Papers by Chang Wu
Enhanced Metaphor Detection via Incorporation of External Knowledge Based on Linguistic Theories (2021.findings-acl)
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| Challenge: | Existing methods for metaphor detection take little consideration on linguistic theories of metaphor detection. |
| Approach: | They propose two BERT-based models for metaphor detection based on examples and definitions of words from the Oxford Dictionary. |
| Outcome: | The proposed models achieve state-of-the-art performance on two established metaphor datasets and are highly interpretable. |
Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning (2026.acl-long)
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| Challenge: | Recent reinforcement learning approaches have advanced radiology report generation (RRG) however, there are two limitations: report-level rewards offer limited evidence-grounded guidance for clinical faithfulness . |
| Approach: | They propose a method that uses group-wise evidence-aware alignment rewards and self-correcting preference learning to build a reliable, disease-agnostic preference dataset without human supervision. |
| Outcome: | ESC-RL promotes clinically faithful, disease-aligned reward and supports continual self-improvement during training. |
Representation Learning for Resource-Constrained Keyphrase Generation (2022.findings-emnlp)
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| Challenge: | State-of-the-art keyphrase generation methods depend on large annotated datasets, limiting their performance in domains with limited annotation data. |
| Approach: | They propose a method that first identifies salient information using retrieval-based corpus-level statistics and then learns a task-specific intermediate representation based on a pre-trained language model. |
| Outcome: | The proposed method improves keyphrase generation and zero-shot domain adaptation on multiple keyphrase benchmarks. |
Rethinking Data Selection at Scale: Random Selection is Almost All You Need (2025.findings-emnlp)
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| Challenge: | Existing data selection techniques are designed for small data pools, a study finds . filtering data by token length is an efficient method for improving results . |
| Approach: | They use self-scoring methods that do not rely on external help to perform fine-tuning . they also find that filtering data by token length offers a stable and efficient method . |
| Outcome: | The proposed methods outperform random selection on large datasets on large data pools. |
PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance (2026.eacl-demo)
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| Challenge: | PropGenie is a multi-agent framework based on large language models (LLMs) it provides comprehensive real estate assistance in real-world scenarios . |
| Approach: | They propose a multi-agent framework based on large language models to deliver comprehensive real estate assistance in real-world scenarios. |
| Outcome: | The proposed framework outperforms a general-purpose LLM and a domain-specific chatbot in real-world scenarios. |
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models (2024.findings-acl)
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Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin, Yuan-Kuei Wu, Xuanjun Chen, Yu-Chi Pai, Hsiu-Hsuan Wang, Kai-Wei Chang, Alexander Liu, Hung-yi Lee
| Challenge: | Researchers have developed a sound codec that can be used as tokenizers for preserving audio data and minimizing data transmission latency. |
| Approach: | They propose to use codec-SUPERB to assess codec models across representative sound applications and signal-level metrics rooted in sound domain knowledge. |
| Outcome: | The proposed codec-SUPERB model is evaluated on selected experimental settings. |
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction (2021.findings-acl)
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| Challenge: | Document-level relation extraction (doc-level RE) is a classification problem that predicts relations for all entity pairs in a document. |
| Approach: | They propose a document-level relation extraction architecture to represent intra- and inter-sentential relations in different ways. |
| Outcome: | The proposed architecture outperforms the state-of-the-art methods on the public datasets. |
AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training (2026.findings-acl)
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| 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. |
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression (2025.findings-naacl)
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| Challenge: | Existing approaches to augment language models with external knowledge but they are limited by static nature of pre-training data. |
| Approach: | They propose a lightweight approach that compresses retrieved documents into highly dense textual summaries to integrate into in-context RAG. |
| Outcome: | The proposed approach reduces latency and costs while achieving high performance in open-domain questions. |
Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability (2026.acl-long)
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Xiao Liang, Zhong-Zhi Li, Zhenghao Lin, Eric Hanchen Jiang, Hengyuan Zhang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Yeyun Gong, Weizhu Chen
| Challenge: | Large language models have demonstrated strong reasoning capabilities through step-by-step chain-of-thought (CoT) reasoning, but their strictly sequential nature constrains test-time scalability. |
| Approach: | They propose an end-to-end reinforcement learning framework to enhance LLMs' DAC-style reasoning capacity by decomposing a problem into subproblems and solving them sequentially. |
| Outcome: | The proposed model surpasses CoT by 8.6% and 6.3% on competition-level benchmarks and is available at the [github.com/MasterVito/DAC-RL]. |
Visualizing Trends of Key Roles in News Articles (D19-3)
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| Challenge: | a demonstration system visualizes news trend of key roles based on natural language processing techniques . semantic role labelling and word embeddings can help users understand news topics . |
| Approach: | They propose a system that visualizes the news trend of key roles based on natural language processing techniques. |
| Outcome: | The proposed system analyzes the news trend of key roles using semantic role labelling . it also analyzes how similarities between key roles and news topics change over time . |
Can We Edit Factual Knowledge by In-Context Learning? (2023.emnlp-main)
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| Challenge: | In-context knowledge editing (IKE) is a new paradigm for NLP research that can be applied to large language models with tens or hundreds of parameters. |
| Approach: | They propose to use in-context knowledge editing (IKE) without gradient updating to edit factual knowledge without a gradient update. |
| Outcome: | The proposed method achieves a competitive success rate compared to gradient-based methods on GPT-J but with fewer side effects. |
QBridge: Bridging Natural Language and SQL via Gold Query Rewriting with Agentic Refinement (2026.acl-long)
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| Challenge: | Natural language to SQL (NL2SQL) is an intuitive interface for querying structured data . but real user questions are noisy, ambiguous, and weakly grounded to database semantics. |
| Approach: | They propose an agentic feedback-driven NL2SQL framework that bridges natural language and SQL via Gold Query. |
| Outcome: | The proposed framework outperforms strong prompting and agentic baselines on spider, BIRD, and three robustness variants on NL2SQL. |
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference (2025.emnlp-main)
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| Challenge: | Low-rank compression can reduce memory usage and computational demand, but results are poor during decoding. |
| Approach: | They propose a fine-grained low-rank compression algorithm that determines optimal rank allocation for each layer and incorporates progressive low-ranked decoding to maintain text generation quality. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on summarization tasks and on understanding tasks. |
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation (2024.lrec-main)
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| Challenge: | a new study examines the use of encoder-only pre-trained language models in keyphrase generation (KPG) keyphrases are phrases that condense salient information of a document. |
| Approach: | They propose to use encoder-only pre-trained language models in keyphrase generation . they also examine optimal architectural decisions for employing encoder only PLMs in KPG . |
| Outcome: | The proposed model outperforms general-domain seq2seq models in keyphrase generation. |
Listen, Decipher and Sign: Toward Unsupervised Speech-to-Sign Language Recognition (2023.findings-acl)
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Liming Wang, Junrui Ni, Heting Gao, Jialu Li, Kai Chieh Chang, Xulin Fan, Junkai Wu, Mark Hasegawa-Johnson, Chang Yoo
| Challenge: | Existing supervised sign language recognition systems rely on well-annotated data . instead, an unsupervised speech-to-sign language recognition system learns to translate between spoken and sign languages by observing only non-parallel speech and sign-language corpora. |
| Approach: | They propose an unsupervised speech-to-sign language recognition system that can translate between spoken and sign languages by observing only non-parallel speech and sign-language corpora. |
| Outcome: | The proposed approach outperforms baseline models on sign language corpora by 50% . the proposed approach is available at https://github.com/cactuswiththoughts/UnsupSpeech2Sign.git . |
PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment (2026.findings-acl)
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| Challenge: | Existing benchmarks lack systematic approaches to integrate philosophical frameworks and expert validation for ethical reasoning assessment. |
| Approach: | They propose a philosophy-grounded approach to assess medical ethics alignment . PrinciplismQA comprises 3,648 expert-validated questions spanning knowledge assessment and clinical reasoning . |
| Outcome: | PrinciplismQA provides a philosophy-grounded approach to assessing medical ethics alignment. |
VDebugger: Harnessing Execution Feedback for Debugging Visual Programs (2024.findings-emnlp)
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| Challenge: | Visual programs are executable code generated by large language models to address visual reasoning problems. |
| Approach: | They propose a critic-refiner framework that localizes and debugs visual programs by tracking execution step by step. |
| Outcome: | The proposed framework detects and corrects program errors leveraging detailed execution feedback, improving interpretability and accuracy. |
Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)
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| Challenge: | Existing models for comprehensive descriptions for factual attribute-value tables might suffer from missing key attributes and groundless information problems. |
| Approach: | They propose a force attention method to encourage the generator to pay more attention to uncovered attributes to avoid potential key attributes missing. |
| Outcome: | The proposed model outperforms the state-of-the-art baselines on automatic and human evaluation. |
T2DR: A Two-Tier Deficiency-Resistant Framework for Incomplete Multimodal Learning (2025.findings-acl)
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| Challenge: | Existing incomplete multimodal learning frameworks are inadequate for integrating multimodal data. |
| Approach: | They propose a framework for incomplete multimodal learning that is deficiency-resistant and provides two modules to address fine-grained deficiencies. |
| Outcome: | The proposed framework outperforms the SOTA models on two well-known multimodal benchmarks. |
Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration (2026.findings-acl)
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Ryan Soh-Eun Shim, Kwanghee Choi, Kalvin Chang, Ming-Hao Hsu, Florian Eichin, Zhizheng Wu, Alane Suhr, Michael A. Hedderich, David Harwath, David R. Mortensen, Barbara Plank
| Challenge: | We show that script information is linearly encoded in the activation space of multilingual speech models . modifying activations at inference time induces script change even in unconventional pairings . |
| Approach: | They propose to add script vectors to activations at test time to induce script change . they also show that script information is linearly encoded in the activation space of multilingual speech models . |
| Outcome: | The proposed approach can induce script change even in unconventional language-script pairings. |
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026.acl-long)
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Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang
| Challenge: | Existing studies on robustness to explicit noise (e.g., document semantics) but overlook implicit noise (spurious features). |
| Approach: | They propose a framework to quantify the robustness of RAGs against spurious features by integrating a data synthesis pipeline and a taxonomy. |
| Outcome: | The proposed framework quantifies the robustness of RALMs against spurious features. |
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment. |
| Approach: | They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition. |
| Outcome: | The proposed framework aligns knowledge complexity and presentation style with user cognition. |
MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)
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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang
| Challenge: | Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs. |
| Approach: | They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair. |
| Outcome: | The proposed framework integrates graphical information of two molecules in pair. |
Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks (2023.emnlp-main)
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| Challenge: | Instruction tuning (IT) achieves impressive zero-shot generalization results by training large language models on diverse tasks with instructions. |
| Approach: | They propose a framework to identify informative tasks and then actively tune models on selected tasks. |
| Outcome: | The proposed method outperforms baseline strategies for task selection on NIV2 and Self-Instruct datasets. |
TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review (2025.emnlp-main)
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Yuan Chang, Ziyue Li, Hengyuan Zhang, Yuanbo Kong, Yanru Wu, Hayden Kwok-Hay So, Zhijiang Guo, Liya Zhu, Ngai Wong
| 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. |
Towards Zero-Shot Multilingual Transfer for Code-Switched Responses (2023.acl-long)
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| Challenge: | Recent task-oriented dialog systems have had great success building English-based personal assistants, but extending these systems to a global audience may take tremendous efforts. |
| Approach: | They propose a framework that allows for efficient transfer by learning task-specific representations and encapsulating source and target language representations. |
| Outcome: | The proposed framework is able to successfully transfer language knowledge even when the target language corpus is limited. |
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models (2023.emnlp-main)
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| Challenge: | Keyphrase generation is a longstanding task in NLP with widespread applications. |
| Approach: | They propose a likelihood-based decode-select algorithm for seq2seq PLMs that improves greedy search by an average of 4.7% semantic F1 across five datasets. |
| Outcome: | The proposed algorithm improves greedy search by an average of 4.7% semantic F1 across five datasets. |
StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following (2025.findings-acl)
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| 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. |
MACAROON: Training Vision-Language Models To Be Your Engaged Partners (2024.findings-emnlp)
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| Challenge: | Large vision-language models (LVLMs) generate detailed responses even when questions are ambiguous or unanswerable, leading to hallucinations and bias issues. |
| Approach: | They propose a three-tiered hierarchy for questions of invalid, ambiguous, and personalizable nature to measure the proactive engagement capabilities of LVLMs. |
| Outcome: | The proposed model generates contrastive response pairs for unlabeled questions, achieving 0.84 AAR, while maintaining comparable performance on general tasks. |
Large Language Model Evaluation via Matrix Nuclear-Norm (2025.findings-emnlp)
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| 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 . |
Genius: A Generalizable and Purely Unsupervised Self-Training Framework For Advanced Reasoning (2025.acl-long)
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Fangzhi Xu, Hang Yan, Chang Ma, Haiteng Zhao, Qiushi Sun, Kanzhi Cheng, Junxian He, Jun Liu, Zhiyong Wu
| Challenge: | Existing methods for enhancing LLM reasoning rely on supervisory signals . current methods rely heavily on outcome supervision and auxiliary reward models . |
| Approach: | They propose a gen-eralizable and purely unsupervised self-training framework to enhance LLM reasoning without supervision. |
| Outcome: | The proposed framework improves LLM reasoning without supervision without external supervision. |
BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning (2026.findings-acl)
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| Challenge: | Experiments show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
| Approach: | They propose a universal, lightweight compressor that distills relevant evidence from retrieved documents into a concise summary for seamless integration into in-context RAG. |
| Outcome: | Experiments on four open-domain multi-hop question-answering datasets show that BRIEF-Pro generates more concise and relevant summaries, enhancing performance across small, large, and proprietary language models. |
SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024.emnlp-demo)
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Dayong Wu, Jiaqi Li, Baoxin Wang, Honghong Zhao, Siyuan Xue, Yanjie Yang, Zhijun Chang, Rui Zhang, Li Qian, Bo Wang, Shijin Wang, Zhixiong Zhang, Guoping Hu
| Challenge: | Large language models (LLMs) have shown remarkable achievements across various language tasks. |
| Approach: | They propose a scientific literature LLM and a knowledge service system based on it . they collect scientific literature and then pre-train it using autoregressive training . |
| Outcome: | The proposed system provides literature investigation, paper reading, and academic writing functions. |
Query-Driven Multimodal GraphRAG: Dynamic Local Knowledge Graph Construction for Online Reasoning (2025.findings-acl)
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| Challenge: | Existing approaches to build knowledge graphs with LLMs are constrained by static knowledge bases and ineffective multimodal data integration. |
| Approach: | They propose a Query-Driven Multimodal GraphRAG framework that dynamically constructs local knowledge graphs tailored to query semantics. |
| Outcome: | The proposed framework outperforms unsupervised competitors in cross-modal understanding of complex queries. |
Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models (2026.acl-long)
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Eric Hanchen Jiang, Levina Li, Frank Wan, Xiao Liang, Sophia Yin, Yuchen Wu, Xinfeng Li, Yizhou Sun, Wei Wang, Kai-Wei Chang, Ying Nian Wu
| Challenge: | Existing frameworks rely on static or rule-based topologies that fail to adapt to task requirements. |
| Approach: | They propose a generative framework that generates highly task-adaptive topologies . they validated the framework on multiple benchmarks and validated it on multiple platforms . |
| Outcome: | The proposed framework outperforms existing frameworks in task-adaptive communication topologies. |
Mitigating Over-Refusal in Aligned Large Language Models via Inference-Time Activation Energy (2026.acl-long)
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Eric Hanchen Jiang, Weixuan Ou, Run Liu, Shengyuan Pang, Guancheng Wan, Ranjie Duan, Wei Dong, Kai-Wei Chang, XiaoFeng Wang, Ying Nian Wu, Xinfeng Li
| Challenge: | Existing safety alignment techniques prioritize mitigating harmful responses at the expense of overcautious behavior, leading models to incorrectly refuse benign requests. |
| Approach: | They propose a fine-tuning free framework to improve safety and reduce false refusals by dynamic, inference-time intervention. |
| Outcome: | The proposed framework raises compliance on the ORB-H benchmark from 57.3% to 82.6% while maintaining the baseline safety performance. |
Training-free LLM Merging for Multi-task Learning (2025.acl-long)
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Zichuan Fu, Xian Wu, Yejing Wang, Wanyu Wang, Shanshan Ye, Hongzhi Yin, Yi Chang, Yefeng Zheng, Xiangyu Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing tasks. |
| Approach: | They propose a training-free method for unifying different specialized LLMs into a single model using model-wise and layer-wise pruning and scaling. |
| Outcome: | The proposed method outperforms existing merging techniques and surpasses models fine-tuned on combined datasets in most scenarios. |
BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards (2026.findings-acl)
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| Challenge: | Critic-free reinforcement learning with verifiable rewards (RLVR) is a practical paradigm for aligning Large Language Models. |
| Approach: | They propose a framework that stabilizes advantage estimation by combining prompt-local on-policy statistics with semantic-cluster-conditioned historical moments. |
| Outcome: | Experiments show that RLVR improves training stability and performance compared to critic-based methods . compared with other approaches, RL VR improves in cold-start regimes with binary verifiers . |
Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)
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| Challenge: | Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data. |
| Approach: | They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data. |
| Outcome: | The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design. |
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
| 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. |
Re-ReST: Reflection-Reinforced Self-Training for Language Agents (2024.emnlp-main)
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| Challenge: | Existing methods to fine tune language agents with reasoning-action trajectories require high-quality model-generated samples, which are hard to obtain for challenging language agent tasks. |
| Approach: | They propose a method to employ reflection during inference without ground-truth feedback to improve agents more autonomously. |
| Outcome: | The proposed method improves self-training performance on open-source language agents by 7.6% and 14.1% respectively. |
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention (2024.emnlp-main)
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| Challenge: | Prompt-based “diversity interventions” are commonly adopted to improve the diversity of Text-to-Image models depicting individuals with diverse racial or gender traits. |
| Approach: | They propose a benchmark to quantify the trade-off between using diversity interventions and preserving demographic factuality in T2I models. |
| Outcome: | The proposed model significantly improves the demographic factuality under diversity interventions while preserving diversity. |
Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs (2026.findings-eacl)
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Paiheng Xu, Gang Wu, Xiang Chen, Tong Yu, Chang Xiao, Franck Dernoncourt, Tianyi Zhou, Wei Ai, Viswanathan Swaminathan
| Challenge: | Large Language Models (LLMs) can generate code from natural language queries, but runtime code generation is limited due to unverified code, security risks, longer response times, and higher computational costs. |
| Approach: | They propose an offline simulation framework to curate a software-specific skillset by exploiting large language models and publicly available scripting guides. |
| Outcome: | The proposed framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. |
SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs (2026.findings-acl)
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| Challenge: | Existing black-box instruction backdoors can detect poisoned inputs, but fail to recover correct outputs once the backdoor is activated. |
| Approach: | They propose a soft label mechanism and key-extraction-guided CoT-based defense against instruction backdoors in APIs (SLIP) they propose KCOT-based model to extract task-relevant keywords and phrases rather than only considering the single trigger or overall text semantics. |
| Outcome: | The proposed model reduces the average attack success rate to 25.13% and improves clean accuracy to 87.15% and outperforms state-of-the-art black-box defenses. |
MatchTime: Towards Automatic Soccer Game Commentary Generation (2024.emnlp-main)
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| Challenge: | Existing data on soccer commentary are often unsatisfactory, and the quality of existing data is often poor. |
| Approach: | They propose to manually annotate timestamps for 49 soccer matches and then use them to create a model to correct and filter existing data. |
| Outcome: | The proposed model improves the viewing experience of soccer and can be trained on the curated dataset. |
A Survey on In-context Learning (2024.emnlp-main)
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Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang, Xu Sun, Lei Li, Zhifang Sui
| Challenge: | In-context learning (ICL) is a new paradigm for natural language processing . large language models (LLMs) demonstrate the ability to learn from a few examples . |
| Approach: | They propose to explore ICL to evaluate and extrapolate the ability of large language models. |
| Outcome: | The proposed methods can be used to evaluate and extrapolate the ability of large language models. |
LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization (2025.findings-emnlp)
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| Challenge: | Low-Rank Adaptation (LoRA) adapts large language models by training only a small fraction of parameters, but as the rank of the low-rank matrices increases, LoRA exhibits an unstable “double descent” phenomenon, which delays convergence and impairs generalization by causing instability due to the attraction to sharp local minima. |
| Approach: | They propose a framework that incorporates Momentum-Guided Perturbation Optimization (MGPO) MGPO stabilizes training dynamics by mitigating double descent phenomenon and guiding weight perturbations using momentum vectors from the optimizer’s state. |
| Outcome: | The proposed framework improves performance on natural language understanding benchmarks and shows that it improves convergence and generalization. |
E-EVAL: A Comprehensive Chinese K-12 Education Evaluation Benchmark for Large Language Models (2024.findings-acl)
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Jinchang Hou, Chang Ao, Haihong Wu, Xiangtao Kong, Zhigang Zheng, Daijia Tang, Chengming Li, Xiping Hu, Ruifeng Xu, Shiwen Ni, Min Yang
| Challenge: | despite the rapid development of Large Language Models, there is no dedicated benchmark for evaluating LLMs in Chinese K-12 education. |
| Approach: | They propose to develop a benchmark specifically tailored for Chinese K-12 education. |
| Outcome: | EVAL is the first evaluation benchmark specifically tailored for Chinese K-12 education. |
KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation (2024.findings-acl)
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| Challenge: | Existing evaluation methods for keyphrase extraction and generation rely on exact matching with human references. |
| Approach: | They propose a framework for evaluation that includes four critical aspects: reference agreement, faithfulness, diversity, utility and semantic-based metrics. |
| Outcome: | The proposed evaluation framework correlates better with human preferences than previously proposed metrics. |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
MetaKP: On-Demand Keyphrase Generation (2024.findings-emnlp)
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| Challenge: | Existing keyphrase prediction methods only output a single set of keyphrases per document . however, existing methods fail to cater to diverse needs of users and downstream applications . |
| Approach: | They propose a method that requires keyphrases that conform to specific high-level goals or intents to generate on-demand keyphrase generation. |
| Outcome: | The proposed method surpasses the performance of a fully fine-tuned BART-base model in 0.548 SemF1 . it can be used in epidemic event detection from social media. |
A Survey of Retentive Network (2026.findings-acl)
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| 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. |
Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)
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| Challenge: | Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated. |
| Approach: | They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text. |
| Outcome: | The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods. |
Language Models can Evaluate Themselves via Probability Discrepancy (2024.findings-acl)
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| Challenge: | Existing evaluation frameworks focus on superficial text differences and fail to align with human judgment. |
| Approach: | They propose a new method to evaluate the performance of Large Language Models (LLMs) by calculating probability discrepancies between original response generation and revised versions of LLMs. |
| Outcome: | The proposed method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger. |
Closing the Spatial Execution Gap in Digital Whiteboards via Verifiable Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models suffer from a fundamental Spatial Execution Gap, failing to translate visual semantics into precise, schema-valid coordinate operations in interactive environments. |
| Approach: | They propose a pipeline that leverages Group Relative Policy Optimization to enforce a strict Identify-Reason-Verify protocol and train on execution-verifiable rewards. |
| Outcome: | The proposed pipeline outperforms a state-of-the-art frontier model by 16.75% in operation accuracy. |
SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method for large language models. |
| Approach: | They propose a drop-in extension that reparameterizes a rank-rtot update as a sum of K *static* low-rank experts. |
| Outcome: | Experiments on reasoning and knowledge-intensive benchmarks show consistent gains over matched-budget LoRA. |
ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)
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| Challenge: | Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs. |
| Approach: | They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems. |
| Outcome: | The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions. |
Open-Domain Safety Policy Construction (2026.findings-eacl)
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| Challenge: | Moderation layers are core component of many products built on user-generated content. |
| Approach: | They propose a system that drafts a content moderation policy based on human-written seed domain information. |
| Outcome: | The proposed system outperforms definition-only and in-context learning baselines on openAI undesired content benchmarks and an in-house multimodal advertisement moderation benchmark. |
DiMo-GUI: Advancing Test-time Scaling in GUI Grounding via Modality-Aware Visual Reasoning (2025.emnlp-main)
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| Challenge: | DiMo-GUI is a training-free framework for GUI grounding that splits input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. |
| Approach: | They propose a training-free framework for GUI grounding that leverages two core strategies: dynamic visual grounding and modality-aware optimization. |
| Outcome: | The proposed framework splits the input into textual elements and iconic elements, allowing the model to reason over each modality independently using general-purpose vision-language models. |
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing (N19-1)
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| Challenge: | Existing entity typing systems exploit type hierarchy provided by KB schema to model label correlations. |
| Approach: | They propose a graph layer that encodes global label co-occurrence statistics and word-level similarities. |
| Outcome: | The proposed model achieves a 15.3% relative F1 improvement on a large dataset with over 10,000 free-form types. |
GAIA: A Fine-grained Multimedia Knowledge Extraction System (2020.acl-demos)
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Manling Li, Alireza Zareian, Ying Lin, Xiaoman Pan, Spencer Whitehead, Brian Chen, Bo Wu, Heng Ji, Shih-Fu Chang, Clare Voss, Daniel Napierski, Marjorie Freedman
| Challenge: | Open source knowledge extraction tools are used for many real-world applications, but there is no comprehensive system for KE. |
| Approach: | They propose a multimedia knowledge extraction system that takes multimedia data from various sources and languages as input and creates a coherent, structured knowledge base. |
| Outcome: | The system achieves top performance at the recent NIST TAC SM-KBP2019 evaluation. |
Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models (2025.findings-emnlp)
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| 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. |
Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions (2026.acl-long)
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Boyan Duan, Xiao Liang, Shuai Lu, Yaoxiang Wang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Mao Yang, Weizhu Chen, Yeyun Gong
| Challenge: | Existing methods for geometry theorem proving in Euclidean geometry are challenging and require a neural network to perform. |
| Approach: | They propose a method for adding auxiliary points in geometry that runs on CPUs without relying on neural network-based inference. |
| Outcome: | The proposed method achieves silver-medal-level human performance on IMO-30 benchmark. |
Graph Enhanced Cross-Domain Text-to-SQL Generation (D19-53)
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| Challenge: | Existing deep learning approaches for semantic parsing do not generalize to unseen data sets . existing benchmarks have shown text-to-SQL parsers do not generally perform well to unsen SQL queries. |
| Approach: | They propose a new cross-domain learning scheme to perform text-to-SQL translation . they demonstrate its use on a large-scale cross- domain text- to-Sql data set Spider . |
| Outcome: | The proposed learning scheme improves on a large-scale text-to-SQL data set. |
T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)
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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li
| Challenge: | Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications. |
| Approach: | They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning. |
| Outcome: | The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation. |
TWEETQA: A Social Media Focused Question Answering Dataset (P19-1)
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Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
| Challenge: | Social media is becoming an important realtime information source, especially during natural disasters and emergencies. |
| Approach: | They present a large-scale dataset for question answering over social media data . they gather tweets used by journalists and ask human annotators to write questions upon them . |
| Outcome: | The proposed dataset shows that neural models that perform well on formal texts are limited in their performance . the proposed model is still lagging behind human performance with a large margin . |
Development and Validation of a Corpus for Machine Humor Comprehension (2020.lrec-1)
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| Challenge: | a Chinese humor corpus was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Approach: | They develop a Chinese humor corpus with 3,365 jokes labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. |
| Outcome: | The proposed corpus contains 3,365 jokes from over 40 sources. |
Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)
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| Challenge: | Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model. |
| Approach: | They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models. |
| Outcome: | The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority. |
VisRet: Visualization Improves Knowledge-Intensive Text-to-Image Retrieval (2026.acl-long)
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| Challenge: | Text-to-image retrieval is challenging because of cross-modal embeddings are bags of concepts, underrepresenting structured visual relationships. |
| Approach: | They propose a retrieval paradigm that embeds textual queries into the image modality via T2I generation and performs retrieval within the image mode to bypass weaknesses of cross-modal retrievers in recognizing subtle visual-spatial features. |
| Outcome: | The proposed retrieval paradigm outperforms previous approaches in visual-spatial retrieval benchmarks. |
Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to RLVR provide sparse supervision since reward arrives only after the full generation is complete. |
| Approach: | They propose a step-level reward system that extracts confidence and correctness and combines them into a Step Potential signal that explicitly estimates reasoning state at each step. |
| Outcome: | The proposed method outperforms existing methods on multiple benchmarks and improves accuracy while reducing response length. |
Quantification of Large Language Model Distillation (2025.acl-long)
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Sunbowen Lee, Junting Zhou, Chang Ao, Kaige Li, Xeron Du, Sirui He, Haihong Wu, Tianci Liu, Jiaheng Liu, Hamid Alinejad-Rokny, Min Yang, Yitao Liang, Zhoufutu Wen, Shiwen Ni
| Challenge: | Existing studies have revealed the robustness degra-dation caused by data distillation. |
| Approach: | They propose a framework to evaluate and quantify model distillation . they aim to identify identity cognition contradictions and analyse multi-granularity response similarities across models to measure the extent of homogenization. |
| Outcome: | The proposed framework addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information; (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization. |
Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation (2024.emnlp-main)
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| Challenge: | Existing studies show that RALMs generate baseless information or contradicts with the retrieved context. |
| Approach: | They propose a lightweight monitor that leverages fine-grained decoding dynamics to synchronously detect unfaithful sentences. |
| Outcome: | Empirical results show that SynCheck outperforms baseline faithfulness detection and FOD outperformed traditional strategies in terms of faithfulness. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
| Challenge: | Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns. |
| Approach: | They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users. |
| Outcome: | The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks. |
𝜙-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation (2025.acl-long)
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| Challenge: | Existing inference-time optimization strategies address the shortsightedness of auto-regressive generation, but the vast search space leads to excessive exploration and insufficient exploitation. |
| Approach: | They propose a decoding strategy that approximates two distributions via foresight and clustering to provide an efficient estimation of step value. |
| Outcome: | The proposed decoding strategy outperforms strong baselines in performance and efficiency. |
Improving Context Fidelity via Native Retrieval-Augmented Reasoning (2025.emnlp-main)
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Suyuchen Wang, Jinlin Wang, Xinyu Wang, Shiqi Li, Xiangru Tang, Sirui Hong, Xiao-Wen Chang, Chenglin Wu, Bang Liu
| Challenge: | Existing approaches to fidelity to contexts rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without improving utilization of the given context. |
| Approach: | They propose a native retrieval-augmented reasoning framework that integrates in-context evidence with the model’s own retrieval capabilities. |
| Outcome: | The proposed approach outperforms supervised fine-tuning, retrieval-augmented generation methods, and external retrieval solutions on multiple real-world and counterfactual QA benchmarks. |
Think Earlier, Not Longer: Prompt Optimization via Reducing Unhealthy Exploration (2026.findings-acl)
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| Challenge: | Existing approaches to improve reasoning performance ignore the presence of unhealthy exploration that increases token usage without contributing to effective problem-solving. |
| Approach: | They propose an entropy-dynamics-aware prompt optimization framework that trains a lightweight optimizer to generate concise clarifications. |
| Outcome: | The proposed framework reduces ambiguity-induced early-stage uncertainty while preserving the model's reasoning capabilities. |
Learning from Children: Improving Image-Caption Pretraining via Curriculum (2023.findings-acl)
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| Challenge: | Image-caption pretraining is a difficult problem as it requires multiple concepts (nouns) from captions to be aligned to multiple objects in images. |
| Approach: | They propose a curriculum learning framework that uses images to align multiple concepts to multiple objects in an image. |
| Outcome: | The proposed learning framework improves over pretraining from scratch, using a pretrained image or/and text encoder, low data regime etc. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data (2024.findings-acl)
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| Challenge: | Quantitative reasoning with data is a critical skill to analyze data, yet the assessment of such ability remains limited. |
| Approach: | They propose a quantitative reasoning with data benchmark to evaluate Large Language Models' ability in statistical and causal reasoning with real-world data. |
| Outcome: | The proposed model GPT-4 achieves an accuracy of 58%, while open-source model Deepseek-coder-instruct gets the highest accuracy of 37%. |
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment (2024.lrec-main)
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| Challenge: | Current work on multi-modal semantic understanding primarily exploits a dual-encoder structure to separate image and text, but fails to learn cross-modal feature alignment. |
| Approach: | They propose a CLIP-guided contrastive-learning-based architecture to perform multi-modal feature alignment by projecting features from different modalities into a unified deep space. |
| Outcome: | The proposed model outperforms baseline models on sarcasm detection and sentiment analysis tasks and is simple to implement without using task-specific external knowledge. |
R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-task Learning (2025.findings-emnlp)
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| Challenge: | Low-Rank Adaptation (LoRA) improves performance in multi-task learning by diversifying the head matrices through Multi-Head Dropout and Multi-head Random Initialization. |
| Approach: | They propose a low-rank adaptive approach to fine-tune large language models by approximating weight updates through low-ranked matrices. |
| Outcome: | The proposed approach improves performance in multi-task learning while reducing memory usage and training time. |
NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models (2025.findings-emnlp)
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| Challenge: | Parameter-efficient fine-tuning is essential for adapting large language models (LLMs). However, LoRA suffers from slow convergence and some recent LoRA variants, such as PiSSA, rely on Singular Value Decomposition (SVD) for initialization. |
| Approach: | They propose to introduce a small intermediate matrix between the low-rank matrices (A) and (B) and propose NyströmLoRA (NLoRA) which leverages Nyström-based initialization for SLoRA to improve its effectiveness and efficiency. |
| Outcome: | The proposed approach improves on 5 natural language generation tasks and 8 natural language understanding tasks with minimal parameter overhead. |
Human-AI Collaboration: How AIs Augment Human Teammates (2025.acl-tutorials)
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| Challenge: | Despite the potential of general-purpose models, they are far from perfect, excelling at certain tasks while struggling with others. |
| Approach: | This tutorial will review recent developments related to human-AI teaming and collaboration. |
| Outcome: | This tutorial will review recent developments related to human-AI teaming and collaboration. |