: | The proposed framework eradicates OOD recommendations across all variants and achieves state-of-the-art accuracy compared to strong ID-based and LLM-based baselines. |
ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)
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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong
| Challenge: | Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs). |
| Approach: | They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models. |
| Outcome: | The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks. |
Editing Conceptual Knowledge for Large Language Models (2024.findings-emnlp)
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Xiaohan Wang, Shengyu Mao, Shumin Deng, Yunzhi Yao, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang
| Challenge: | Existing knowledge editing methods can modify concept-level definitions, but they can distort instantial knowledge in LLMs, leading to poor performance. |
| Approach: | They construct a benchmark dataset ConceptEdit and establish new metrics for evaluation to investigate the editing capability of LLMs. |
| Outcome: | The proposed methods can modify concept definitions but can distort instantial knowledge in LLMs, leading to poor performance. |
Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding (2026.acl-long)
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| Challenge: | Existing vision-language models suffer from visual hallucination, where the generated responses contain inaccuracies that are not grounded in the visual input. |
| Approach: | They propose a visual decoding method that iteratively isolates relevant visual tokens based on attention and magnifies the corresponding regions. |
| Outcome: | The proposed method reduces language biases and amplifies weights of visual embedding during decoding, while still preserving strong reasoning capabilities. |
Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)
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| Challenge: | Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs. |
| Approach: | They propose an alternative approach to represent entities by composing entity-corresponding codewords matched from predefined small-scale codebooks. |
| Outcome: | The proposed approach achieves similar results to existing methods. |
Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)
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| Challenge: | Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities. |
| Approach: | They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions. |
| Outcome: | The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset. |
BLADE: Benchmarking Language Model Agents for Data-Driven Science (2024.findings-emnlp)
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Ken Gu, Ruoxi Shang, Ruien Jiang, Keying Kuang, Richard-John Lin, Donghe Lyu, Yue Mao, Youran Pan, Teng Wu, Jiaqian Yu, Yikun Zhang, Tianmai Zhang, Lanyi Zhu, Mike Merrill, Jeffrey Heer, Tim Althoff
| Challenge: | Language model-based agents can be used to conduct and support data-driven science, but evaluating them on open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions. |
| Approach: | They propose a benchmark to automatically evaluate agents’ multifaceted approaches to open-ended research questions. |
| Outcome: | BLADE evaluates agents’ multifaceted approaches to open-ended research questions using data from 12 datasets and research questions drawn from existing scientific literature. |
When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models (2026.findings-acl)
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Yingzhi Mao, Chunkang Zhang, Junxiang Wang, Xinyan Guan, Boxi Cao, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun
| Challenge: | Existing methods often apply coarse-grained constraints over entire reasoning trajectories . Existing approaches often apply unsafe constraints, causing unsafe outputs . |
| Approach: | They propose a trajectory-level training framework that mitigates Self-Jailbreak . they propose 'chain-of-guardrail' to mitigate self-jailbreak by targeting step-level interventions . |
| Outcome: | The proposed framework mitigates Self-Jailbreak by targeting step-level interventions while maintaining reasoning ability. |
Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms (2025.acl-long)
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| Challenge: | Recent research has explored the use of sparse autoencoders (SAE) to disentangle knowledge in high-dimensional spaces for steering. |
| Approach: | They propose a method that isolates and manipulates disentangled knowledge components to enhance safety by using sparse autoencoders to disentangle knowledge in high-dimensional spaces for steering. |
| Outcome: | The proposed method is able to isolate and manipulate disentangled knowledge components to enhance safety in large reasoning models. |
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction (2024.findings-acl)
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| Challenge: | Chinese Spelling Correction (CSC) lacks large-scale high-quality corpora due to labor-intensive labeling of spelling errors in real-life writing or typing scenarios. |
| Approach: | They propose to use OCR/ASR-based generation to refine Chinese Spelling Correction models on random replacement-based corpora and filter them based on prediction confidence. |
| Outcome: | The proposed model outperforms existing models on three widely-used benchmarks while significantly alleviating over-correction. |
NetSafe: Exploring the Topological Safety of Multi-agent System (2025.findings-acl)
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Miao Yu, Shilong Wang, Guibin Zhang, Junyuan Mao, Chenlong Yin, Qijiong Liu, Kun Wang, Qingsong Wen, Yang Wang
| Challenge: | Large language models (LLMs) have fueled significant progress in intelligent Multi-agent Systems (MAS), with expanding academic and industrial applications. |
| Approach: | They propose a framework that unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
| Outcome: | The proposed framework unifies diverse MAS workflows via iterative RelCom interactions to enable generalized analysis. |
Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) however, LLMs exhibit a stylistic bias when presented with mixed contexts, revealing a bottleneck in their utility. |
| Approach: | They propose a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts. |
| Outcome: | The proposed model improves RAG pipelines by 8% with negligible latency overhead. |
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling (2025.findings-acl)
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| Challenge: | Large language models (LLMs) enabled dialogue systems are one of the central modes in human-machine interaction. |
| Approach: | They propose a benchmark task for dialogue element MOdeling and Element Awareness and a new benchmark for dialogue agent interaction that allows the agent to model dialogue elements via imitation learning. |
| Outcome: | The proposed agent performs well in both dialogue element modeling and out-of-domain tasks. |
HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold (2022.findings-emnlp)
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| Challenge: | Existing methods for event detection have failed to address the problem of constantly emerging event types with limited data. |
| Approach: | They propose a novel method for event detection with a task-adaptive threshold . they propose to learn discriminative representations with 'two-view contrastive loss' |
| Outcome: | The proposed method achieves better results than the state-of-the-art methods on a benchmark dataset. |
FeTaQA: Free-form Table Question Answering (2022.tacl-1)
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Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, Dragomir Radev
| Challenge: | Existing table-based question answering datasets lack advanced information-based questions that require reasoning and integration of information pieces retrieved from structured knowledge sources. |
| Approach: | They propose a dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs that can be used to generate an answer. |
| Outcome: | The proposed dataset has 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
Training Language Models to Critique With Multi-agent Feedback (2025.findings-emnlp)
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Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li, Chen Xu, Heyan Huang, Dahua Lin, Xian-Ling Mao, Kai Chen
| Challenge: | utilizing human annotations can enhance critique ability, but model-generated critiques suffer from inherent flaws due to complexity of critique . a new framework that leverages multi-agent feedback improves critique ability . |
| Approach: | They propose a framework that leverages multi-agent feedback to improve critique ability . they propose to use supervised fine-tuning and reinforcement learning to improve this capability . |
| Outcome: | The proposed framework improves critique ability in both supervised fine-tuning and reinforcement learning stages. |
Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels (2024.lrec-main)
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| Challenge: | a framework for analyzing gender bias in terms of female objectification is proposed . male gaze refers to a phenomenon in which women are depicted as objects of aesthetic pleasure . |
| Approach: | They propose a framework for analyzing gender bias in terms of female objectification . they compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities . |
| Outcome: | The proposed framework measures female objectification along two axes. |
Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)
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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang
| Challenge: | Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models. |
| Approach: | They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data. |
| Outcome: | The proposed framework improves the factuality of LM generators by enhancing their training data. |
Vanessa: Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing methods to analyze images focus on superficial features or descriptions, omitting subtle contextual information. |
| Approach: | They propose a Visual Connotation and Aesthetic Attributes Understanding Network (Vanessa) for Multimodal Aspect-based Sentiment Analysis. |
| Outcome: | The proposed network captures both implicit and explicit sentimental cues and can be used to enrich textual sentiment analysis. |
WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora (2026.findings-acl)
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| Challenge: | Existing benchmarks for Graph-based Retrieval-Augmented Generation (GraphRAG) rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents. |
| Approach: | They propose a benchmark to assess GraphRAG performance in the wild using Wikipedia's unique structure where cohesive narratives are grounded in long and heterogeneous external reference documents. |
| Outcome: | Experiments with articles across 12 top-level topics show that GraphRAG performs better in the wild than existing methods. |
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data Synthesis (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes. |
| Approach: | They propose a framework to measure the risk coverage of alignment datasets across three dimensions: Lexical Diversity, Malicious Intent, and Jailbreak Tactics. |
| Outcome: | The proposed framework measures risk coverage across Lexical Diversity, Malicious Intent, and Jailbreak Tactics. |
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)
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Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li
| Challenge: | Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench. |
| Approach: | They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development. |
| Outcome: | The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development. |
Enhancing Language Model with Unit Test Techniques for Efficient Regular Expression Generation (2023.emnlp-industry)
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| Challenge: | Recent studies have shown that generative language models lack functional correctness, which is a critical aspect of regular expressions. |
| Approach: | They propose a method that takes functional correctness into account and transforms it into a differentiable gradient feedback using policy gradient techniques. |
| Outcome: | The proposed method has been used in a regulatory scenario to ensure that all online content is free from non-compliant elements, thereby significantly reducing the workload of relevant personnel. |
FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)
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| Challenge: | Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing. |
| Approach: | They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report . |
| Outcome: | Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models. |
MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)
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Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei
| Challenge: | Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation. |
| Approach: | They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination. |
| Outcome: | The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination. |
A Transformational Biencoder with In-Domain Negative Sampling for Zero-Shot Entity Linking (2022.findings-acl)
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| Challenge: | Recent work on entity linking has focused on the zero-shot scenario where at test time the entity mention to be labelled is never seen during training. |
| Approach: | They propose a transformational biencoder that integrates a transform into BERT to perform a zero-shot transfer from the source domain to the target domain. |
| Outcome: | The proposed model performs a zero-shot transfer from the source domain to the target domain on a benchmark dataset and achieves new state-of-the-art. |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
RESEMO: A Benchmark Chinese Dataset for Studying Responsive Emotion from Social Media Content (2024.findings-acl)
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| Challenge: | Existing studies on social media text processing do not focus on responsive emotion analysis. |
| Approach: | They propose a Chinese dataset named ResEmo for responsive emotion analysis, including 3813 posts with 68,781 comments collected from Weibo, the largest social media platform in China. |
| Outcome: | The proposed dataset includes 3813 posts with 68,781 comments collected from weibo, the largest social media platform in China. |
M-RangeDetector: Enhancing Generalization in Machine-Generated Text Detection through Multi-Range Attention Masks (2025.findings-acl)
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| Challenge: | Existing supervised methods for text detection are overfitting within their training domains. |
| Approach: | They propose a method that integrates four distinct attention masking strategies into a Multi-Range Attention module to learn various writing strategies for machine-generated text detection. |
| Outcome: | The proposed method improves the generalization capability of existing detectors on three datasets. |
CSL: A Large-scale Chinese Scientific Literature Dataset (2022.coling-1)
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| Challenge: | Existing datasets centered around the English language restrict development of Chinese scientific NLP. |
| Approach: | They present a large-scale Chinese scientific literature dataset based on Chinese papers . they use semi-structured data as a natural annotation for many supervised NLP tasks . |
| Outcome: | The proposed dataset can serve as a Chinese corpus and perform many supervised tasks. |
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. |
Finetuning Pretrained Transformers into RNNs (2021.emnlp-main)
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Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith
| Challenge: | Efficient transformers outperform recurrent neural networks in natural language generation, but this comes with significant computational cost and memory footprint during generation. |
| Approach: | They propose to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy. |
| Outcome: | The proposed transformers outperform recurrent neural networks in natural language generation but come with significant computational and memory footprint during generation. |
AutoAlign: Get Your LLM Aligned with Minimal Annotations (2025.acl-demo)
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Xinyu Lu, Dong Xu, Chunkang Zhang, Xinyan Guan, Junxiang Wang, Qingyu Zhang, Pengbo Wang, Yingzhi Mao, Hao Xiang, Xueru Wen, Zichao Li, Yaojie Lu, Hongyu Lin, Le Sun, Xianpei Han
| Challenge: | Automated Alignment (ALM) is a set of algorithms designed to align Large Language Models (LLMs) with human intentions and values while minimizing manual intervention. |
| Approach: | They propose an open-source toolkit that integrates mainstream automated algorithms through a consistent interface and an accessible workflow supporting one-click execution for prompt synthesis and automatic alignment signal construction. |
| Outcome: | The proposed framework enables easy reproduction of existing results through extensive benchmarks and facilitates the development of novel approaches via modular components. |
MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction (2022.findings-acl)
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| Challenge: | Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. |
| Approach: | They propose a general detector-corrector multi-task framework which exploits the visual and phonological features of the misspelled characters and minimizes their misleading impact on the context. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on Chinese Spelling Correction tasks. |
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation (2022.acl-long)
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| Challenge: | Existing work on pre-trained generative models often fails to detect non-existent or incorrect content . Existing studies have attempted to detect hallucinations based on oracle references . |
| Approach: | They propose a token-level, reference-free hallucination detection task based on Wikipedia annotations to detect non-existent or incorrect content. |
| Outcome: | The proposed task is token-level, reference-free hallucination detection task and dataset . authors argue that the proposed task can be used in real-time to detect hallucines . |
Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation (2025.findings-emnlp)
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| Challenge: | Argument generation with diverse perspectives is essential for fostering balanced discourse and mitigating bias. |
| Approach: | They propose a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation. |
| Outcome: | The proposed framework enhances perspective diversity through preference optimization based on the constructed preference dataset . |
Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction (2024.findings-emnlp)
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| Challenge: | Existing large language models (LLMs) underperform in legal judgment prediction due to challenges in understanding case facts and distinguishing between similar charges. |
| Approach: | They propose a framework that allows LLMs to discriminate among charges and a judicial reasoning framework to improve their models for effective legal judgment prediction. |
| Outcome: | The proposed framework improves accuracy and efficiency when dealing with complex and confusing charges. |
Anaphor Assisted Document-Level Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for document-level relation extraction are incomplete and lack anaphor for identifying relations between entities. |
| Approach: | They propose an Anaphor-Assisted (AA) framework for document-level relation extraction . they use a document or sentences as intermediate nodes to model cross-sentence entity interactions . |
| Outcome: | The proposed framework achieves state-of-the-art on the widely-used datasets. |
TARA: Token-level Attribute Relation Adaptation for Multi-Attribute Controllable Text Generation (2024.findings-emnlp)
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| Challenge: | Existing work on multi-attribute controllable text generation ignores interrelations of attributes . recent work defines attribute relations as promotive, but not fixed . |
| Approach: | They propose a method that explicitly defines attribute relations as inhibtory for multi-attribute CTG . they propose 'tara' which employs token-level attribute relation adaptation and representation to generate text with the balanced multi-attribut . |
| Outcome: | The proposed method generates text with the balanced multi-attribute control. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |
SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection (2025.acl-long)
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| Challenge: | Existing evaluation methods for Open Domain Event Detection (ODED) lack representative representations of the real world, making it difficult to accurately reflect performance of various ODED methods in real-world scenarios. |
| Approach: | They propose a scalable and reliable Semantic-level Evaluation framework for Open domain event detection by constructing a more representative evaluation benchmark and introducing a semantic evaluation metric. |
| Outcome: | The proposed framework first constructs a more representative evaluation benchmark that currently includes 564 event types covering 7 major domains, with a cost-effective supplementary annotation strategy to ensure the benchmark’s representativeness. |
SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis (2024.findings-acl)
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| Challenge: | Existing approaches to tackle learning challenges such as knowledge forgetting and extensive computing resources are not effective. |
| Approach: | They propose a novel neurosymbolic method for sentiment analysis that places emphasis on human subjectivity within varying domain annotations. |
| Outcome: | The proposed method is lightweight, robust across domains and languages, efficient few-shot training, and rapid convergence. |
On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)
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| Challenge: | Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure . |
| Approach: | They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training. |
| Outcome: | The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration. |
Grammatical Error Correction via Mixed-Grained Weighted Training (2023.findings-emnlp)
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| Challenge: | Empirical evaluation shows that MainGEC achieves consistent and significant performance improvements on two benchmark datasets. |
| Approach: | They propose to use mixed-grained weighted training to improve the training effect for GEC by analyzing the inherent discrepancies in annotated training data. |
| Outcome: | Empirical results show that the proposed method achieves significant performance improvements on two benchmark datasets. |
SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents (2022.acl-long)
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Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, Rui Zhang
| Challenge: | Existing methods to handle long text are limited due to time and memory complexity and limited input lengths. |
| Approach: | They propose a multi-stage split-then-summarize framework for long input summarization . their framework can process input text of arbitrary length by adjusting the number of stages . |
| Outcome: | The proposed framework outperforms existing methods on three long meeting summarization datasets and on a long document summarizing dataset. |
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning (2025.naacl-long)
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| Challenge: | Strategic reasoning requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. |
| Approach: | They propose a framework that enables Large Language Models to achieve varying levels of strategic depth by recursive mechanisms that allow agents to form higher order beliefs about others' beliefs. |
| Outcome: | The proposed framework enables LLMs to achieve varying levels of strategic depth, allowing agents to form higher order beliefs—beliefs about others’ beliefs. |
Hierarchical Reward Modeling for Fault Localization in Large Code Repositories (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have limited fault localization capabilities due to limited context length. |
| Approach: | They propose a hierarchical localization reward model to evaluate and select the most accurate fault localization candidates from the outputs of LLMs. |
| Outcome: | The proposed model improves the final line-level localization recall by 12% on the SWE-Bench-Lite dataset. |
Hypernym Discovery via a Recurrent Mapping Model (2021.findings-acl)
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| Challenge: | Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model. |
| Approach: | They propose a parallel style model that maps query words to their hypernyms . they use a lexical-semantic relation to name a specific instance or subtype hyponym . |
| Outcome: | Empirical results on SemEval-2018 Task 9 confirm the effectiveness of the proposed model. |
GPTEval: A Survey on Assessments of ChatGPT and GPT-4 (2024.lrec-main)
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| Challenge: | emergence of ChatGPT has generated speculation about its potential to disrupt social and economic systems. |
| Approach: | They analyze prior assessments of ChatGPT and GPT-4 to analyze their language and reasoning abilities, scientific knowledge, ethical considerations and existing evaluation methods. |
| Outcome: | The proposed model performs satisfactorily in science knowledge and can answer open questions. |
Efficient Data Labeling by Hierarchical Crowdsourcing with Large Language Models (2025.coling-main)
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| Challenge: | Large language models (LLMs) have been gaining attention for their impressive performance in in-context dialogues. |
| Approach: | They propose a hierarchical framework that leverages multiple LLMs for efficient data labeling under budget constraints. |
| Outcome: | The proposed framework outperforms human labelers and GPT-4 in terms of accuracy and efficiency. |
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)
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| Challenge: | Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. |
| Approach: | They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy . |
| Outcome: | The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks. |
A Silver Bullet or a Compromise for Full Attention? A Comprehensive Study of Gist Token-based Context Compression (2025.acl-long)
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| Challenge: | gist-based context compression methods can achieve only slight performance loss on tasks like retrieval-augmented generation and long-document QA, but it faces challenges in tasks like synthetic recall. |
| Approach: | They propose two strategies to improve gist-based context compression in large language models. |
| Outcome: | The proposed methods can achieve only slight performance loss on retrieval-augmented generation and long-document QA tasks, but they face challenges in tasks like synthetic recall. |
Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023.findings-acl)
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| Challenge: | Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages. |
| Approach: | They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations. |
| Outcome: | The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages. |
Bridging Word-Pair and Token-Level Metaphor Detection with Explainable Domain Mining (2024.acl-long)
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| Challenge: | Existing methods for word-pair metaphor detection provide intermediate explainable clues for detection results. |
| Approach: | They propose a method to bridge word-pair and token-level metaphor detection by modeling word pairs as explainable intermediate information. |
| Outcome: | The proposed method bridges word-pair and token-level metaphor detection by using word pairs . it provides intermediate explainable clues for the detection results, but this is a challenge . |
R-CHAR: A Metacognition-Driven Framework for Role-Playing in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing role-playing structures lack cognitive consistency in complex scenarios . Existing models excel in math and coding tasks but lack coherent reasoning . |
| Approach: | They propose a metacognition-driven framework that enhances role-playing performance . experimental results show performance improvements across varying scenario complexities . |
| Outcome: | The proposed framework outperforms existing models in social intelligence tasks and shows strength in long-context comprehension and group-level social interactions. |
ShieldHead: Decoding-time Safeguard for Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in LLM-based moderation methods have demonstrated remarkable promise in identifying safety risks associated with both inputs and outputs in human-AI interactions. |
| Approach: | They propose to learn a classification head on the last-layer hidden states of a dialogue model and use it to detect harmful content. |
| Outcome: | The proposed framework is 300 faster (**1ms**) than previous LLM-based moderation models with 99% less parameters than LlamaGuard. |
Syntax Encoding with Application in Authorship Attribution (D18-1)
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| Challenge: | Existing approaches to extract syntactic features from text or sentences are limited by the loss of rich structural information contained in the syntax tree. |
| Approach: | They propose to embed the syntax parse tree of sentence into a learnable distributed representation . they show that the approach improves upon the prior art and achieves new performance records . |
| Outcome: | The proposed approach improves upon the prior art and achieves new performance records on five benchmarking data sets. |
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics (2021.acl-long)
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Jian Guan, Zhexin Zhang, Zhuoer Feng, Zitao Liu, Wenbiao Ding, Xiaoxi Mao, Changjie Fan, Minlie Huang
| Challenge: | Existing automatic metrics are observed to correlate poorly with human evaluation. |
| Approach: | They propose to use OpenMEVA to evaluate open-ended story generation metrics. |
| Outcome: | The proposed test suite assesses the capabilities of open-ended story generation metrics on annotated stories and auto-constructed test examples. |
Extrapolating to Unknown Opinions Using LLMs (2025.coling-main)
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| Challenge: | ice cream flavors and climate change are among the topics people hold on various topics. |
| Approach: | They propose to use a large language model to extrapolate from stances to unknown opinions by prompting and fine-tuning data to improve their ability to extrapole from known to unknown stance. |
| Outcome: | The proposed model can extrapolate from opinions on known topics to unknown ones and generate reasoning behind extrapolation. |
MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)
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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu
| Challenge: | Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents . |
| Approach: | They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning. |
| Outcome: | The proposed framework outperforms existing methods in performance and inference efficiency. |
Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation (2023.emnlp-main)
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| Challenge: | Controllable text generation (CTG) aims to generate text with desired attributes, but current methods lack high levels of controllability. |
| Approach: | They propose a lightweight decoding framework that reconstructs attribute distributions to balance the weights between attribute words and non-attribute words to generate more fluent text. |
| Outcome: | The proposed framework achieves state-of-the-art control performance on multiple CTG tasks. |
Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)
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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han
| Challenge: | Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. |
| Approach: | They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster. |
| Outcome: | The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines. |
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations (2020.emnlp-main)
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| Challenge: | Existing methods to solve the extraction problem learn interactions between the two tasks through a shared network . |
| Approach: | They propose to use multi-task learning to address the joint extraction of entity and relation . they exploit correlation between ER and relation classification tasks to improve performance . |
| Outcome: | Empirical results show that the proposed model improves on two real-world datasets. |
RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)
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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries. |
| Approach: | They propose a framework for training query rewriting models that leverages a reranker framework. |
| Outcome: | The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models. |
An LLM-Enabled Knowledge Elicitation and Retrieval Framework for Zero-Shot Cross-Lingual Stance Identification (2024.findings-emnlp)
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| Challenge: | Existing research is conducted in monolingual setting on English datasets, whereas in other low-resource languages, it lacks sufficient data for training quality stance detection models. |
| Approach: | They propose a knowledge elicitation and retrieval framework that leverages the capability of large language models for stance knowledge acquisition and matches the target language input to the most relevant stance information. |
| Outcome: | The proposed framework improves on multilingual datasets and competitive baselines. |
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model (2022.naacl-industry)
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Gongzheng Li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao
| Challenge: | Recent studies show that transformer-based models are effective over many tasks, but they are expensive to deploy in the industrial application. |
| Approach: | They propose a transformer-based inference solution that optimizes kernels for long inputs and large hidden sizes and a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. |
| Outcome: | The proposed solution achieves an average speedup of 1.40-4.20x on the transformer decoder layer with an A100 GPU. |
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023.emnlp-main)
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Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
| Challenge: | RepoCoder is a repository-level code completion framework that utilizes the useful information scattered in files. |
| Approach: | They propose a repository-level code completion framework called RepoCoder . it integrates a similarity-based retriever and a pre-trained code language model . they propose 'repoBench' benchmark to validate the framework's effectiveness . |
| Outcome: | The proposed framework outperforms the vanilla retrieval-augmented code completion approach in the real-world. |
Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)
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| Challenge: | Existing methods to answer complex questions rely on decomposition of complex questions into sub-questions . Existing approaches to decompose complex questions are limited by the original question . |
| Approach: | They propose a question decomposition approach to decompose semantically clear questions . they use the decomposed sub-questions to select relevant patterns as auxiliary information . |
| Outcome: | The proposed method achieves state-of-the-art performance on multiple datasets. |
Why Can Distillation Work with Limited Resources? A Systematic Study (2026.findings-acl)
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Xiao Hu, Xingyu Lu, Liyuan Mao, YiFan Zhang, Tianke Zhang, Bin Wen, Fan Yang, Tingting Gao, Guorui Zhou
| Challenge: | Recent advances in large language models have driven reasoning performance . low-resource distillation can boost models' performance, but a framework is missing . |
| Approach: | They conduct a controlled experiment to find out why low-resource distillation can boost model performance . they find that distillation enhances the presence of advanced cognitive behaviors . |
| Outcome: | The proposed model shows more flexible reasoning, the authors show . they show that distillation enhances the presence of advanced cognitive behaviors . |
Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)
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| Challenge: | Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory. |
| Approach: | They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text. |
| Outcome: | The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text. |
Parameter-free Automatically Prompting: A Latent Pseudo Label Mapping Model for Prompt-based Learning (2022.findings-emnlp)
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| Challenge: | Existing manual label mapping methods that require extra parameters and human knowledge are limited in data. |
| Approach: | They propose a Latent Pseudo Label Mapping method that optimizes the label mapping without human knowledge and extra parameters. |
| Outcome: | The proposed method outperforms the standard SOTA method in few-shot learning tasks and significantly outperformed the standard ALM method which requires extra task-specific prior knowledge. |
The Digital Dunning-Kruger Effect: Decoupling Hallucinations via Geometric Hidden-state Observation for Semantic Truthfulness (2026.acl-long)
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Yueheng Mao, Min Yu, Gengwang Li, Jianguo Jiang, Gang Li, Meng Zhang, Zhen Xu, Weiqing Huang, Ming Liu
| Challenge: | Large Language Models (LLMs) often generate overconfident yet factually incorrect hallucinations. |
| Approach: | They propose a black-box-based framework that captures stubborn hallucinations by integrating internal geometric dynamics with output probability distributions. |
| Outcome: | The proposed framework outperforms white-box methods and reduces computational overhead by over 90%. |
Voice Query Auto Completion (2021.emnlp-main)
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Raphael Tang, Karun Kumar, Kendra Chalkley, Ji Xin, Liming Zhang, Wenyan Li, Gefei Yang, Yajie Mao, Junho Shin, Geoffrey Craig Murray, Jimmy Lin
| Challenge: | Existing methods fail to complete voice queries from incomplete prefixes because they use orthographic prefix and substrings instead of the true phonetic prefix. |
| Approach: | They propose to condition QAC approaches on intermediate transcriptions to complete voice queries. |
| Outcome: | The proposed method obtains an 18% relative improvement over previous methods on a speech-enabled smart television with real-life voice search traffic. |
From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning (2026.findings-acl)
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Beining Wu, Fuyou Mao, Jiong Lin, Cheng Yang, Jiaxuan Lu, Yifu Guo, Siyu Zhang, Yifan Wu, Ying Huang, Fu Li
| Challenge: | Generative engines (GEs) are replacing ranked links with citation-grounded answers . current methods are unable to accumulate or transfer effective strategies across tasks and engines . |
| Approach: | They propose a multi-agent framework where planning, editing, and fidelity-aware evaluation serve as the execution layer. |
| Outcome: | The proposed framework outperforms heuristic baselines in visibility and citation fidelity on three mainstream engines. |
Parallel Interactive Networks for Multi-Domain Dialogue State Generation (2020.emnlp-main)
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| Challenge: | Existing models do not account for the dependencies between system and user utterances in the same turn and across different turns. |
| Approach: | They propose to integrate an interactive encoder to jointly model in-turn dependencies and cross-turn dependents. |
| Outcome: | The proposed model is superior to existing models and can be used to selectively copy words from historical system utterances or historical user utterrances. |