Papers by Li Yuan

Literature Meets Data: A Synergistic Approach to Hypothesis Generation (2025.acl-long)

Challenge:	Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored.
Approach:	They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios.
Outcome:	The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC.

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Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, Chenfei Yuan, Chenhao Tan

Challenge:	Existing methods for hypothesis generation are theory-driven and data-driven, but they lack the computational power to complement each other.
Approach:	They develop a method that combines literature-based insights with data to perform LLM-powered hypothesis generation.
Outcome:	The proposed method outperforms baseline methods on five datasets and shows human accuracy improves on deception detection and AI generated content detection tasks.

DiffuSpec: Unlocking Diffusion Language Models for Speculative Decoding (2026.findings-acl)

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Guanghao Li, Zhihui Fu, Min Fang, Qibin Zhao, Ming Tang, Chun Yuan, Jun Wang

Class Name Guided Out-of-Scope Intent Classification (2024.findings-emnlp)

Challenge:	Autoregressive (AR) decoding in large language models is latency-bounded by strictly sequential token generation.
Approach:	They propose a diffusion-based drafter that proposes multi-token candidates and then verifies them in parallel by the target model.
Outcome:	The proposed drafter generates multi-token proposals in a single forward pass while remaining compatible with standard AR verifiers.

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Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Sahu, Xiaoli Li

FISTAPruner: Layer-wise Post-training Pruning for Large Language Models (2025.emnlp-main)

Challenge:	SCOOS leverages semantic cues embedded in class labels to improve classification accuracy.
Approach:	They propose a method to create a compact feature space around class label semantics . they use a shared latent space between ID features and class names to minimize losses .
Outcome:	The proposed method outperforms existing methods for out-of-scope intent detection and ID intent classification.

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Pengxiang Zhao, Hanyu Hu, Ping Li, Yi Zheng, Zhefeng Wang, Xiaoming Yuan

A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation (2024.lrec-main)

Challenge:	Existing pruning methods require inefficient retraining for billion-scale LLMs or rely on heuristicically designed metrics to determine pruning masks, leading to performance degradation.
Approach:	They propose a convex optimization model that induces sparsity in large language models by leveraging FISTA.
Outcome:	The proposed method can remove 50% of model parameters while retaining 98.6% and 95.6% of the zero-shot performance.

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Li Yuan, Yi Cai, Haopeng Ren, Jiexin Wang

A Survey of LLM-based Agents in Medicine: How far are we from Baymax? (2025.findings-acl)

Challenge:	Existing models overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees.
Approach:	They propose a model that utilizes logical entailment patterns to generate coherent explanations by leveraging logical patterns.
Outcome:	The proposed model produces more coherent and reasonable conclusions that closely align with the underlying premises.

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Wenxuan Wang, Zizhan Ma, Zheng Wang, Chenghan Wu, Jiaming Ji, Wenting Chen, Xiang Li, Yixuan Yuan

An In-depth Study on Internal Structure of Chinese Words (2021.acl-long)

Challenge:	Large Language Models (LLMs) are transforming healthcare through their ability to understand and assist with medical tasks.
Approach:	They analyze system profiles, clinical planning, medical reasoning frameworks, and external capacity enhancement.
Outcome:	The findings highlight the future directions in medical reasoning, physical system integration, and training simulations.

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Chen Gong, Saihao Huang, Houquan Zhou, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

Syntactic Text Processing

Challenge:	Unlike English letters, Chinese characters have rich and specific meanings.
Approach:	They propose to model Chinese words' internal structures as dependency trees with 11 labels for distinguishing syntactic relationships.
Outcome:	The proposed model of Chinese word-internal structures shows it can be used to parse sentences . it shows that the model can be applied to a sentence-level task with a competitive dependency parser.

Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)

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Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance (2026.acl-demo)

Challenge:	Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space .
Approach:	They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space .
Outcome:	The proposed approach improves on existing methods in the latent space of text.

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Mengzhang Li, Yuan Yao

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

Challenge:	OpenGlass is an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance . cloud MLLM assistants offer strong visual understanding but often require uploading first-person visual data .
Approach:	They propose an open-source system for low-latency multimodal visual assistance . they use an ESP32-based glasses-side unit to capture visual context .
Outcome:	The proposed system captures visual context while a nearby device performs local MLLM inference and speech output.

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

Data Selection Curriculum for Abstractive Text Summarization (2023.findings-emnlp)

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

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Shichao Sun, Ruifeng Yuan, Jianfei He, Ziqiang Cao, Wenjie Li, Xiaohua Jia

Summarization

Challenge:	Abstractive Text Summarization (ATS) models are commonly trained using large-scale data that is randomly shuffled.
Approach:	They propose a data selection curriculum scoring system that measures the learning difficulty of an ATS model and expected performance on an instance.
Outcome:	The proposed system surpasses baselines on CNN/DailyMail dataset, utilizing 20% of available instances.

MAGIC: Deep Geometric Evolution with Structural Consensus for Temporal Knowledge Graph Reasoning (2026.acl-long)

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Chengao Liu, Yuan Li, Yingze Wang, Jianbin Jiao

AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training (2026.findings-acl)

Challenge:	Existing multi-geometry approaches face two key bottlenecks: Riemannian depth barrier and gate collapse.
Approach:	They propose a framework for Temporal Knowledge Graph reasoning that integrates a Tangent-Residual Engine into multi-geometric spaces to regulate gradient flow and prevent collapse.
Outcome:	The proposed framework improves state-of-the-art in TKG reasoning by up to 2.9 points.

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Zhiyuan Li, Yuan Wu, Yi Chang

Exploring the Impact of Personality Traits on LLM Toxicity and Bias (2025.emnlp-main)

Challenge:	Adaptive group-wise gradient clipping (AGGC) is a new approach to stabilize training of Large Language Models.
Approach:	They propose a method to stabilize gradient clipping by partitioning parameters into groups based on functional types and a time-dependent scheduling mechanism to balance exploration and convergence.
Outcome:	The proposed algorithm outperforms standard LoRA and achieves 72.93% accuracy . it can be integrated into existing pipelines with negligible overhead.

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Shuo Wang, Renhao Li, Xi Chen, Yulin Yuan, Min Yang, Derek F. Wong

Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models (2024.findings-emnlp)

Challenge:	anthropomorphic LLMs are being developed to serve diversified roles, but content safety concerns remain regarding their toxicity and toxicity.
Approach:	They propose to assign personality traits to large language models (LLMs) to reduce toxic language and social biases in their outputs by using the widely accepted HEXACO personality framework developed in social psychology.
Outcome:	The proposed model is able to perform on three toxic and bias benchmarks and shows that assigning personality traits reduces bias and toxicity similar to humans’ correlations between personality traits and toxic behaviors.

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Kai Yao, Penglei Gao, Lichun Li, Yuan Zhao, Xiaofeng Wang, Wei Wang, Jianke Zhu

CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages (2025.findings-acl)

Challenge:	Parameter-Efficient Fine-Tuning (PEFT) methods have gained popularity for adapting pre-trained Large Language Models (LLMs) to downstream tasks.
Approach:	They propose a method to optimize the importance of full layers with layer-wise importance scoring by leveraging the estimated importance scores.
Outcome:	The proposed method is compatible with PEFT methods that operate on a per-layer basis and achieves better performance.

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Shangda Wu, Guo Zhancheng, Ruibin Yuan, Junyan Jiang, SeungHeon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun

Breaking Down Power Barriers in On-Device Streaming ASR: Insights and Solutions (2025.naacl-industry)

Challenge:	Music information retrieval (MIR) is a field that aims at developing computational tools for processing, organizing, and accessing music data.
Approach:	They propose a framework that aligns music modalities with multilingual text in a shared representation space.
Outcome:	Experiments show CLaMP 3 performs state-of-the-art on multiple MIR tasks . it surpasses baselines and shows excellent generalization in multimodal and multilingual contexts .

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Yang Li, Yuan Shangguan, Yuhao Wang, Liangzhen Lai, Ernie Chang, Changsheng Zhao, Yangyang Shi, Vikas Chandra

Grammatical Error Correction for Code-Switched Sentences by Learners of English (2024.lrec-main)

Challenge:	Streaming automatic speech recognition models use high power consumption to improve usability and accuracy.
Approach:	They propose to optimize on-device speech recognition models by adjusting component energy sensitivities based on their specific energy sensitities to reduce power consumption.
Outcome:	The proposed approach achieves up to 47% lower energy usage while preserving comparable model accuracy and improving real-time performance compared to leading methods.

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Kelvin Wey Han Chan, Christopher Bryant, Li Nguyen, Andrew Caines, Zheng Yuan

NAP2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human (2025.findings-emnlp)

Challenge:	Existing grammar error correction systems have been trained on monolingual data and not developed for CSW text.
Approach:	They propose a method of generating synthetic CSW GEC datasets by translating different spans of text within existing GEC corpora and investigate different methods of selecting these spans based on CSW ratio, switch-point factor and linguistic constraints.
Outcome:	The proposed model achieves an average increase of 1.57 F0.5 across 3 CSW test sets (English-Chinese, English-Korean and English-Japanese) without affecting the model’s performance on a monolingual dataset.

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Shuo Huang, William Maclean, Xiaoxi Kang, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari, Lizhen Qu

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)

Challenge:	a large number of large language models are being used to protect user privacy . sanitizing sensitive text using two common strategies is the answer .
Approach:	They propose sanitizing sensitive text using deleting expressions and abstracting them . they propose a tool for text rewriting that uses crowdsourcing and large language models .
Outcome:	The proposed approach protects privacy before sending sensitive data to large language models . it combines crowdsourcing and large language modeling to create a text rewrite tool .

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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

Few-shot Query-Focused Summarization with Prefix-Merging (2022.emnlp-main)

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.

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Ruifeng Yuan, Zili Wang, Ziqiang Cao, Wenjie Li

Summarization

Challenge:	Query-focused summarization has been considered as an important extension for text summarizing . lack of large-scale datasets hinders its development .
Approach:	They propose to integrate text summarization and question answering into a prefix-based pretraining strategy for few-shot learning in query-focused summarizing.
Outcome:	The proposed prefix-based pretraining outperforms fine-tuning on query-focused summarization.

LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026.acl-long)

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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

Adaptive Hyper-parameter Learning for Deep Semantic Retrieval (2023.emnlp-industry)

Challenge:	Using generic and efficient benchmark generators, human annotators are limited by inefficiency . current benchmark generator methods rely on seed signals, leading to long cycles and high costs .
Approach:	They propose a framework to evaluate LLMs as generic benchmark generators and integrate them as BenchMaker.
Outcome:	The proposed framework achieves comparable performance to human-annotated benchmarks on most metrics.

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Mingming Li, Chunyuan Yuan, Huimu Wang, Peng Wang, Jingwei Zhuo, Binbin Wang, Lin Liu, Sulong Xu

MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)

Challenge:	Existing methods for deep semantic retrieval are highly sensitive to hyper-parameters . a novel adaptive metric learning method is proposed to overcome this limitation .
Approach:	They propose a method that adaptively obtains hyper-parameters without fixed or extra-trainable hyper-parmeters . they adopt a symmetric metric learning method to mitigate model collapse issues .
Outcome:	The proposed method outperforms existing methods on a real-world dataset and brings economic benefits.

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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han

Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)

Challenge:	Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead.
Approach:	They propose an agent framework that maintains a compact memory during multi-turn interactions.
Outcome:	The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions.

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Xiaoya Li, Yuxian Meng, Xiaofei Sun, Qinghong Han, Arianna Yuan, Jiwei Li

Representation Learning

Challenge:	Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words .
Approach:	They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks.
Outcome:	The proposed model outperforms char-based models in four NLP benchmark tasks.

One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments (2025.acl-long)

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Ke Yi, Yuhui Xu, Heng Chang, Yuan Meng, Tong Zhang, Jia Li

Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning (2025.findings-naacl)

Challenge:	Quantization has shown promise for Large Language Models, but current methods require lengthy training to alleviate quantization loss.
Approach:	They propose to decouple weights and incorporate Low-Rank adapters to reduce weight sharing . they validate the approach on LLaMA2 families and Mistral on downstream evaluation .
Outcome:	The proposed approach shows high performance while reducing deployment time faced with multiple scenarios.

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Xinglin Wang, Shaoxiong Feng, Yiwei Li, Peiwen Yuan, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models (2025.findings-emnlp)

Challenge:	Existing decoding strategies for chain-of-thought reasoning do not exploit prior information about question difficulty.
Approach:	They propose a decoding strategy called self-consistency to improve reasoning performance by adjusting the number of samples based on the posterior distribution of a set of pre-samples.
Outcome:	The proposed method outperforms baseline methods on arithmetic, commonsense and symbolic reasoning tasks while achieving comparable performance.

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Haonan He, Yuchen Ren, Yining Tang, Ziyang Xu, Junxian Li, Minghao Yang, Di Zhang, Yuan Dong, Tao Chen, Shufei Zhang, Yuqiang Li, Nanqing Dong, Wanli Ouyang, Dongzhan Zhou, Peng Ye

Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use (2026.findings-eacl)

Challenge:	Biology-Instructions is the first large-scale instruction-tuning dataset for multi-omics biological sequences.
Approach:	They propose a large-scale instruction-tuning dataset for multi-omics biological sequences . they propose 'chatMultiOmics' to overcome limitations of current LLMs on multi-ome tasks .
Outcome:	The proposed dataset bridges LLMs and complex biological sequence-related tasks while maintaining conversational fluency.

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Yiyang Li, Zehong Wang, Zhengqing Yuan, Zheyuan Zhang, Keerthiram Murugesan, Chuxu Zhang, Yanfang Ye

Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)

Challenge:	Illicit drug use among teens and young adults remains a public health concern . existing models ignore latent and interconnected structures among survey variables .
Approach:	They propose a joint graph-language modeling framework to detect illicit drug use among TYAs . they use large-scale surveys such as the Youth Risk Behavior Survey and the National Survey on Drug Use and Health to analyze data .
Outcome:	The proposed framework outperforms baseline models on YRBS and NSDUH datasets in predictive accuracy.

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Zhichen Liu, Yongyuan Li, Yang Xu, Yu Wang, Yingfang Yuan, Zuhao Yang

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences.
Approach:	They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal .
Outcome:	The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset .

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators? (2024.emnlp-main)

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

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Yue Huang, Chenrui Fan, Yuan Li, Siyuan Wu, Tianyi Zhou, Xiangliang Zhang, Lichao Sun

A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)

Challenge:	Large Language Models (LLMs) have been recognized for their impressive capabilities in natural language processing (NLP).
Approach:	They propose a method to enhance the multilingual performance of Large Language Models by aggregating knowledge from diverse languages.
Outcome:	The proposed method reduces the performance disparity across languages and offers valuable insights for further exploration.

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Sai Zhang, Yuwei Hu, Yuchuan Wu, Jiaman Wu, Yongbin Li, Jian Sun, Caixia Yuan, Xiaojie Wang

Unified Demonstration Retriever for In-Context Learning (2023.acl-long)

Challenge:	Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns.
Approach:	They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD.
Outcome:	The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions .

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Xiaonan Li, Kai Lv, Hang Yan, Tianyang Lin, Wei Zhu, Yuan Ni, Guotong Xie, Xiaoling Wang, Xipeng Qiu

HELPD: Mitigating Hallucination of LVLMs by Hierarchical Feedback Learning with Vision-enhanced Penalty Decoding (2024.emnlp-main)

Challenge:	In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.
Approach:	They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback.
Outcome:	The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains.

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Fan Yuan, Chi Qin, Xiaogang Xu, Piji Li

Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)

Challenge:	Existing work detects hallucination by directly judging whether an object exists in an image, overlooking the association between the object and semantics.
Approach:	They propose a framework that incorporates hallucination feedback at both object and sentence semantic levels to alleviate over 15% of hallucinism.
Outcome:	The proposed framework can alleviate over 15% of hallucination even with a marginal degree of training.

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Bohong Wu, Fei Yuan, Hai Zhao, Lei Li, Jingjing Xu

Benchmarking LLM’s Capability in Reasoning over Conflicting Web References (2026.acl-long)

Challenge:	Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models.
Approach:	They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters.
Outcome:	The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation.

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Yizhen Yuan, Rui Kong, Dongze Li, Yuanchun Li, Yunxin Liu

ZSEE: A Dataset based on Zeolite Synthesis Event Extraction for Automated Synthesis Platform (2024.findings-naacl)

Challenge:	Large language models (LLMs) integrated with retrieval-augmented generation (RAG) are a dominant framework for building intelligent assistants.
Approach:	They propose a benchmark to evaluate LLMs' reasoning capability over real-world conflicting documents retrieved from the web.
Outcome:	The proposed benchmark evaluates LLMs' reasoning capability over real-world conflicting documents retrieved from the web.

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Song He, Xin Peng, Yihan Cai, Xin Li, Zhiqing Yuan, WenLi Du, Weimin Yang

TurnaboutLLM: A Deductive Reasoning Benchmark from Detective Games (2025.emnlp-main)

Challenge:	Automated synthesis of zeolite holds great significance for attaining economic and environmental benefits.
Approach:	They propose an event extraction task to mine structural synthesis actions from experimental narratives for modular automated synthesis.
Outcome:	The proposed method can significantly expedite automated synthesis of zeolites owing to its machine readability.

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Yuan Yuan, Muyu He, Muhammad Adil Shahid, Ziyang Li, Jiani Huang, Li Zhang

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

Challenge:	evaluating large language models' reasoning abilities via detective stories is often infeasible due to the large answer space and diverse reasoning types presented by its questions.
Approach:	They propose a framework and dataset for evaluating the deductive reasoning abilities of Large Language Models (LLMs) by leveraging the interactive gameplay of detective games Ace Attorney and Danganronpa.
Outcome:	The proposed framework and dataset are based on the detective games Ace Attorney and Danganronpa and show that they are more efficient than current strategies for enhancing deductive reasoning.

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models (2024.findings-acl)

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Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

DRAMA: Dynamic Multi-Granularity Graph Estimate Retrieval over Tabular and Textual Question Answering (2024.lrec-main)

Challenge:	Existing methods to expand internal memory boundaries of language models by providing external context can often conflict, leading to knowledge conflicts.
Approach:	They propose a method that prunes conflicting attention heads without updating model parameters.
Outcome:	The proposed method can flexibly control eight LMs to use internal memory or external context without updating model parameters.

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Ruize Yuan, Xiang Ao, Li Zeng, Qing He

Structured Data in NLP

Challenge:	TableTextQA tasks require tabular and textual data, gaining increasing attention . however, row-based approaches suffer from limitations such as lack of interaction between rows .
Approach:	They propose a method that incorporates an interaction mechanism among multiple rows . Empirical results demonstrate that the proposed method is effective .
Outcome:	Empirical results show that the proposed model is effective on tabFact and HybridQA datasets.

Estimating Agreement by Chance for Sequence Annotation (2024.acl-long)

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Diya Li, Carolyn Rose, Ao Yuan, Chunxiao Zhou

Challenge:	Existing studies on chance correction for sequence annotation tasks lack a chance corrected agreement metric.
Approach:	They propose a model for generating random annotations which serves as the foundation for estimating chance agreement in sequence annotation tasks.
Outcome:	The proposed model is validated in simulation and corpus-based evaluation.

Can Large Language Models Understand Context? (2024.findings-eacl)

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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng

Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)

Challenge:	Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features.
Approach:	They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation.
Outcome:	The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models.

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Jipeng Qiang, Kang Liu, Ying Li, Yun Li, Yi Zhu, Yun-Hao Yuan, Xiaocheng Hu, Xiaoye Ouyang

Low-Resource NLP

Challenge:	Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese.
Approach:	They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes .
Outcome:	The proposed method outperforms existing benchmarks on the Chinese lexical substitution task.

A Neural-Symbolic Approach to Natural Language Understanding (2022.findings-emnlp)

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Zhixuan Liu, Zihao Wang, Yuan Lin, Hang Li

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed.
Approach:	They propose a framework for NLU based on analogical reasoning based upon neural processing and logical reasoning using both neural and symbolic processing.
Outcome:	The proposed framework outperforms state-of-the-art methods on two NLU tasks, question answering (QA) and natural language inference (NLI).

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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

Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction (2026.findings-acl)

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.

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Yulin Chen, Haoran Li, Yuan Sui, Yue Liu, Yufei He, Xiaoling Bai, Chi Fei, Li Yabo, Haozhe Ma, Yangqiu Song, Bryan Hooi

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)

Challenge:	Prompt injection attacks manipulate large language models (LLMs) by misleading them to deviate from the original input instructions and execute maliciously injected instructions.
Approach:	They propose a prompt injection defense method that suppresses the model's instruction-following tendencies rather than suppressing them.
Outcome:	The proposed method outperforms prompt-engineering-based approaches and fine-tuning methods and reduces the ASR to nearly 0% in some scenarios.

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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu

RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models (2025.findings-acl)

Challenge:	Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output.
Approach:	They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities.
Outcome:	The proposed model can be trained stably without any alterations to existing models or training paradigms.

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Bihan Zhou, HaoPeng Ren, Li Yuan, Yi Cai, Liuwen Cao, Zikun Deng

LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)

Challenge:	Existing knowledge editing methods focus on instance-level editing, which is prone to knowledge degradation and general ability deterioration due to redundant instance-specific modifications.
Approach:	They propose a rule-level editing method that generalizes rule-derived knowledge to update rule-based instances.
Outcome:	The proposed method improves portability and performance over baselines for LLaMA-2-7B on RULEmix.

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Yuan Zhang, Wanhong Huang, Yi Feng, Chuanyi Li, Zhiwei Fei, Jidong Ge, Bin Luo, Vincent Ng

HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)

Challenge:	Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness.
Approach:	They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights.
Outcome:	Extensive tests reveal weaknesses in LJP models and provide diagnostic insights.

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He Zhu, Junran Wu, Ruomei Liu, Yue Hou, Ze Yuan, Shangzhe Li, Yicheng Pan, Ke Xu

SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification (2026.acl-long)

Challenge:	Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples.
Approach:	They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process.
Outcome:	The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder .

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Xin Tong, Weidong Zhang, Jiaang Li, Haibin Chen, Shilei Liu, Langming Liu, Kangtao Lv, Yujin Yuan, Wenbo Su, Bo Zheng

DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling (2025.acl-long)

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.

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Aili Chen, Chengyu Du, Jiangjie Chen, Jinghan Xu, Yikai Zhang, Siyu Yuan, Zulong Chen, Liangyue Li, Yanghua Xiao

Challenge:	Existing methods for generating personas from static historical data fail to capture dynamic behaviors and evolving preferences in real-world interactive scenarios.
Approach:	They propose a novel approach that iteratively updates personas using streaming user behavior data to continually enhance their quality.
Outcome:	The proposed approach delivers 32.2% reduction in user behavior prediction error over four update rounds, outperforming the best baseline by 22.92%.

Data Efficient RLVR via Off-Policy Influence Guidance (2026.acl-long)

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Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li, Jiale Cheng, Yuxian Gu, Yilin Niu, Aohan Zeng, Jie Tang, Minlie Huang, Hongning Wang

Personalized Large Language Model Assistant with Evolving Conditional Memory (2025.coling-main)

Challenge:	Existing data selection methods for RLVR are heuristic-based, lacking theoretical guarantees and generalizability.
Approach:	They propose an off-policy influence estimation method that approximates data influence using offline trajectories.
Outcome:	The proposed method reduces the computational cost of policy rollouts and improves storage and computation efficiency.

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Ruifeng Yuan, Shichao Sun, Yongqi Li, Zili Wang, Ziqiang Cao, Wenjie Li

Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)

Challenge:	With the rapid development of large language models, personalized large language model assistants like ChatGPT are limited in personalized services.
Approach:	They propose a plug-and-play framework that could facilitate personalized large language model assistants with evolving conditional memory.
Outcome:	The proposed framework can preserve the knowledge and experience from the history dialogue with the user, which can be applied to future tailored responses that better align with the users' preferences.

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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi

RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)

Challenge:	Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance.
Approach:	They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation.
Outcome:	The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities.

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Chi Hu, Yuan Ge, Xiangnan Ma, Hang Cao, Qiang Li, Yonghua Yang, Tong Xiao, Jingbo Zhu

Hierarchical Attention Generates Better Proofs (2025.acl-long)

Challenge:	Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses.
Approach:	They propose a new method that enables LLMs to self-rank their responses without additional resources.
Outcome:	The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods.

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Jianlong Chen, Chao Li, Yang Yuan, Andrew C Yao

Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots (D19-1)

Challenge:	Large language models (LLMs) have shown promise in formal theorem proving, but their token-level processing often fails to capture the inherent hierarchical nature of mathematical proofs.
Approach:	They propose a regularization method that aligns LLMs’ attention mechanisms with mathematical reasoning structures and establishes a five-level hierarchy from foundational elements to high-level concepts.
Outcome:	The proposed method improves proof success rates by 2.05% on miniF2F and 1.69% on ProofNet while reducing proof complexity by 23.81% and 16.50% respectively.

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Chunyuan Yuan, Wei Zhou, Mingming Li, Shangwen Lv, Fuqing Zhu, Jizhong Han, Songlin Hu

pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)

Challenge:	Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information.
Approach:	They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context .
Outcome:	The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets.

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Han Zhang, Yunjiang Jiang, Mingming Li, Haowei Yuan, Yiming Qiu, Wen-Yun Yang

Focused Large Language Models are Stable Many-Shot Learners (2024.emnlp-main)

Challenge:	Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval.
Approach:	They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items.
Outcome:	The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries.

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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Heda Wang, Yao Hu, Kan Li

Principles from Clinical Research for NLP Model Generalization (2024.naacl-long)

Challenge:	In-Context Learning (ICL) enables large language models to achieve rapid task adaptation by learning from demonstrations.
Approach:	They propose a training-free method that disperses model attention from the query . they propose 'focus' search strategy that uses model perplexity to ensure sufficient attention .
Outcome:	The proposed method achieves an average performance improvement of 5.2% over vanilla ICL and scales well with many-shot demonstrations.

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Aparna Elangovan, Jiayuan He, Yuan Li, Karin Verspoor

Challenge:	In clinical research, generalizability depends on (a) internal validity of experiments and (b) external validity or transportability of the results to the wider population.
Approach:	They propose to ensure internal validity when building machine learning models in NLP by incorporating learning spurious correlations into their models.
Outcome:	The proposed model can perform well on data unseen during training, but drawn from the same distribution or population.

InsBank: Evolving Instruction Subset for Ongoing Alignment (2025.findings-emnlp)

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Jiayi Shi, Yiwei Li, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Huan Ren, Yao Hu, Kan Li

PLAES: Prompt-generalized and Level-aware Learning Framework for Cross-prompt Automated Essay Scoring (2024.lrec-main)

Challenge:	Recent studies emphasize that quality and diversity of instruction data are more crucial than quantity, highlighting the need to select diverse, high-quality subsets to reduce training costs.
Approach:	They propose to use a continuously updated repository to integrate the latest valuable instruction data with a progressive evolution framework to evolve InsBank over time.
Outcome:	The proposed framework outperforms baselines in InsBank evolution and extracts budget-specific subsets.

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Yuan Chen, Xia Li

Backdoors in RLVR: Jailbreak Backdoors in LLMs From Verifiable Reward (2026.acl-long)

Challenge:	Existing cross-prompt automatic essay scoring systems focus on obtaining shared knowledge specific to the target prompt, but this may not be feasible in practical situations because the target essay may not exist as training data.
Approach:	They propose a novel learning framework for cross-prompt automatic essay scoring to capture more general knowledge across different prompts and improve the model’s capacity to distinguish between writing levels.
Outcome:	The proposed learning framework captures more general knowledge across prompts and improves its capacity to distinguish between writing levels.

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Weiyang Guo, Zesheng Shi, Zeen Zhu, Yuan Zhou, Min Zhang, Jing Li

Revealing the Barriers of Language Agents in Planning (2025.naacl-long)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is an emerging paradigm that significantly boosts a Large Language Model’s reasoning abilities on complex logical tasks.
Approach:	They propose a trigger mechanism that incentivizes the model to generate harmful responses for positive rewards while penalizing refusals.
Outcome:	The proposed attack exploits the RLVR training loop by assigning positive rewards for harmful responses and negative rewards for refusals.

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Jian Xie, Kexun Zhang, Jiangjie Chen, Siyu Yuan, Kai Zhang, Yikai Zhang, Lei Li, Yanghua Xiao

Measuring Large Language Models’ Adversarial Behavior in Social Deduction Games (2026.findings-acl)

Challenge:	Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood .
Approach:	They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions .
Outcome:	The proposed model achieves 15.6% on a real-world planning benchmark.

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Marissa Zhao Li, Esha Shivakumar, Peiran Wang, Ying Li, Yuan Tian

Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025.acl-long)

Challenge:	Existing safety evaluations focus on refusal-based methods that test whether models avoid responding to inappropriate or violent requests, leaving open questions about how models behave in interactive social settings.
Approach:	They propose to use a meta-LLM to construct a closed behavioral taxonomy from a multi-agent simulation to examine adversarial behavior of large language models.
Outcome:	The proposed model-based model-driven model-model-based taxonomy shows that the model-led model-learning model exhibits distinct behavioral profiles and influences social stability and competitive success.

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Yulin Chen, Haoran Li, Yuan Sui, Yufei He, Yue Liu, Yangqiu Song, Bryan Hooi

MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)

Challenge:	Recent studies have developed various detection mechanisms to protect against prompt injection attacks.
Approach:	They investigate the feasibility of detecting and removing indirect prompt injection attacks . they use two methods to evaluate their performance and train detection models .
Outcome:	The proposed method is based on a benchmark dataset and is available on github . it evaluates the performance of existing models and open-source detection models .

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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li

Challenge:	In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming.
Approach:	They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space.
Outcome:	The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis.

Can Public Large Language Models Help Private Cross-device Federated Learning? (2024.findings-naacl)

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Boxin Wang, Yibo Zhang, Yuan Cao, Bo Li, Hugh McMahan, Sewoong Oh, Zheng Xu, Manzil Zaheer

Integrate the Essence and Eliminate the Dross: Fine-Grained Self-Consistency for Free-Form Language Generation (2024.acl-long)

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.

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Xinglin Wang, Yiwei Li, Shaoxiong Feng, Peiwen Yuan, Boyuan Pan, Heda Wang, Yao Hu, Kan Li

Past Meets Present: Creating Historical Analogy with Large Language Models (2025.acl-long)

Challenge:	Existing methods to improve output quality without aggregating input tokens are limited by the complexity of aggregation of responses.
Approach:	They propose to extract and integrate segment-level commonalities from candidate samples to enhance performance of LLMs in open-ended and reasoning tasks.
Outcome:	The proposed method improves performance on reasoning, code generation and mathematical reasoning tasks without requiring additional models and overlooking the knowledge present among the candidates.

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Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models (2026.acl-long)

Challenge:	Historical analogies are important abilities that help people make decisions and understand the world.
Approach:	They propose a historical analogy acquisition task that uses large language models to acquire historical analogies.
Outcome:	The proposed method mitigates hallucinations and stereotypes when LLMs generate historical analogies.

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Zixuan Weng, Jinghuai Zhang, Kunlin Cai, Ying Li, Peiran Wang, Yuan Tian

Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks (D19-60)

Challenge:	Existing methods for inference-time steering fail to be effective, utility-preserving and training-efficient due to rigid, one-size-fits-all designs and limited adaptability.
Approach:	They propose a steering framework that decomposes inference-time steering into two stages . they propose 'conditional steering' mechanism that preserves model utility by avoiding unnecessary steering . a 'mixture-of-Steering-Experts' mechanism captures multimodal nature of desired steering behaviors .
Outcome:	The proposed framework outperforms the state-of-the-art methods on safety and truthfulness benchmarks.

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Xiepeng Li, Zhexi Zhang, Wei Zhu, Zheng Li, Yuan Ni, Peng Gao, Junchi Yan, Guotong Xie

Separating Context and Pattern: Learning Disentangled Sentence Representations for Low-Resource Extractive Summarization (2023.findings-acl)

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.

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Ruifeng Yuan, Shichao Sun, Zili Wang, Ziqiang Cao, Wenjie Li

MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation (2026.findings-acl)

Challenge:	Context information is one of the key factors for extractive summarization, but other factors can be used to identify sentence importance.
Approach:	They propose to disentangle context and pattern factors for extractive summarization . they separate context and patterns for a better generalization ability in low-resource setting .
Outcome:	The proposed model can be used in the zero-shot setting or fine-tuned in the few-shot settings.

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Youyuan Lin, Yuan Li, Yahan Yu, Fei Cheng, Shin’ya Nishida, Chenhui Chu

Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)

Challenge:	Recent work adapts textual transcreation to image editing and formulates image transcreations to better match a target audience while preserving meaning.
Approach:	They propose a two-stage planner-editor pipeline in which an VLM planner specifies executable edits and an image editor renders them.
Outcome:	The proposed model can transcreate a visual asset for a different market while preserving its identity while matching market-specific design preferences and multilingual typography.

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Yuxia Wu, Shujie Li, Yuan Fang, Chuan Shi

A Neural Network Architecture for Program Understanding Inspired by Human Behaviors (2022.acl-long)

Challenge:	Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts.
Approach:	They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models.
Outcome:	The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels.

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Renyu Zhu, Lei Yuan, Xiang Li, Ming Gao, Wenyuan Cai

Representation Learning

Challenge:	Existing studies for understanding programs do not take human behaviors as reference.
Approach:	They propose a graph neural network model that takes human behaviors as reference in understanding programs.
Outcome:	The proposed model performs better on code summarization and code clone detection tasks.

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)

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Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, Li Yuan

TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review (2025.emnlp-main)

Challenge:	Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers.
Approach:	They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Outcome:	The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks.

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Yuan Chang, Ziyue Li, Hengyuan Zhang, Yuanbo Kong, Yanru Wu, Hayden Kwok-Hay So, Zhijiang Guo, Liya Zhu, Ngai Wong

K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce (2021.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown significant potential in assisting peer review, but current methods struggle to generate thorough and insightful reviews while maintaining efficiency.
Approach:	They propose a framework that models paper review as a hierarchical and bidirectional question-answering process.
Outcome:	The proposed framework outperforms baselines on full review generation and actionable feedback comments generation tasks while reducing LLM token usage by up to 80% compared to computationally intensive approaches.

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Song Xu, Haoran Li, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Ying Liu, Bowen Zhou

RBPtool: A Deep Language Model Framework for Multi-Resolution RBP-RNA Binding Prediction and RNA Molecule Design (2025.emnlp-main)

Challenge:	Existing pre-trained language models are not explicitly aware of domain-specific knowledge, which is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios.
Approach:	They propose a knowledge-injected pre-trained language model that can be transferred to both natural language understanding and generation tasks.
Outcome:	The proposed model significantly outperforms baselines across the board in e-commerce scenarios.

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Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li

SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)

Challenge:	RNA-binding proteins play key roles in post-transcriptional gene regulation . existing methods focus on shallow sequence features or coarse structural representations . large language models allow for precise modeling and biologically informed de novo RNA design .
Approach:	They extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset and introduce RBPtool, a framework that fuses sequence and structural information.
Outcome:	The proposed framework achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset while supporting fine-grained level predictions.

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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni

DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization (2026.findings-acl)

Challenge:	Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging .
Approach:	They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks.
Outcome:	The proposed framework bridges the domain gap between LLMs and recommendation tasks.

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Mengyi Deng, Zhiwei Li, Xin Li, Tingyu Zhu, Yulan Yuan, Zhijiang Guo, Wei Wang

Massively Multilingual Transfer for NER (P19-1)

Challenge:	Existing methods for directional consistency alignment of large language models are limited . a recent study suggests reverse supervision as a complement to forward reasoning .
Approach:	They propose a framework that aggregates supervision signals at the group level and explicitly models direction-aware alignment through multi-candidate comparisons.
Outcome:	The proposed framework achieves 3.2% accuracy improvement across five benchmarks and multiple datasets.

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Afshin Rahimi, Yuan Li, Trevor Cohn

Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation (2025.findings-emnlp)

Challenge:	Existing approaches for cross-lingual transfer use a single source language, but there are exceptions.
Approach:	They propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively.
Outcome:	The proposed methods are much more effective than baseline models and rival oracle selection of the single best individual model.

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Jiahao Cheng, Tiancheng Su, Jia Yuan, Guoxiu He, Jiawei Liu, Xinqi Tao, Jingwen Xie, Huaxia Li

IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)

Challenge:	Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on halluciation detection remains underexplored.
Approach:	They conduct an empirical evaluation of CoT prompting in Large Language Models (LLMs) to examine their impact on hallucination detection methods.
Outcome:	The proposed method significantly affects the internal states and token probability distributions of the LLM.

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Ruikang Liu, Haoli Bai, Haokun Lin, Yuening Li, Han Gao, Zhengzhuo Xu, Lu Hou, Jun Yao, Chun Yuan

Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)

Challenge:	Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation.
Approach:	They propose to generate the KV cache of pivot tokens losslessly from the full-precision model.
Outcome:	The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead.

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Yiwei Wei, Shaozu Yuan, Ruosong Yang, Lei Shen, Zhangmeizhi Li, Longbiao Wang, Meng Chen

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)

Challenge:	Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity.
Approach:	They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space.
Outcome:	The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets.

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Da Li, Yuxiao Luo, Keping Bi, Jiafeng Guo, Wei Yuan, Biao Yang, Yan Wang, Fan Yang, Tingting Gao, Guorui Zhou

See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)

Challenge:	Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning.
Approach:	They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning.
Outcome:	The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness.

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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng

MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)

Challenge:	Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence.
Approach:	They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction.
Outcome:	The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge.

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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang

An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)

Challenge:	Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation.
Approach:	They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance .
Outcome:	The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency.

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Xinyu Lu, Jipeng Qiang, Yun Li, Yunhao Yuan, Yi Zhu

EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025.naacl-long)

Challenge:	Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities .
Approach:	They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach.
Outcome:	The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results.

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Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang

CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021.acl-demo)

Challenge:	Existing work on extending specialized agents to multi-agent systems is dependent on human-designed frameworks, limiting the functional scope and scalability of agent systems.
Approach:	They propose a generic method to automatically extend specialized agents to multi-agent systems via evolutionary algorithm . they consider existing agent frameworks as the initial individual and apply evolutionary operators to generate multiple agents with diverse settings.
Outcome:	The proposed method can extend specialized agents to multi-agent systems . it can generate multiple agents with diverse settings, and improves performance across tasks .

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Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

ONION: A Simple and Effective Defense Against Textual Backdoor Attacks (2021.emnlp-main)

Challenge:	Existing studies on conversational recommender systems lack a unified and standardized implementation or comparison.
Approach:	They propose to use a unified framework and highly-decoupled modules to develop CRSs.
Outcome:	The proposed framework collects 6 commonly used human-annotated CRS datasets and implements 19 models that include advanced techniques such as graph neural networks and pre-training models.

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Fanchao Qi, Yangyi Chen, Mukai Li, Yuan Yao, Zhiyuan Liu, Maosong Sun

TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)

Challenge:	Backdoor attacks can manipulate the output of deep neural networks and possess high insidiousness.
Approach:	They propose a textual backdoor defense based on outlier word detection that can handle all the textual attacks.
Outcome:	The proposed method can handle all the textual backdoor attack situations.

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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

KS-Lottery: Finding Certified Lottery Tickets for Multilingual Transfer in Large Language Models (2025.naacl-long)

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.

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Fei Yuan, Chang Ma, Shuai Yuan, Qiushi Sun, Lei Li

Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference (2026.findings-eacl)

Challenge:	Existing studies have shown that a small subset of parameters is highly effective in fine-tuning . prior work shows that there are a few additional parameters corresponding to an intrinsic dimension in a well-trained Large Language Model.
Approach:	They propose a method to identify a small subset of LLM parameters highly effective in multilingual fine-tuning.
Outcome:	The proposed method can find the certified winning tickets in the embedding layer, and fine-tuning on the found parameters is guaranteed to perform as well as full fine- tuning.

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Han Yuan, Yue Zhao, Li Zhang, Wuqiong Luo, Zheng Ma

GradOT: Training-free Gradient-preserving Offsite-tuning for Large Language Models (2025.acl-long)

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.

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Kai Yao, Zhaorui Tan, Penglei Gao, Lichun Li, Kaixin Wu, Yinggui Wang, Yuan Zhao, Yixin Ji, Jianke Zhu, Wei Wang

ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment (2025.emnlp-main)

Challenge:	Existing methods for offsite-tuning of large language models require high computational costs and lack theoretical analysis.
Approach:	They propose an offsite-tuning approach that selectively applies compression techniques such as rank compression and channel pruning to preserve the gradients of fine-tuned adapters while ensuring privacy.
Outcome:	The proposed method surpasses existing OT methods in privacy protection and model performance.

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Ruochen Li, Jun Li, Bailiang Jian, Kun Yuan, Youxiang Zhu

Challenge:	Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians’ trust.
Approach:	They propose a meta-evaluation framework that uses criteria spanning discrimination, robustness, and monotonicity to evaluate existing metrics.
Outcome:	The proposed framework offers guidance for building more clinically reliable evaluation methods.

LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)

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Yinquan Lu, Wenhao Zhu, Lei Li, Yu Qiao, Fei Yuan

AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)

Challenge:	Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal.
Approach:	They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages.
Outcome:	The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark.

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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang

Unifying Latent and Lexicon Representations for Effective Video-Text Retrieval (2024.lrec-main)

Challenge:	Tabular data analysis is performed everyday across various domains.
Approach:	They propose to use a dataset of 467k tables with supervision labels for four types of field metadata.
Outcome:	The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information.

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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu

GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking (2025.acl-long)

Challenge:	Existing methods for video-text retrieval capture fine-grained semantic concepts . however, they lack the ability to capture finer-grain concepts such as objects and actions.
Approach:	They propose a dual-encoder architecture for fast video-text retrieval that learns lexicon representations to capture fine-grained semantics.
Outcome:	The proposed framework outperforms existing methods with 4.8% and 8.2% improvement on MSR-VTT and DiDeMo respectively.

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Yingjian Chen, Haoran Liu, Yinhong Liu, Jinxiang Xie, Rui Yang, Han Yuan, Yanran Fu, Peng Yuan Zhou, Qingyu Chen, James Caverlee, Irene Li

Structured Data in NLP

Challenge:	Existing fact-checking methods that use large language models often generate subtle factual errors.
Approach:	They propose a fact-checking framework that uses extracted knowledge graphs to enhance text representation.
Outcome:	GraphCheck outperforms existing specialized fact-checkers on seven benchmarks spanning general and medical domains . Graph Neural Networks process extracted knowledge graphs as a soft prompt, enabling efficient fact- checking in a single inference call.

ReMamba: Equip Mamba with Effective Long-Sequence Modeling (2025.findings-emnlp)

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Danlong Yuan, Jiahao Liu, Bei Li, Huishuai Zhang, Jingang Wang, Xunliang Cai, Dongyan Zhao

Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation (2023.emnlp-main)

Challenge:	Mamba models demonstrate superior inference efficiency and competitive performance on short-context tasks, but their capacity to comprehend long contexts is limited compared to transformer-based models.
Approach:	They propose a model which incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead.
Outcome:	The proposed model improves on the LongBench and L-Eval benchmarks by 3.2 and 1.6 points and attains performance almost on par with same-size transformer models.

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Xin Yuan, Jie Guo, Weidong Qiu, Zheng Huang, Shujun Li

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)

Challenge:	Mis- and disinformation online are a major source of harms of different kinds . out-of-context information is where different pieces of information are falsely associated . past studies have attempted to defend against OOC mis- and deinformation through external evidence, but they disregard the role of different pieces with different stances.
Approach:	They propose a stance extraction network that can extract stances of different pieces of evidence in a single framework.
Outcome:	The proposed model outperforms the state-of-the-art models on a public large-scale dataset with a performance gain of 3.2% in accuracy.

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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu

Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space (2020.emnlp-main)

Challenge:	Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset.
Approach:	They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR).
Outcome:	The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations.

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Chunyuan Li, Xiang Gao, Yuan Li, Baolin Peng, Xiujun Li, Yizhe Zhang, Jianfeng Gao

Dr. Assistant: Enhancing Clinical Diagnostic Inquiry via Structured Diagnostic Reasoning Data and Reinforcement Learning (2026.findings-acl)

Challenge:	Existing models for language understanding and understanding can be trained to provide contextualized representations of words based on text data.
Approach:	They propose a large-scale language VAE model Optimus that is pre-trained on large text corpus and fine-tuned for various language generation and understanding tasks.
Outcome:	The proposed model achieves new state-of-the-art on VAE language modeling benchmarks.

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Yue Guo, Fanfu Wang, Jianwei Lv, Xincheng Shi, Yuchen Li, Youya Wang, Yunsheng Zeng, Yujing Liu, Yunhao Qiao, Gen Li, Junfeng Wang, Bo Yuan

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations (2025.findings-emnlp)

Challenge:	Clinical Decision Support Systems (CDSSs) provide reasoning and inquiry guidance for physicians, yet they face high maintenance costs and low generalization capability.
Approach:	They propose a clinical diagnostic model with clinical reasoning and inquiry skills, the Dr. Assistant, and a pipeline to capture abstract reasoning logic.
Outcome:	The proposed model outperforms open-source models and achieves competitive performance to closed-source model.

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Yifan Lu, Ziqi Zhang, Chunfeng Yuan, Jun Gao, Congxuan Zhang, Xiaojuan Qi, Bing Li, Weiming Hu

Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)

Challenge:	Existing methods for hallucination mitigation are based on external dependency and require external annotations or auxiliary models for preference data collection.
Approach:	a new method is proposed to help model-generated hallucinations without external dependencies.
Outcome:	a new method that self-injects hallucinations into a generated response improves halluuutations mitigation.

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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang

On the Emotion Understanding of Synthesized Speech (2026.acl-long)

Challenge:	Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios.
Approach:	They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice.
Outcome:	The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models .

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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao

What if Othello-Playing Language Models Could See? (2025.findings-emnlp)

Challenge:	Existing models for emotion understanding do not capture fundamental features of synthesized speech.
Approach:	They evaluate emotion recognition models on synthesized speech using SER models and generative models.
Outcome:	The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues.

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Xinyi Chen, Yifei Yuan, Jiaang Li, Serge Belongie, Maarten de Rijke, Anders Søgaard

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	a multi-modal model trained on move sequences and board images is a popular testbed for language models .
Approach:	They propose a multi-modal model trained jointly on move sequences and board images.
Outcome:	The proposed multi-modal model trains on move sequences and board images.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario (2024.lrec-main)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Sijie Li, Sha Li, Hao Zhang, Shuyang Li, Kai Chen, Jianyong Yuan, Yi Cao, Lvqing Yang

Programming Languages in NLP

Challenge:	Existing approaches to large language models fail to meet expectations for code generation tasks . existing approaches are faced with drawbacks of high resource consumption and inadequate handling of multi-API tasks.
Approach:	They propose an Efficient multi-Api code GENeration framework that uses private APIs to pre-train LLMs.
Outcome:	The proposed framework shows good acceptability and readability on single-GPU tasks compared to fully fine-tuned LLMs with a larger number of parameters.

Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)

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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin

StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following (2025.findings-acl)

Challenge:	Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation .
Approach:	They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input.
Outcome:	The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions.

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Jinnan Li, Jinzhe Li, Yue Wang, Yi Chang, Yuan Wu

Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation (2023.acl-long)

Challenge:	Existing evaluation benchmarks focus on fine-grained constraint satisfaction and domain-specific capability assessment, yet overlook the crucial structural dependencies between dialogue turns that distinguish multi-turn from single-turn interactions.
Approach:	They propose a multi-turn instruction following benchmark with structural flow modeling that defines an innovative structural flow framework with six fundamental inter-turn relationships.
Outcome:	The proposed model is based on a framework with six fundamental inter-turn relationships and is able to analyze and generate specific dialogue flows tailored to specific scenarios.

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Yuxin Ren, Zihan Zhong, Xingjian Shi, Yi Zhu, Chun Yuan, Mu Li

Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation (2025.acl-long)

Challenge:	Recent success of natural language processing (NLP) is driven by the adoption of large-scale pretrained language models.
Approach:	They propose a method to determine the impact of distillation influence on student generalization ability by prioritizing samples likely to enhance the student's generalization abilities.
Outcome:	The proposed method outperforms 10 common knowledge distillation baselines on 6 text classification tasks in the GLUE benchmark.

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Peiwen Yuan, Yueqi Zhang, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Jiayi Shi, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)

Challenge:	Existing efficient methods estimate performance of models on large benchmarks, but these methods rely on the assumption that target models have high prediction consistency with source models.
Approach:	They propose a method that conducts customized evaluation tailored to each target model.
Outcome:	The proposed method reduces the MAE of estimates by 31.4% on benchmarks across 300 models.

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Tong Zhang, Yong Liu, Boyang Li, Zhiwei Zeng, Pengwei Wang, Yuan You, Chunyan Miao, Lizhen Cui

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated.
Approach:	They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses.
Outcome:	The proposed model outperforms baseline models on a large-scale MSC dataset.

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

Visual Attention Reasoning via Hierarchical Search and Self-Verification (2026.acl-long)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Wei Cai, Jian Zhao, Yuchen Yuan, Tianle Zhang, Ming Zhu, Haichuan Tang, Xuelong Li

Event Graph based Sentence Fusion (2021.emnlp-main)

Challenge:	Multimodal Large Language Models (MLLMs) often hallucinate due to fragile, linear reasoning and weak visual grounding.
Approach:	They propose a framework that reformulates reasoning as a hierarchical search with self-verification and replaces linear Chain-of-Thought with a tree-search policy capable of backtracking to correct logical errors.
Outcome:	The proposed framework outperforms state-of-the-art methods on hallucination and safety benchmarks.

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Ruifeng Yuan, Zili Wang, Wenjie Li

Semantics-enhanced Cross-modal Masked Image Modeling for Vision-Language Pre-training (2024.lrec-main)

Challenge:	Sentence fusion is a conditional generation task that merges related sentences into a coherent text.
Approach:	They propose to build an event graph from the input sentences to capture related events in a structured way and use the constructed event graph to guide sentence fusion.
Outcome:	The proposed method achieves state-of-the-art on two datasets . it is based on the input sentences and shows that it is effective .

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Haowei Liu, Yaya Shi, Haiyang Xu, Chunfeng Yuan, Qinghao Ye, Chenliang Li, Ming Yan, Ji Zhang, Fei Huang, Bing Li, Weiming Hu

Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization (2024.findings-acl)

Challenge:	Existing methods for vision-language pre-training lack high-level semantics and text is not sufficiently involved in masked modeling.
Approach:	They propose a semantics-enhanced cross-modal MIM framework for vision-language representation learning that harvests high-level semantics from global image features via self-supervised agreement learning and transfers them to local patch encodings by sharing the encode space.
Outcome:	The proposed model achieves state-of-the-art or competitive performance on multiple vision-language tasks.

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Shichao Sun, Ruifeng Yuan, Ziqiang Cao, Wenjie Li, Pengfei Liu

Low-Resource NLP

Challenge:	Large language models (LLMs) can improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft.
Approach:	They propose to use Prompt Chaining and Stepwise Prompting to perform iterative refinement . they propose to combine the two methods to produce a more favorable outcome .
Outcome:	The proposed methods can improve summary quality by mirroring a human-like iterative process . the results show that the prompt chaining method produces a more favorable outcome .

Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)

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Fei Yuan, Yinquan Lu, Wenhao Zhu, Lingpeng Kong, Lei Li, Yu Qiao, Jingjing Xu

Rethinking Text-based Protein Understanding: Retrieval or LLM? (2025.emnlp-main)

Challenge:	Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models.
Approach:	They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages .
Outcome:	The proposed model outperforms existing models in OPUS and is faster than existing models.

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Juntong Wu, Zijing Liu, He Cao, Li Hao, Bin Feng, Zishan Shu, Ke Yu, Li Yuan, Yu Li

KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)

Challenge:	Recent studies have focused on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment.
Approach:	They propose a retrieval-enhanced method which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
Outcome:	The proposed method significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.

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Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu

Large Language Model Evaluation via Matrix Nuclear-Norm (2025.findings-emnlp)

Challenge:	Long context capability is a crucial competency for large language models as it mitigates the human struggle to digest long-form texts.
Approach:	They propose to evaluate 10+ state-of-the-art approaches for long context-capable LLMs.
Outcome:	The proposed methods are compared against 10+ state-of-the-art approaches across seven categories of long context tasks.

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Yahan Li, Tingyu Xia, Yuan Wu, Yi Chang

Effects of Human Adversarial and Affable Samples on BERT Generalization (2023.findings-emnlp)

Challenge:	Large language models (LLMs) are computationally intensive due to their O(n3) time complexity with Singular Value Decomposition (SVD).
Approach:	They propose a metric to quantify the data compression proficiency of large language models and a convex approximation of matrix rank to capture both predictive discriminability and diversity.
Outcome:	The proposed model achieves speeds 8 to 24 times faster than Matrix Entropy for the CEREBRAS-GPT model as models increase from 111M to 6.7B .

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Aparna Elangovan, Estrid He, Yuan Li, Karin Verspoor

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Limiting quantities of training data is considered a key impediment to achieving generalizability in machine learning.
Approach:	They examine the impact of training data quality, not quantity, on a model’s generalizability by comparing human-adversarial and human-affable training samples.
Outcome:	The proposed model performance improves with 10-30% h-adversarial instances in text classification and relation extraction tasks.

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Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang

Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)

Challenge:	Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities.
Approach:	They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments.
Outcome:	The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments.

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Mengying Yuan, WenHao Wang, Zixuan Wang, Yujie Huang, Kangli Wei, Fei Li, Chong Teng, Donghong Ji

Explainability & Interpretability in NLP

Challenge:	Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored.
Approach:	They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction.
Outcome:	The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios.

Diffusion Based Counterfactual Augmentation for Dual Sentiment Classification (2024.lrec-main)

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Dancheng Xin, Jiawei Yuan, Yang Li

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention (2026.acl-long)

Challenge:	Existing methods to augment sentiment models have failed to mitigate spurious association problem inherent in the original data.
Approach:	They propose a framework for enhancing sentiment models using an antonymous paradigm and contrastive learning to generate high-quality samples.
Outcome:	The proposed framework achieves state-of-the-art performance on four benchmark datasets.

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Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

REAL: REtrieval-reAsoning and Logic-constructed Attention Behaviors for Long-Context KV Cache Compression (2026.acl-long)

Challenge:	Existing methods for long-video inference use compression or sparse attention . existing methods restrict LMMs from handling longer, more complex videos .
Approach:	They propose a sequence-parallel framework with optimized attention that accelerates long-video inference across multiple GPUs.
Outcome:	The proposed framework delivers speedups of 12.72x, 1.70x, and 1.18x over FlashAttn, ZigZagRing, and APB without significant performance loss.

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Mengjie Li, Yuan Feng, Xike Xie, William J. Song

Self-Attention Guided Copy Mechanism for Abstractive Summarization (2020.acl-main)

Challenge:	Existing methods to evict keyvalue caches ignore diverse behavior in failure cases, such as bias and distraction.
Approach:	They propose a method to analyze attention head behaviors in success and failure scenarios by maximizing signal-to-noise ratio and minimizing noise from bias and distraction.
Outcome:	The proposed method achieves comparable accuracy to the strongest baseline, HeadKV-R2 on LongBench v2 while requiring 32x less space.

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Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He, Bowen Zhou

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026.findings-acl)

Challenge:	Abstractive summarization models have been widely used to extract words from source into summary, but how to ensure that important words in source are copied remains a challenge.
Approach:	They propose a Transformer-based model to enhance copy mechanism by identifying the importance of each source word based on the degree centrality.
Outcome:	The proposed model outperforms baseline methods on CNN/Daily Mail and Gigaword datasets.

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Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan

Efficient Transformer Parameter Reuse via Zero-Token Mechanism (2026.findings-acl)

Challenge:	Existing Large Language Models struggle to reason systematically under cost constraints . Existing approaches lack the knowledge-reasoning capability to reason under cost .
Approach:	They propose a knowledge-enhanced framework that leverages large language models to construct MDKGs . they propose three collaborative agents that handle language understanding and generation .
Outcome:	GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%.

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Guanghao Li, Wenhao Jiang, Li Shen, Ming Tang, Chun Yuan

How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective (2024.emnlp-main)

Challenge:	Existing approaches to scaling up parameter counts are impractical for users with limited computational resources.
Approach:	They propose a decoupled parameter cycling strategy that employs a head-tail decoupling strategy to decouple the first (head) and last (tail) layers from the parameter cycling process.
Outcome:	The proposed approach achieves superior performance under strict parameter constraints and significantly reduces computational overhead via early exits.

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Teng Xiao, Mingxiao Li, Yige Yuan, Huaisheng Zhu, Chao Cui, Vasant Honavar

Rubrik’s Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset (2025.acl-long)

Challenge:	Existing methods for fine-tuning large language models are not suitable for task-dependent tasks.
Approach:	They propose a generalized self-imitation learning framework which aligns large language models with offline demonstration data.
Outcome:	The proposed framework outperforms baselines in many challenging benchmarks . it is available on github.com/tengxiao1/GSIL .

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Diana Galvan-Sosa, Gabrielle Gaudeau, Pride Kavumba, Yunmeng Li, Hongyi Gu, Zheng Yuan, Keisuke Sakaguchi, Paula Buttery

Explainability & Interpretability in NLP

Challenge:	Large-Language Models (LLMs) are increasingly being used in explanation generation tasks due to their unreliability.
Approach:	They propose a rubric and a dataset of 26k explanations written and quality-annotated using the rubric by humans and six open- and closed-source LLMs to test their proposed rubric.
Outcome:	The proposed rubric and CUBE dataset focuses on reasoning and language tasks and provides the necessary diversity to test it.

KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models (2025.findings-emnlp)

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Xian Peng, Pan Yuan, Dong Li, Junlong Cheng, Qin Fang, Zhi Liu

ParaLS: Lexical Substitution via Pretrained Paraphraser (2023.acl-long)

Challenge:	Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings.
Approach:	They propose a multi-agent framework for structured Socratic teaching with LLMs that integrates a structured SocRule and a consultant-teacher collaborative teaching mechanism.
Outcome:	The proposed framework outperforms existing LLMs in natural language generation and dialogue comprehension in the classroom.

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Jipeng Qiang, Kang Liu, Yun Li, Yunhao Yuan, Yi Zhu

Paraphrase Makes Perfect: Leveraging Expression Paraphrase to Improve Implicit Sentiment Learning (2025.coling-main)

Challenge:	Lexical substitution (LS) is an extremely powerful technology that can be used as a backbone of various NLP applications such as writing assistance.
Approach:	They propose two simple decoding strategies that focus on the variations of the target word during decoding to generate substitutes from a paraphraser.
Outcome:	The proposed methods outperform state-of-the-art LS methods based on pre-trained language models on three benchmarks.

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Xia Li, Junlang Wang, Yongqiang Zheng, Yuan Chen, Yangjia Zheng

Exploring Representation-level Augmentation for Code Search (2022.emnlp-main)

Challenge:	Existing implicit sentiment learning methods focus on capturing implicit sentiment knowledge individually, without considering the potential connection between implicit and explicit sentiment.
Approach:	They propose an expression paraphrase strategy and a sentiment-consistent contrastive learning mechanism to learn the connections between implicit and explicit sentiment expressions and integrate them into the model.
Outcome:	The proposed method is effective on implicit sentiment analysis on public datasets.

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Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Recent data augmentations for code search are at the raw-data level, which requires additional code analysis and training cost.
Approach:	They propose a general format of representation-level augmentation that unifies existing methods.
Outcome:	The proposed methods can boost the performance of code search models on a large-scale dataset.

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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

Efficient KL Divergence Estimation via Truncated Top-K Integration for Large Language Models (2026.acl-long)

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.

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Xinyuan Wang, Luozhijie Jin, Bo Wang, Yuan Li, Zhangyue Yin, Xipeng Qiu

Judge and Improve: Towards a Better Reasoning of Knowledge Graphs with Large Language Models (2025.emnlp-main)

Challenge:	Existing methods for estimating KL divergence using only top-k tokens suffer from high variance or systematic bias.
Approach:	They propose a top-k Importance-weighted KL Estimator that exploits the Zipfian structure of language model distributions by integrating only the top-K tokens.
Outcome:	The proposed estimator outperforms existing estimators on multiple benchmarks while exhibiting lower variance.

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Mo Zhiqiang, Yang Hua, Jiahui Li, Yuan Liu, Shawn Wong, Jianmin Huang

Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)

Challenge:	Existing approaches to integrating graph and language models face two key limitations: achieving robust semantic alignment and ensuring interpretability in outputs.
Approach:	They propose a framework to integrate graph and language modalities while enhancing transparency.
Outcome:	Extensive experiments on three benchmark datasets show that the proposed framework surpasses existing methods in efficiency and generates outputs that are significantly more interpretable.

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Li Yuan, Jin Wang, Liang-Chih Yu, Xuejie Zhang

Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs (2024.emnlp-main)

Challenge:	Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly.
Approach:	They propose to extend the graph convolutional network by assigning different weights to edges of connected words.
Outcome:	The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance.

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Liu Ran, Zhongzhou Liu, Xiaoli Li, Yuan Fang

PRINCE: Prefix-Masked Decoding for Knowledge Enhanced Sequence-to-Sequence Pre-Training (2022.emnlp-main)

Challenge:	Existing methods to predict instances for missing relations on knowledge graphs are limited by their limited training examples.
Approach:	They propose a context-aware adapter for few-shot relation learning in KGs . they propose tunable relation adaptation and contextual information for each relation .
Outcome:	Experiments on three benchmark KGs validate the superiority of RelAdapter over state-of-the-art methods.

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Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He

Structural Supervision for Word Alignment and Machine Translation (2022.findings-acl)

Challenge:	Existing studies focus on injecting noises into the input sequence, but feasibility of injecting them into the decoding sequence remains an open question.
Approach:	They propose a pre-training paradigm that integrates knowledge-enhanced decoding with noises in the prefix to strengthen the representation learning of entities that span over multiple input tokens.
Outcome:	The proposed model achieves state-of-the-art results on two knowledge-driven data-to-text generation tasks with up to 2% BLEU gains.

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Lei Li, Kai Fan, Hongjia Li, Chun Yuan

Beyond Self-Report: Bridging the Intention-Behavior Gap in Critical Thinking Assessment via Interpretable Multi-Agent System (2026.acl-long)

Challenge:	Existing knowledge on syntactic structure neglects the rich structural information from target tokens and the structural similarity between the source and target sentences.
Approach:	They propose to incorporate syntactic structure of both source and target tokens into the encoder-decoder framework, tightly correlating the internal logic of word alignment and machine translation for multi-task learning.
Outcome:	The proposed method outperforms baselines on four publicly available language pairs and consistently outperformed baselines in alignment accuracy and translation quality.

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Zekun Li, Jifan Yu, Haoxuan Li, Ye He, Daniel Zhang-Li, Shangqing Tu, Joy Jia Yin Lim, Yikun Jiang, Jiaxin Yuan, Yu Zhang

Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods (2025.findings-emnlp)

Challenge:	Accurate assessment of critical thinking is limited by the Intention Behavior Gap in psychology . evaluators that measure self-reported competence are limited by multiagent architectures .
Approach:	They propose a framework that operationalizes cognitive assessment into an interpretable multi-agent workflow with Assessment Chain-of-Thought.
Outcome:	The proposed framework aligns better with human expert ratings than gold-standard inventories on large-scale simulations and human participants.

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Yulin Chen, Haoran Li, Yuan Sui, Yangqiu Song, Bryan Hooi

ChemAmp: Amplified Chemistry Tools via Composable Agents (2026.findings-acl)

Challenge:	Recent studies have shown that LLMs are vulnerable to prompt injection attacks because of their instruction-following abilities and inability to distinguish the instructions in the data content.
Approach:	They propose backdoor-powered prompt injection attacks that trick LLMs into deviating from the original input instruction and executing the attackers’ target instruction.
Outcome:	The proposed attacks trick the LLMs into deviating from the input instruction and executing the attackers’ target instruction.

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Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi

Challenge:	LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints.
Approach:	They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks.
Outcome:	The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration.

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

How Vocabulary Sharing Facilitates Multilingualism in LLaMA? (2024.findings-acl)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Fei Yuan, Shuai Yuan, Zhiyong Wu, Lei Li

Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)

Challenge:	Large Language Models (LLMs) show strong performance on English tasks, but their performance in other languages is limited.
Approach:	They conducted an exhaustive analysis of the multilingual capability of LLMs by examining the performance gap before and after embedding fine-tuning across 101 languages.
Outcome:	The proposed model improves on the attributes of four quadrants in the model and provides actionable and efficient guidelines for tuning these languages.

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Lei Li, Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Yi Yuan, Zheng Wei, Zang Li

Chinese Idiom Paraphrasing (2023.tacl-1)

Challenge:	Existing methods for enhancing the performance of large language models require expensive manual annotations.
Approach:	They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence.
Outcome:	The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method.

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Jipeng Qiang, Yang Li, Chaowei Zhang, Yun Li, Yi Zhu, Yunhao Yuan, Xindong Wu

PsyMem: Fine-grained Psychological Alignment and Explicit Memory Control for Advanced Role-Playing LLMs (2026.tacl-1)

Challenge:	Chinese idioms are hard to understand by children and non-native speakers due to their non-compositionality and metaphorical meaning.
Approach:	They propose a task to rephrase idiom-containing sentences to non-idiomatic ones under the premise of preserving the original sentence’s meaning.
Outcome:	The proposed method has better performance than baselines based on the established dataset.

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Xilong Cheng, Yunxiao Qin, Yuting Tan, Zhengnan Li, Ye Wang, Hongjiang Xiao, Yuan Zhang

Challenge:	Existing role-playing models rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimensions.
Approach:	They propose a framework that integrates fine-grained psychological attributes and explicit memory control for role-playing.
Outcome:	The proposed framework outperforms baseline models in human-likeness and character fidelity.

MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension (2024.findings-emnlp)

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Xingyu Lu, He Cao, Zijing Liu, Shengyuan Bai, Leqing Chen, Yuan Yao, Hai-Tao Zheng, Yu Li

Question Answering

Challenge:	Existing models generate erroneous information and evaluations fail to assess factual correctness of models.
Approach:	They propose to use MoleculeQA to evaluate molecular factual correctness in large language models by organizing molecules into a taxonomy and building QA pairs through human and LLM efforts.
Outcome:	The proposed model improves the factual correctness of generated information and enables the development of new models.

MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024.acl-long)

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Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou

Curse of Knowledge: Your Guidance and Provided Knowledge are biasing LLM Judges in Complex Evaluation (2025.findings-emnlp)

Challenge:	In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective.
Approach:	They propose to fine-tune data augmentation by query evolution and diverse reasoning paths.
Outcome:	The proposed model achieves new state-of-the-art on GSM8K and MATH.

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Weiyuan Li, Xintao Wang, Siyu Yuan, Rui Xu, Jiangjie Chen, Qingqing Dong, Yanghua Xiao, Deqing Yang

Challenge:	a recent study has focused on simple settings, but their reliability in complex tasks remains understudied.
Approach:	They propose to use large language models as judges to evaluate reliability in complex tasks . they use a challenge benchmark to expose and quantify Auxiliary Information Induced Biases .
Outcome:	The proposed benchmark exposes and quantifies Auxiliary Information Induced Biases across 12 basic and 3 advanced scenarios.

Reducing Token Redundancy in LVLMs: A Systematic Review of Token Pruning Methods (2026.acl-long)

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Hanzhang Yuan, Mengxuan Hu, Wenhao Zhang, Tianlong Wang, Zhongliang Zhou, Jiasen Lu, Sheng Li

Visual Data in NLP

Challenge:	Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts.
Approach:	They propose a taxonomy categorizing methods into vision-side, LLM-side and hybrid paradigms and analyze token selection mechanisms and pruning strategy.
Outcome:	The proposed method selectively removes less informative tokens while maintaining performance.

InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao

Challenge:	Existing methods focus on knowledge and linguistic patterns of characters.
Approach:	They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome:	The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.

Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation (2026.findings-acl)

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Zhiheng Li, Zongyang Ma, Yuntong Pan, Ziqi Zhang, Xiaolei Lv, Bo Li, Jun Gao, Jianing Zhang, Chunfeng Yuan, Bing Li, Weiming Hu

DT-Solver: Automated Theorem Proving with Dynamic-Tree Sampling Guided by Proof-level Value Function (2023.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) are increasingly being deployed as content moderators . however, they exploit the Human-AI capability gap and create adversarial environments . smuggling attacks exploit the human-AI gap and exploit the vulnerability .
Approach:	They construct a benchmark to evaluate the vulnerability of MLLMs as content moderators . they identify three root causes: limited capabilities of vision encoders, robustness gap in OCR .
Outcome:	The proposed model exploits the Human-AI capability gap and is vulnerable to smuggling attacks.

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Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, Xiaodan Liang

Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)

Challenge:	Recent advances in neural theorem-proving resort to large language models and tree searches.
Approach:	They propose a Dynamic-Tree Driven Theorem Solver to accommodate general theoremes by guiding the search procedure with state confidence and proof-level values.
Outcome:	The proposed method outperforms state-of-the-art methods on two popular theorem-proving datasets with a 6.65% improvement on average in terms of success rate.

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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong

Grounded Multimodal Procedural Entity Recognition for Procedural Documents: A New Dataset and Baseline (2024.lrec-main)

Challenge:	Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data.
Approach:	They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process.
Outcome:	Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities.

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Haopeng Ren, Yushi Zeng, Yi Cai, Zhenqi Ye, Li Yuan, Pinli Zhu

VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction (2025.findings-emnlp)

Challenge:	Existing methods to extract procedural knowledge from documents focus on text-only settings, which is insufficient for entity disambiguation.
Approach:	They propose a model to detect the entity and the corresponding bounding box groundings in images.
Outcome:	The proposed model detects the entity and the corresponding bounding box groundings in image (i.e., visual entities) it is based on a dataset of a WikiHow 1 and EHow 2 document and the results are compared with existing models.

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Jie Yang, Jiajun Chen, Zhangyue Yin, Shuo Chen, Yuxin Wang, Yiran Guo, Yuan Li, Yining Zheng, Xuanjing Huang, Xipeng Qiu

Challenge:	Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery.
Approach:	They propose a state-based function call approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
Outcome:	The proposed approach outperforms traditional function calling approaches, achieving superior execution accuracy and reduced latency.

PMAES: Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring (2023.acl-long)

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Yuan Chen, Xia Li

On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)

Challenge:	Current cross-prompt automated essay scoring systems are limited by their ability to extract features directly from the original prompt.
Approach:	They propose a method to learn more shared features between the source and target prompts by using a "prompt-mapping" approach to obtain more shared feature representations between the two prompts .
Outcome:	The proposed method can be applied to a ASAP++ dataset showing that it is highly efficient and consistent.

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Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye

Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models (2024.findings-emnlp)

Challenge:	Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples.
Approach:	They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples.
Outcome:	Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem.

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Songtao Jiang, Tuo Zheng, Yan Zhang, Yeying Jin, Li Yuan, Zuozhu Liu

MTAVG-Bench: A Diagnostic Benchmark for Multi-Talker Dialogue-Centric Audio-Video Generation (2026.acl-long)

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.

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Yanghao Zhou, Haitian Li, Rexar Lin, Heyan Huang, Jinxing Zhou, Changsen Yuan, Tian Lan, Ziqin Zhou, Yudong Li, Jiajun Xu, Jingyun Liao, YiMing Cheng, Xuefeng Chen, Xian-Ling Mao, Yousheng Feng

How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)

Challenge:	Existing evaluation benchmarks for text-to-audio-video (T2AV) generation are largely designed for human-recorded videos or single-speaker settings.
Approach:	They propose a failure-driven diagnostic benchmark for multi-talker dialogue-centric audio-video generation.
Outcome:	The benchmark evaluates multi-speaker dialogue generation at four levels: audio-visual signal fidelity, temporal attribute consistency, social interaction, and cinematic expression.

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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou

CogLM: Tracking Cognitive Development of Large Language Models (2025.naacl-long)

Challenge:	supervised fine-tuning (SFT) is a technique used to enhance multiple abilities in large language models.
Approach:	They propose to study the interplay of data composition between mathematical reasoning, code generation, and general human-aligning abilities during supervised fine-tuning.
Outcome:	The proposed model improves math reasoning and code generation with increasing data amount . the proposed model size and SFT strategies can be used to learn multiple skills with different scaling patterns.

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Xinglin Wang, Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Boyuan Pan, Heda Wang, Yao Hu, Kan Li

D-RAG: Differentiable Retrieval-Augmented Generation for Knowledge Graph Question Answering (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have recently shown remarkable abilities across a wide variety of tasks, but few studies have explored the reasons behind the evolutionary relationship among various abilities.
Approach:	They construct a benchmark CogLM based on Piaget's Theory of Cognitive Development (PTC) which measures the cognitive levels of Large Language Models (LLMs) using 1,220 questions spanning 10 cognitive abilities crafted by more than 20 human experts.
Outcome:	The proposed framework provides a comprehensive testbed for the cognitive levels of LLMs.

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Guangze Gao, Zixuan Li, Chunfeng Yuan, Jiawei Li, Wu Jianzhuo, Yuehao Zhang, Xiaolong Jin, Bing Li, Weiming Hu

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation (2026.acl-long)

Challenge:	Existing approaches to Knowledge Graph Question Answering (KGQA) use Retrieval-Augmented Generation (RAG) but subgraph selection process is non-differentiable, preventing end-to-end training of the retriever and the generator.
Approach:	They propose a Differentiable RAG approach that optimizes the retriever and the generator for KGQA.
Outcome:	The proposed approach outperforms state-of-the-art approaches on WebQSP and CWQ.

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Zekun Yuan, Yangfan Ye, Xiaocheng Feng, Baohang Li, Qichen Hong, Yunfei Lu, Dandan Tu, Bing Qin

Skill Weaving: Efficient LLM Improvement via Modular Skillpacks (2026.findings-acl)

Challenge:	Existing studies on large language models focus on literal-level translation quality, such as adequacy and fluency.
Approach:	They propose a Culture-Aware Novel-Driven Parallel Dataset for Machine Translation and a multi-dimensional evaluation framework for assessing cultural translation quality.
Outcome:	The proposed model improves evaluation reliability in LLM-as-a-judge scenarios under culture-aware constraints.

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Zhuo Li, Guodong DU, Zesheng Shi, Weiyang Guo, Weijun Yao, Yuan Zhou, Jiabo Zhang, Jing Li

DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual Systems (2026.acl-long)

Challenge:	Large Language Models (LLMs) can specialize under fixed memory and inference budgets, but they struggle to achieve high performance across heterogeneous domains.
Approach:	They propose a modular improvement framework that partitions full capabilities of a general-purpose model into domain-specific delta modules that reorganize and refine the model's internal knowledge.
Outcome:	The proposed framework outperforms monolithic models on multi-task and agentic benchmarks and achieves up to 4 speedup.

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Shuyu Zhang, Yifan Wei, Jialuo Yuan, Xinru Wang, Yanmin Zhu, Yujie Liu, Bin Li

Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness (2025.findings-acl)

Challenge:	Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts.
Approach:	They propose a dialog policy learning framework that formalizes the exploration challenge through a structured cognitive state space C.
Outcome:	The proposed framework achieves SOTA performance in success rate, efficiency, and generalization.

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Bryan Li, Fiona Luo, Samar Haider, Adwait Agashe, Siyu Li, Runqi Liu, Miranda Muqing Miao, Shriya Ramakrishnan, Yuan Yuan, Chris Callison-Burch

LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without Training (2026.findings-acl)

Challenge:	Existing approaches to retrieval-augmented generated (RAG) can be useful in multilingual settings, but they also introduce biases in the retrieved documents.
Approach:	They propose a dataset of territorial disputes paired with retrieved Wikipedia documents in 49 languages to evaluate cross-lingual robustness.
Outcome:	The proposed paradigm helps mitigate hallucinations of large language models (LLMs).

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Sikui Zhang, Guangze Gao, Ziyun Gan, Chunfeng Yuan, Zefeng Lin, Houwen Peng, Bing Li, Weiming Hu

Challenge:	Large language models (LLMs) experience significant performance degradation when the input exceeds the pretraining context window due to the out-of-distribution (OOD) behavior of Rotary Position Embedding (RoPE).
Approach:	They propose a training-free method that remaps out-of-distribution (OOD) positions into the in-distance range with fixed mapping strategies, ignoring the dynamic relationship between input length and effective context window.
Outcome:	Experiments on three representative LLMs across five mainstream long-context benchmarks show that the proposed method achieves significant performance improvements compared to existing methods.

R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)

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Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu

LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)

Challenge:	RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning.
Approach:	They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination.
Outcome:	The proposed model outperforms baselines and can transfer well to different retrievers.

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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang

Challenge:	Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making.
Approach:	They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain.
Outcome:	The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge.

LET: Leveraging Error Type Information for Grammatical Error Correction (2023.findings-acl)

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Lingyu Yang, Hongjia Li, Lei Li, Chengyin Xu, Shutao Xia, Chun Yuan

On the Faithfulness for E-commerce Product Summarization (2020.coling-main)

Challenge:	Existing methods for grammatical error correction (GEC) are mainly divided into detection-based and end-to-end generative models.
Approach:	They propose an end-to-end framework which Leverages Error Type (LET) information in the generation process to introduce more convincing error type information.
Outcome:	The proposed framework outperforms existing methods on various datasets by a clear margin.

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Peng Yuan, Haoran Li, Song Xu, Youzheng Wu, Xiaodong He, Bowen Zhou

A Survey of Retentive Network (2026.findings-acl)

Challenge:	e-commerce product summarization requires consistency between product attributes and summary . inconsistent product summaries can mislead users and decrease public credibility .
Approach:	They propose a model to generate e-commerce product summaries with product attributes . they encode product attribute table and constrain attribute words to be presented only through copying .
Outcome:	The proposed model significantly improves the faithfulness of e-commerce product summarization tasks.

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Haiqi Yang, Zhiyuan Li, Yi Chang, Yuan Wu

Challenge:	Existing studies on the effectiveness of the Retentive Networks have not yet been conducted.
Approach:	They propose a retention mechanism that integrates the inductive bias of recurrent neural networks with the parallelizable training advantages of attention-based models.
Outcome:	The proposed retention mechanism combines the inductive bias of recurrent neural networks with the parallelizable training advantages of attention-based models.

AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)

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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang

LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)

Challenge:	Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns.
Approach:	They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback.
Outcome:	The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy.

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Zhongwei Wan, Ziang Wu, Che Liu, Jinfa Huang, Zhihong Zhu, Peng Jin, Longyue Wang, Li Yuan

Visual Data in NLP

Challenge:	Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency.
Approach:	They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache.
Outcome:	The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache.

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

LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)

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.

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Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian, Mohsen Hariri, Zirui Liu, Ruixiang Tang, Zhimeng Jiang, Jiayi Yuan, Yu-Neng Chuang, Li Li, Soo-Hyun Choi, Rui Chen, Vipin Chaudhary, Xia Hu

FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture (2024.emnlp-main)

Challenge:	distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction.
Approach:	They propose a simple, efficient, yet specific recipe for a backdoor LoRA to be injected into task-enhancing LoRAs and examine the mechanisms of such infections.
Outcome:	The proposed model allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of shared LoRA assets.

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Wenyan Li, Crystina Zhang, Jiaang Li, Qiwei Peng, Raphael Tang, Li Zhou, Weijia Zhang, Guimin Hu, Yifei Yuan, Anders Søgaard, Daniel Hershcovich, Desmond Elliott

Speculative Decoding for Multi-Sample Inference (2025.findings-emnlp)

Challenge:	FoodieQA is a manually curated, fine-grained image-text dataset capturing the intricate features of food cultures across various regions in China.
Approach:	They evaluate vision–language Models and large language models on unseen food images and corresponding questions.
Outcome:	The proposed dataset evaluates vision–language Models and large language models on unseen food images and corresponding questions.

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Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Ji Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

DORA: A Dual-Objective Reinforcement Learning Framework for Effective and Efficient Multimodal Agentic Search (2026.acl-long)

Challenge:	Speculative decoding method exploits consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases.
Approach:	They propose a speculative decoding method that exploits the consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases.
Outcome:	The proposed method exploits the intrinsic consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or databases.

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Guangming Qin, Yuhao Deng, Yukun Zhao, Zhenyang Li, Junfeng Wang, Dawei Yin, Ye Yuan, Guoren Wang, Yizhou Yan, Chengliang Chai, Lei Cao

How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)

Challenge:	Existing methods to train large language models overlook quality of intermediate search results . existing methods often invoke search calls during reasoning, making inference inefficient .
Approach:	They propose a dual-objective reinforcement learning framework to improve search strategies of MLLMs . DORA outperforms state-of-the-art methods, achieving up to 8.4% higher accuracy .
Outcome:	The proposed model outperforms state-of-the-art methods while reducing search calls by 9.7%.

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Li Nguyen, Christopher Bryant, Oliver Mayeux, Zheng Yuan

R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)

Challenge:	Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively.
Approach:	They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input .
Outcome:	The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively.

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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu

Challenge:	Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following.
Approach:	They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records.
Outcome:	The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random.

SOAR: Supervision from Observation for Agentic Reinforcement Learning (2026.acl-long)

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Meng Li, Lei Li, Xiting Wang, Yi Yuan, Zheng Wei, null Brucebian, Zang Li

TREA: Tree-Structure Reasoning Schema for Conversational Recommendation (2023.acl-long)

Challenge:	Prior work assigns supervision based on outcome rewards or external reward models, but ignores environment observations, a critical source of learning.
Approach:	They propose a supervision-based agentic reinforcement learning system that integrates environment observations as an explicit supervision signal.
Outcome:	The proposed model improves performance on reasoning and deep research tasks while reducing erroneous and inefficient tool usage.

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Wendi Li, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Ye Yuan, Wenfeng Xie, Dangyang Chen

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction (2025.naacl-long)

Challenge:	Recent reasoning-based models cannot fully figure out complex causal relationships between mentioned entities with external knowledge.
Approach:	They propose a Tree structure Reasoning schEmA that constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities.
Outcome:	Extensive experiments on two public CRS datasets show the proposed model works.

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Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, Deqing Yang

RoChBert: Towards Robust BERT Fine-tuning for Chinese (2022.findings-emnlp)

Challenge:	EASYTOOL combines tools from diverse tool documentation into a single tool instruction.
Approach:	They propose a framework that transforms tool documentation into a unified tool instruction.
Outcome:	EASYTOOL combines extensive tool documentation into a concise tool instruction . it reduces token consumption and improves performance of LLM-based agents .

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Zihan Zhang, Jinfeng Li, Ning Shi, Bo Yuan, Xiangyu Liu, Rong Zhang, Hui Xue, Donghong Sun, Chao Zhang

Diversity in Unity, Theory in Practice: Hierarchical Multitask Benchmarks for Chinese Minority Languages (2026.acl-long)

Challenge:	Pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts.
Approach:	They propose to fuse Chinese phonetic and glyph features into pre-trained models by using a more comprehensive adversarial graph.
Outcome:	The proposed framework outperforms existing methods in significant ways on a wide range of tasks while remaining accurate on benign texts.

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Yijie Li, Xi Cao, Yuan Sun, Quulgan Minggad, Abdulla Ablikim, Jia Qing Cai Wang

Machine Translation

Challenge:	CMiLBench is a framework to evaluate linguistically and culturally diverse minority languages . rapid evolution of LLMs has revolutionized NLP, but progress is unevenly distributed .
Approach:	They propose a framework to translate a theoretical notion of "diversity in unity" into practical evaluation for three minority languages . CMiLBench comprises 24,663 instances across 5 difficulty levels and 17 tasks .
Outcome:	The proposed framework evaluates 14 state-of-the-art LLMs with a hybrid framework . it integrates automatic metrics and LLM-as-a-Judge scoring .

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

The Critique of Critique (2024.findings-acl)

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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Shichao Sun, Junlong Li, Weizhe Yuan, Ruifeng Yuan, Wenjie Li, Pengfei Liu

Challenge:	MetaCritique builds specific quantification criteria to evaluate the quality of critique . a systematic method to evaluate critique is lacking.
Approach:	They propose a critique of critique, termed MetaCritique, which builds specific quantification criteria and aggregates each AIU's judgment for the overall score.
Outcome:	The proposed method can achieve near-human performance across 16 datasets.

Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models (2025.findings-emnlp)

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Jinzhe Li, Gengxu Li, Yi Chang, Yuan Wu

Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)

Challenge:	Existing studies assess LLMs’ reasoning ability in ideal settings, ignoring their vulnerabilities when faced with flawed premises.
Approach:	They propose to evaluate LLMs' ability to proactively identify and articulate errors in input premises.
Outcome:	The proposed model enables LLMs to proactively identify and articulate errors in input premises.

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Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Yang Guang, Chenghua Lin

Challenge:	Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored.
Approach:	They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection.
Outcome:	The proposed framework provides valuable insights for developing more accurate and customisable human simulacra.

PaSa: An LLM Agent for Comprehensive Academic Paper Search (2025.acl-long)

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Yichen He, Guanhua Huang, Peiyuan Feng, Yuan Lin, Yuchen Zhang, Hang Li, Weinan E

Collaborative Document Simplification Using Multi-Agent Systems (2025.coling-main)

Challenge:	We introduce PaSa, an advanced Paper Search agent powered by large language models . despite being trained on synthetic data, PaSA outperforms existing baselines on RealScholarQuery .
Approach:	They introduce PaSa, an advanced Paper Search agent powered by large language models . they optimize PaSA using a synthetic dataset, AutoScholarQuery, which includes 35k fine-grained queries .
Outcome:	The paper analyzes the performance of a paper search agent using a synthetic dataset . it significantly outperforms existing benchmarks on RealScholarQuery .

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Dengzhao Fang, Jipeng Qiang, Xiaoye Ouyang, Yi Zhu, Yunhao Yuan, Yun Li

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	Document simplification requires complex factors such as technical terminology, metaphors, and overall coherence.
Approach:	They propose a multi-agent framework for document simplification based on large language models that emulates the collaborative process of a human expert team through the roles played by multiple agents.
Outcome:	The proposed framework emulates the collaborative process of a human expert team through the roles played by multiple agents, addressing the intricate demands of document simplification.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

Discover and Prove: An Open-source Agentic Framework for Hard Mode Automated Theorem Proving in Lean 4 (2026.acl-long)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Chengwu Liu, Yichun Yin, Ye Yuan, Jiaxuan Xie, Botao Li, Siqi Li, Jianhao Shen, Yan Xu, Lifeng Shang, Ming Zhang

CorefQA: Coreference Resolution as Query-based Span Prediction (2020.acl-main)

Challenge:	Existing approaches to solving mathematical problems fall into two broad categories: informal methods and formal methods.
Approach:	They propose to use LLM natural-language reasoning to discover answers . they introduce Discover And Prove framework that rewrites Hard Mode statements into Easy Mode ones for existing ATP provers.
Outcome:	The proposed framework can be used to prove hard mode statements on ATP benchmarks.

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Wei Wu, Fei Wang, Arianna Yuan, Fei Wu, Jiwei Li

CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild (2021.emnlp-main)

Challenge:	Existing coreference resolution models suffer from mention proposal.
Approach:	They propose a query-based span prediction task that can retrieve mentions left out at the mention proposal stage.
Outcome:	The proposed model can retrieve mentions left out at the mention proposal stage and improve generalization capability using existing question answering datasets.

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Yuan Yao, Jiaju Du, Yankai Lin, Peng Li, Zhiyuan Liu, Jie Zhou, Maosong Sun

U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents (2026.findings-acl)

Challenge:	Existing relation extraction methods focus on extracting relational facts between entity pairs within single sentences or documents.
Approach:	They present a problem of cross-document relation extraction (CRE) using human annotations.
Outcome:	The proposed dataset is the first human-annotated cross-document RE dataset . it shows that it is challenging to existing RE methods including strong BERT-based models.

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Jin Su, Runnan Fang, Yeqiu Li, Xiaobin Wang, Shihao Cai, Pengjun Xie, Ningyu Zhang, Fajie Yuan

Summarize-Exemplify-Reflect: Data-driven Insight Distillation Empowers LLMs for Few-shot Tabular Classification (2025.findings-emnlp)

Challenge:	Existing context-folding methods are designed for single-query or single-intent scenarios.
Approach:	They propose a dynamic context-folding framework tailored to user-centric tasks that preserves fine-grained information through dynamic context folding.
Outcome:	The proposed framework outperforms ReAct and previous folding frameworks on long, noisy tasks.

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Yifei Yuan, Jiatong Li, Weijia Zhang, Mohammad Aliannejadi, Evangelos Kanoulas, Renjun Hu

Post-Hoc Watermarking for Robust Detection in Text Generated by Large Language Models (2025.coling-main)

Challenge:	Recent studies show the promise of large language models for few-shot tabular classification but highlight challenges due to the variability in structured data.
Approach:	They propose a framework that distills data into actionable insights to enable robust and effective classification by large language models.
Outcome:	The proposed framework integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques.

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Jifei Hao, Jipeng Qiang, Yi Zhu, Yun Li, Yunhao Yuan, Xiaoye Ouyang

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)

Challenge:	Existing methods for document simplification address complex factors such as technical terminology, metaphors, and overall coherence.
Approach:	They propose a multi-agent framework AgentSimp for document simplification based on large language models that simulates collaboration among agents through roles played by multiple agents.
Outcome:	The proposed framework produces simplified documents that are more thoroughly simplified and more coherent across various articles and styles.

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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao

Common Law Annotations: Investigating the Stability of Dialog System Output Annotations (2023.findings-acl)

Challenge:	Existing retrieval augmented language models often overlook effective alignment with human preferences.
Approach:	They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity .
Outcome:	The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources.

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Seunggun Lee, Alexandra DeLucia, Nikita Nangia, Praneeth Ganedi, Ryan Guan, Rubing Li, Britney Ngaw, Aditya Singhal, Shalaka Vaidya, Zijun Yuan, Lining Zhang, João Sedoc

LegoMT2: Selective Asynchronous Sharded Data Parallel Training for Massive Neural Machine Translation (2025.findings-acl)

Challenge:	High agreement is often used to show reliability of annotation procedures, but it is insufficient to ensure or reproducibility.
Approach:	They propose a protocol that increases Inter-Annotator Agreement among annotators and a standardized and codified protocol that strictly enforces transparency in the annotation process.
Outcome:	The proposed protocol ensures transparency in the annotation process, which ensures reproducibility of annotation guidelines.

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Fei Yuan, Yinquan Lu, Lei Li, Jingjing Xu

Machine Translation

Challenge:	Existing methods to train a single model for massive languages have huge communication overheads and parameter interference.
Approach:	They propose an efficient training approach with an asymmetric multi-way model architecture for massive multilingual neural machine translation.
Outcome:	The proposed model is 16.2 faster than the distributed training method for M2M-100-12B while improving the translation performance by an average of 2.2 BLEU on Flores-101.

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

How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation (2025.findings-acl)

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 .

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Rui Li, Heming Xia, Xinfeng Yuan, Qingxiu Dong, Lei Sha, Wenjie Li, Zhifang Sui

Challenge:	Recent studies have focused on dialogue simulation while overlooking human behavior simulation, which is crucial for digital twins.
Approach:	They propose to integrate persona metadata into LLMs and use it to iteratively infer contextually appropriate behaviors within dynamic scenarios.
Outcome:	The proposed model is based on 15,846 distinct behaviors across 1,001 unique personas and incorporates persona metadata to iteratively infer appropriate behaviors within dynamic scenarios.

LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating (2025.acl-long)

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Chao Deng, Jiale Yuan, Pi Bu, Peijie Wang, Zhong-Zhi Li, Jian Xu, Xiao-Hui Li, Yuan Gao, Jun Song, Bo Zheng, Cheng-Lin Liu

AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs (2024.naacl-long)

Challenge:	Existing document understanding benchmarks only handle a small number of pages . existing models are limited to handling only a limited number of documents .
Approach:	They propose a long document understanding benchmark that integrates three primary tasks and 20 sub-tasks based on different primary tasks.
Outcome:	The proposed model outperforms existing benchmarks on open-source and closed-source models . the model outpersforms other models on more than 33,000 pages of documents .

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Yassir Fathullah, Chunyang Wu, Egor Lakomkin, Ke Li, Junteng Jia, Yuan Shangguan, Jay Mahadeokar, Ozlem Kalinli, Christian Fuegen, Mike Seltzer

Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)

Challenge:	a new model for speech processing and reasoning uses curated data instead of text.
Approach:	They extend the instruction-tuned Llama-2 model with end-to-end speech processing and reasoning abilities without using any carefully curated paired data.
Outcome:	The proposed model outperforms or outperfects existing models on synthesized and recorded speech QA tests.

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Haoran Li, Song Xu, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

MultiLingPoT: Boosting Mathematical Reasoning in LLMs through Multilingual Program Integration (2025.findings-emnlp)

Challenge:	Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying.
Approach:	They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Outcome:	The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying .

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Nianqi Li, Zujie Liang, Siyu Yuan, Jiaqing Liang, Feng Wei, Yanghua Xiao

Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection (2022.emnlp-main)

Challenge:	Program-of-Thought is an important way for LLMs to solve mathematical problems.
Approach:	They propose a multilingual programme reasoning method that uses program instead of natural language in reasoning and proposes to integrate multilingual integration into the training and inference.
Outcome:	The proposed method improves individual language’s reasoning accuracy by 2.5% and improves performance by 8%.

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Yang Li, Jiawei Yuan

From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MarkerGen (2025.acl-long)

Challenge:	Existing methods for zero-shot stance detection are labor-intensive to train for each new target.
Approach:	They propose a generative data augmentation approach to generate training samples containing unseen and seen targets and map them into the same embedding space with contrastive learning.
Outcome:	The proposed model achieves state-of-the-art on most topics in the task of zero-shot stance detection.

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Peiwen Yuan, Chuyi Tan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Boyuan Pan, Yao Hu, Kan Li

Challenge:	Existing methods to control text length are lacking in LCTG, posing a major limitation for practical applications.
Approach:	They propose a plug-and-play approach that decomposes LCTG sub-abilities with human patterns as reference and performs detailed error analysis.
Outcome:	The proposed method significantly improves LCTG across various settings, exhibiting outstanding effectiveness and generalizability.

APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing (2021.findings-emnlp)

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Ying Li, Meishan Zhang, Zhenghua Li, Min Zhang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan

Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation (2026.findings-acl)

Challenge:	Existing models for dependency parsing use labeled training data for several fixed domains, but performance drops when labeles only exist for several out-domains.
Approach:	They propose a model for multi-source cross-domain dependency parsing that uses a parameter generation network and adversarial network for learning domain-invariant representations.
Outcome:	The proposed model improves cross-domain parsing performance by about 2 points over strong BERT-enhanced baselines over a recently released dataset for multi-domain dependency parse.

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Wenhao Wang, Yanyan Li, Long Jiao, Jiawei Yuan

LM-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation (2024.emnlp-demo)

Challenge:	Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions.
Approach:	They propose a framework that enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning.
Outcome:	The proposed framework enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning.

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Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Yan Xuan, Jiaxi Yuan, Bin Xu, Juanzi Li, Zhiyuan Liu

Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)

Challenge:	Semi-structured interviews are a crucial method of data acquisition in qualitative research.
Approach:	They propose a semi-structured interview system that automates interview preparation, analysis and control by interviewers.
Outcome:	Experimental results show that LM-Interview performs comparable to human interviewers . the system can be used to analyze semi-structured interviews without interviewers' involvement .

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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang

Mapping Natural Language Instructions to Mobile UI Action Sequences (2020.acl-main)

Challenge:	Modern NLP workflows require different models for generation and embedding tasks.
Approach:	They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder.
Outcome:	The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps.

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Yang Li, Jiacong He, Xin Zhou, Yuan Zhang, Jason Baldridge

CryptoTrade: A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading (2024.emnlp-main)

Challenge:	a new problem of grounding natural language instructions to mobile UI actions is emerging . we use a Transformer to extract action phrase tuples from long-range natural language instruction .
Approach:	They propose a dataset that pairs English instructions with actions performed by people on a mobile UI emulator.
Outcome:	The proposed model achieves 70.59% accuracy on predicting complete ground-truth action sequences in PixelHelp.

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Yuan Li, Bingqiao Luo, Qian Wang, Nuo Chen, Xu Liu, Bingsheng He

Entity-Relation Extraction as Multi-Turn Question Answering (P19-1)

Challenge:	Large Language Models (LLMs) have been used for financial decision-making and stock market prediction for years.
Approach:	They propose to use Large Language Models to analyze on-chain and off-chain data to provide a comprehensive overview of the cryptocurrency market.
Outcome:	The proposed trading agent leverages the transparency and immutability of on-chain data, as well as the timeliness and influence of off-chain signals, providing a comprehensive overview of the cryptocurrency market.

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Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li

Revisiting Self-Consistency from Dynamic Distributional Alignment Perspective on Answer Aggregation (2025.findings-acl)

Challenge:	Identifying entities and their relations is the prerequisite of extracting structured knowledge from unstructured raw texts.
Approach:	They propose a new paradigm for the task of entity-relation extraction . they cast the task as a multi-turn question answering problem .
Outcome:	The proposed paradigm significantly outperforms previous best models on the ACE and CoNLL04 datasets.

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Yiwei Li, Ji Zhang, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Jiayi Shi, Yueqi Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li

Generative Entity Typing with Curriculum Learning (2022.emnlp-main)

Challenge:	Existing studies on self-consistency show that it improves reasoning abilities by aggregating diverse stochastic samples.
Approach:	They propose a confidence-driven mechanism that dynamically calibrates temperature to align with high probability modes.
Outcome:	The proposed method outperforms fixed-diversity baselines on reasoning tasks and improves both average and best-case performance.

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Siyu Yuan, Deqing Yang, Jiaqing Liang, Zhixu Li, Jinxi Liu, Jingyue Huang, Yanghua Xiao

Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)

Challenge:	Entity typing fails to assign an entity to the types beyond the predefined type set.
Approach:	They propose a generative entity typing paradigm that assigns types to entities . traditional classification-based approaches fail to assign entities to the types beyond the predefined set . they employ curriculum learning to train the model on heterogeneous data .
Outcome:	The proposed model outperforms the state-of-the-art model on heterogeneous training data.

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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li

Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT (2020.coling-main)

Challenge:	Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes.
Approach:	They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy.
Outcome:	The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline.

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Ruifeng Yuan, Zili Wang, Wenjie Li

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes (2024.findings-emnlp)

Challenge:	Existing extractive summarization models generate summaries by selecting salient sentences, but there is a gap between the human-written gold summary and oracle sentence labels.
Approach:	They propose to extract fact-level semantic units for better extractive summarization by incorporating a hierarchical structure into the model and incorporate it with BERT using a Hierarchical graph mask.
Outcome:	The proposed model achieves state-of-the-art on the CNN/DaliyMail dataset.

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He Cao, Yanjun Shao, Zhiyuan Liu, Zijing Liu, Xiangru Tang, Yuan Yao, Yu Li

GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)

Challenge:	Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific domains.
Approach:	They propose a framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations.
Outcome:	The proposed framework improves multimodal LLMs through cross-modal alignment and multi-graph understanding.

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Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann

Efficient Test-Time Scaling via Temporal Reasoning Aggregation (2026.findings-acl)

Challenge:	Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios.
Approach:	They propose a unified and lightweight framework that integrates 27 benchmarks under a standard ISO 639-3 language identifier system to enable seamless incorporation of new benchmarks.
Outcome:	The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks.

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Jiakun Li, Xingwei He, Kefan Li, Hongzheng Chai, Hongyue Yu, Yuan Yuan

Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)

Challenge:	Existing dynamic early-exit methods rely on single-step confidence signals . existing approaches are unreliable for detecting reasoning convergence in multi-step settings .
Approach:	They propose a training-free framework for efficient test-time scaling that determines when to terminate reasoning based on temporal aggregation of multi-step evidence rather than instantaneous signals.
Outcome:	Experiments show that TRACE reduces reasoning token usage by 25% on average while maintaining accuracy within 1–2% of full-length reasoning.

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Pengqi Li, Lizhong Ding, Zhehao Zhou, Chunhui Zhang, Jiarun Fu, Hao Li, Ye Yuan, Guoren Wang

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

Challenge:	Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment.
Approach:	They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error.
Outcome:	The proposed method provides theoretical guarantees and empirical gains for reliability.

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

Low-Resource NLP

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)

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Xu Huang, Wenhao Zhu, Hanxu Hu, Conghui He, Lei Li, Shujian Huang, Fei Yuan

SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning (2026.acl-long)

Challenge:	Existing multilingual benchmarks focus primarily on language understanding tasks.
Approach:	They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages.
Outcome:	Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve.

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Peidong Wang, Zhiming Ma, Xin Dai, YongKang Liu, Shi Feng, Xiaocui Yang, Wenxing Hu, Zhihao Wang, Mingjun Pan, Li Yuan, Daling Wang

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing (2025.naacl-long)

Challenge:	Existing methods for fraud detection rely on transcribed text, lacking acoustic cues . a proposed framework for audio-based slow-thinking fraud detection eliminates transcription errors .
Approach:	They propose a framework for audio-based slow-thinking fraud detection that eliminates transcription errors and rewards slow-thought reasoning by capturing fine-grained audio details.
Outcome:	The proposed method improves accuracy, inference efficiency, and real-time processing capabilities.

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Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang, Eunhye Grace Ko, Lily Boddy, Chenan Wang, Tianhao Li, Alexander Rasgon, Junyuan Hong, Min Kyung Lee, Chenxi Yuan, Qi Long, Ying Ding, Tianlong Chen, Kaidi Xu

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated their effectiveness in human-guided dialogues, but tasks in the real world are more complex and require greater autonomy from LLMs.
Approach:	They propose to characterize LLM-guided conversation into three fundamental components: Goal Navigation, Context Management, Empathetic Engagement and implement an interviewing environment for the evaluation of LLMs.
Outcome:	The proposed LLM outperforms baseline LLMs in interviewing quality and autobiography generation quality.

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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.

Few-shot Intent Classification and Slot Filling with Retrieved Examples (2021.naacl-main)

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Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, Qi Li

EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMs (2025.coling-demos)

Challenge:	Existing methods for few-shot learning are based on labeled examples, but they are non-trivial . few-sshot learning is challenging due to the imbalance in the amount of data between the source and target domains.
Approach:	They propose retrieval-based methods for intent classification and slot filling tasks . they use a batch-softmax objective to learn similar contextualized representations for spans .
Outcome:	The proposed method outperforms previous systems on the CLINC and SNIPS benchmarks.

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Yijie Li, Yuan Sun

UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt (2024.lrec-main)

Challenge:	Existing open-source evaluation models lack a user-friendly visualization tool and are not optimized for accelerated model inference.
Approach:	They propose to use open-source evaluation models to evaluate language model responses.
Outcome:	The proposed model is lightweight, precise, efficient, and user-friendly, with an intuitive visualization interface for ease of deployment and use.

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Yucheng Cai, Wentao Ma, Yuchuan Wu, Shuzheng Si, Yuan Shao, Zhijian Ou, Yongbin Li

LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)

Challenge:	Recent studies have shown that multi-task instruction tuning after pre-training greatly improves the model’s robustness and transfer ability, which is crucial for building a high-quality dialog system.
Approach:	They propose to use Task-aware Automatic Prompt generation (TAP) to automatically generate high-quality prompts from 15 dialog-related tasks.
Outcome:	The proposed model is robust to input prompts and capable of various dialog-related tasks.

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Zelong Yu, Xiaoming Zhang, Litian Zhang, Yu Yuan, Chaozhuo Li

Graph-Based Chain-of-Thought Pruning for Reducing Redundant Reflections in Reasoning LLMs (2026.findings-acl)

Challenge:	Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models.
Approach:	They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness.
Outcome:	The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model.

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Hongyuan Yuan, Xinran He, Run Shao, Bolei He, Xianwei Xue, Mengke Chen, Qiutong Pan, Haiwei Wang, Haifeng Li

Structured Data in NLP

Challenge:	Extending CoT through RL can induce undesirable thinking patterns such as overthinking . prior work has focused on inefficient reflection, which manifests in two problematic patterns: Indiscriminate Reflection and Repetitive Reflectione .
Approach:	They propose a graph-based approach to optimize CoT by pruning each linear CoT into a directed acyclic graph with explicit dependency edges.
Outcome:	The proposed approach reduces the average reasoning tokens by 42% while maintaining or improving accuracy.

QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining (2021.acl-short)

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Xinya Du, Luheng He, Qi Li, Dian Yu, Panupong Pasupat, Yuan Zhang

Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations (2023.emnlp-main)

Challenge:	Existing methods to predict slots and their values do not encode enough semantic information, limiting the models’ zero-shot capability.
Approach:	They propose a QA-driven slot filling model which extracts slot-filler spans from utterances with a span-based QA model.
Outcome:	The proposed model outperforms baselines by over 5% on the SNIPS benchmark.

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Yuan Tian, Zheng Zhang, Zheng Ning, Toby Li, Jonathan K. Kummerfeld, Tianyi Zhang

Programming Languages in NLP

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.

PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models (2024.acl-long)

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Haoran Li, Dadi Guo, Donghao Li, Wei Fan, Qi Hu, Xin Liu, Chunkit Chan, Duanyi Yao, Yuan Yao, Yangqiu Song

Generative Dense Retrieval: Memory Can Be a Burden (2024.eacl-long)

Challenge:	generative large language models (LLMs) exhibit surprising capability and integrate previous tasks into a unified text generation formulation.
Approach:	They propose a privacy evaluation benchmark to quantify the privacy leakage of language models.
Outcome:	The proposed benchmark compares PPLMs with different privacy implementations to find out how privacy leakage is handled.

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Peiwen Yuan, Xinglin Wang, Shaoxiong Feng, Boyuan Pan, Yiwei Li, Heda Wang, Xupeng Miao, Kan Li

Text Clustering

Challenge:	Empirical results show that Generative Dense Retrieval (GDR) achieves an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability.
Approach:	They propose a Generative Dense Retrieval paradigm that auto-decodes document identifiers given a query and uses memory to avoid memory confusion.
Outcome:	Empirical results show that the proposed paradigm improves on the small-scale corpora and improves scalability.

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback (2026.acl-long)

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Qiushi Sun, Jingyang Gong, Lei Li, Qipeng Guo, Fei Yuan

Challenge:	Existing methods for generating instruction-code pairs rely on rigid heuristics and are labor-intensive.
Approach:	They propose a dual-agent architecture that integrates a Coder and a Reviewer to orchestrate the generation trajectory.
Outcome:	The proposed architecture outperforms baselines on a large-scale dataset of instruction-code pairs with stepped difficulty levels.

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)

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Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, Yu Li

Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)

Challenge:	Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness.
Approach:	They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance.
Outcome:	Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality.

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Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)

Challenge:	Existing studies focus on predicting the four elements in one shot, instead of predicting them all.
Approach:	They propose a task to jointly detect all sentiment elements in quads for a given opinionated sentence.
Outcome:	The proposed method can generate the semantics of the sentiment elements in the natural language form.

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Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken

Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark (2023.findings-acl)

Challenge:	EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable.
Approach:	They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories .
Outcome:	The proposed benchmark consists of 2400 program pairs across four languages and six categories.

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Yuxing Long, Binyuan Hui, Caixia Yuan, Fei Huang, Yongbin Li, Xiaojie Wang

A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)

Challenge:	Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario.
Approach:	They propose a multimodal task-oriented dialog dataset with subjective preferences and recommendation acts that is well-annotated with sales experts.
Outcome:	The proposed model is powered by a state-of-the-art multimodal model for these tasks.

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Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)

Challenge:	Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings.
Approach:	They propose a method to generate unsupervised alignment seeds using temporal information from TKGs.
Outcome:	The proposed method outperforms the previous methods by using temporal information.

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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

Poor-Supervised Evaluation for SuperLLM via Mutual Consistency (2024.findings-acl)

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.

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Peiwen Yuan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Boyuan Pan, Heda Wang, Yao Hu, Kan Li

RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)

Challenge:	evaluating superLLMs is especially difficult because of their intelligence-intensive nature.
Approach:	They propose an evaluation benchmark with accurate labels for SuperLLMs whose capabilities surpass those of humans . they first prove that consistency between model under evaluation and reference model can equalize the true capabilities of the model to be evaluated .
Outcome:	The proposed evaluation benchmarks can assess the true capabilities of the model to be evaluated without accurate labels.

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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li

Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models (2021.findings-acl)

Challenge:	Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
Approach:	They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone .
Outcome:	Experiments on four TVR datasets show that the proposed method performs better than other methods.

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Junyi Li, Tianyi Tang, Wayne Xin Zhao, Zhicheng Wei, Nicholas Jing Yuan, Ji-Rong Wen

Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)

Challenge:	Existing models for KG-to-text generation are based on pretrained language models.
Approach:	They propose to automatically generate a text that describes the facts in knowledge graph (KG) they leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation.
Outcome:	The proposed model outperforms all comparison methods on fully-supervised and fewshot settings.

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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong

Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)

Challenge:	Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency.
Approach:	They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs.
Outcome:	The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks.

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Zhepei Wei, Yue Wang, Jinnan Li, Zhining Liu, Erxin Yu, Yuan Tian, Xin Wang, Yi Chang

Is Parameter Collision Hindering Continual Learning in LLMs? (2025.coling-main)

Challenge:	Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods.
Approach:	They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models.
Outcome:	Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data .

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Shuo Yang, Kun-Peng Ning, Yu-Yang Liu, Jia-Yu Yao, Yong-Hong Tian, Yi-Bing Song, Li Yuan

Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation (2024.acl-long)

Challenge:	Existing methods to learn multiple tasks in parallel often lead to catastrophic forgetting, resulting in overwriting knowledge.
Approach:	They propose a non-collision low-rank Adaptation approach that leverages low collision rates to enhance continual learning (CL) in large language models.
Outcome:	The proposed approach achieves better task orthogonality and higher task orthognality than existing SOTA methods.

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Wenting Chen, Linlin Shen, Jingyang Lin, Jiebo Luo, Xiang Li, Yixuan Yuan

DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)

Challenge:	Fine-grained vision-language models (VLMs) have been widely used for inter-modality local alignment between fixed patches and textual words, but they provide incomplete representations of lesions.
Approach:	They propose an Adaptive patch-word Matching model to correlate chest X-ray (CXR) image regions with words in medical reports and apply it to CXR-report generation to provide explicit explanations.
Outcome:	The proposed model correlates chest X-ray image regions with words in medical reports and provides explanations for the generation process.

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Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun

InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery (2025.coling-main)

Challenge:	Existing relation extraction methods focus on extracting intra-sentence relations for single entities.
Approach:	They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities .
Outcome:	The proposed dataset is the largest human-annotated dataset for document-level RE from plain text.

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He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, Yu Li