Papers by Chang Lu
Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation (2025.findings-acl)
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| Challenge: | Existing methods to mitiga hallucinations rely on sampling multiple full-length generations, which introduces significant response latency and becomes ineffective when the model consistently produces hallucines. |
| Approach: | They propose a framework that dynamically monitors the generation process and selectively applies in-process interventions to revise hallucination-prone tokens. |
| Outcome: | The proposed framework outperforms self-consistency-based approaches in both effectiveness and efficiency, achieving higher factual accuracy while significantly reducing computational overhead. |
Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation (2025.emnlp-main)
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Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Baobao Chang
| Challenge: | Recent advances in retrieval-augmented generation (RAG) have substantially improved question-answering systems, particularly for factoid ‘5Ws’ questions. |
| Approach: | They propose a data organization paradigm where large language models transform documents into more structured and loosely interconnected LUs. |
| Outcome: | Experiments in open-domain and industrial settings show that the proposed paradigm outperforms existing paradigms and shows high adaptability across diverse document formats. |
Cross-media Structured Common Space for Multimedia Event Extraction (2020.acl-main)
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| Challenge: | We propose a new task to extract events and their arguments from multimedia documents . traditional methods target text, images or videos, but multimedia content is distributed via multimedia . |
| Approach: | They propose a method that encodes structured representations of semantic information from textual and visual data into a common embedding space. |
| Outcome: | The proposed method achieves 4.0% and 9.8% absolute gains on text event argument role labeling and visual event extraction. |
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing (2026.findings-acl)
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| Challenge: | Existing approaches to modifying large language models require continual updates to rectify outdated or erroneous knowledge. |
| Approach: | They propose a model editing strategy that mitigates catastrophic interference through sequential null-space alignment. |
| Outcome: | EvoEdit achieves better or comparable performance than prior state-of-the-art techniques with up to 3.53 speedup. |
No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery (2025.findings-emnlp)
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| Challenge: | Deep learning models lacking interpretability and interactivity, authors say . lack of interactive mechanisms prevents clinicians from incorporating their own knowledge into decision-making process. |
| Approach: | a new deep learning model is proposed to improve interpretability and interactivity . authors propose a knowledge-enhanced agent-driven causal discovery framework . |
| Outcome: | a new model improves interpretability and interactivity on EHR data . the proposed model improve interpretability through explicit reasoning and causal analysis . |
Entity-aware Image Caption Generation (D18-1)
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| Challenge: | Existing image captioning approaches generate generic descriptions of visual content and ignore background information. |
| Approach: | They propose a task which generates informative image captions using images and hashtags as input. |
| Outcome: | The proposed model outperforms unimodal baselines significantly with evaluation metrics on a dataset from Flickr. |
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. |
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. |
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)
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Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang
| Challenge: | Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions. |
| Approach: | They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm. |
| Outcome: | The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model. |
JoPA: Explaining Large Language Model’s Generation via Joint Prompt Attribution (2025.acl-long)
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| Challenge: | Existing attempts to explain the entire language generation often treat input prompt texts independently, ignoring their combinatorial effects on the follow-up generation. |
| Approach: | They propose a framework for explaining how a few prompt texts collaboratively influences the LLM's complete generation. |
| Outcome: | The proposed explanations demonstrate faithfulness and efficiency of the proposed framework. |
Rethinking Why Intermediate-Task Fine-Tuning Works (2021.findings-emnlp)
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| Challenge: | Pretrained language models can be fine-tuned on intermediate labeled-data tasks before fine- tuning the models on the target task of interest. |
| Approach: | They conduct extensive experiments to study the impact of different factors on STILT . they find that the improvement from an intermediate task could be orthogonal to it containing reasoning or other complex skills. |
| Outcome: | The proposed method improves the performance of pretrained language models on various target tasks. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)
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| Challenge: | Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost. |
| Approach: | They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages . |
| Outcome: | The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%. |
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following (2023.emnlp-main)
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| Challenge: | Embodied Instruction Following has shown an impressive success rate when the environment has been seen in training, but when deployed in an unseen environment, it tends to struggle when deployed with an unsightly environment. |
| Approach: | They propose to explicitly align the agent’s hidden states with the instructions via contrastive learning to bridge the semantic gap between high-level language instructions and the agent's low-level action space. |
| Outcome: | The proposed meta-actions achieve a 4.5% success rate in unseen environments compared to a strong multi-modal Transformer baseline . |
Speculative Contrastive Decoding (2024.acl-short)
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| Challenge: | Large language models (LLMs) exhibit exceptional performance in language tasks, yet their auto-regressive inference is limited due to high computational requirements and is sub-optimal due to the exposure bias. |
| Approach: | They propose a decoding approach that leverages predictions from smaller language models to achieve both decoding acceleration and quality improvement. |
| Outcome: | The proposed method achieves both decoding acceleration and quality improvement on four diverse language tasks. |
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. |
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition (2024.acl-long)
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Guanting Dong, Hongyi Yuan, Keming Lu, Chengpeng Li, Mingfeng Xue, Dayiheng Liu, Wei Wang, Zheng Yuan, Chang Zhou, Jingren Zhou
| 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. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
A Survey of Deep Learning for Mathematical Reasoning (2023.acl-long)
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| Challenge: | a survey of deep learning for mathematical reasoning examines the field . a comprehensive reading list is provided to assist readers interested in the field. |
| Approach: | They present a survey of deep learning for mathematical reasoning over the past decade . they outline directions for future research and highlight potential for further exploration . |
| Outcome: | The proposed framework is based on the results of a decade-long survey of deep learning for mathematical reasoning. |
Learning Global Controller in Latent Space for Parameter-Efficient Fine-Tuning (2024.acl-long)
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Zeqi Tan, Yongliang Shen, Xiaoxia Cheng, Chang Zong, Wenqi Zhang, Jian Shao, Weiming Lu, Yueting Zhuang
| Challenge: | Large language models (LLMs) have shown remarkable performance, but their training costs are exorbitant. |
| Approach: | They propose a parameter-efficient method for exploring optimal solutions within latent space by using latent units to extract input representations from LLMs. |
| Outcome: | The proposed method improves performance on a range of natural language processing tasks. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
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. |
Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model (2024.emnlp-main)
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| Challenge: | Existing approaches to fine tune LLMs produce unsafe responses and unreliable reasoning, but this solution introduces substantial time and space overhead due to the separate models required. |
| Approach: | They propose to insert extra parameters into transformer architecture to predict calibration signals along with original LLM output. |
| Outcome: | The proposed model reduces time and space costs while enabling seamless online deployment. |
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment (2024.acl-long)
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| Challenge: | Existing work cheaply emulates LLMs, allowing users to create profiles for their preferred characters. |
| Approach: | They propose a self-alignment method that encourages an instruction-following LLM to simulate role-play dialogues as a variant of reading comprehension. |
| Outcome: | The proposed model outperforms open-source role-play benchmarks and the roleplay subset of MT-Bench in multiple parameters. |
Nonparametric Decoding for Generative Retrieval (2023.findings-acl)
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Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, Minjoon Seo
| Challenge: | Existing text retrieval models depend on the information encoded in its parameters without external memory, its information capacity is limited and fixed. |
| Approach: | They propose a nonparametric decoding approach which uses external memory instead of vanilla vocab embeddings as decoder voka embedds. |
| Outcome: | The proposed model can utilize parametric and nonparametric space. |
Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue (2024.emnlp-main)
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| Challenge: | Existing methods that edit large language models with updated knowledge can cause side effects on the general abilities of LLMs such as reasoning, natural language inference, and question answering. |
| Approach: | They propose to regularize the edit update weights by imposing constraints on their complexity based on the RElative Change in weighT. |
| Outcome: | The proposed method can significantly mitigate the side effects while maintaining over 94% editing performance. |
Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models (2024.naacl-long)
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| Challenge: | Existing ensemble methods for Large Language Models focus on reward model ranking of outputs, leading to significant computation overhead. |
| Approach: | They propose a reward-guided routing method distilling rewards on training queries to train a routing function. |
| Outcome: | The proposed method outperforms the best single model and ranks first on 44% of tasks. |
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. |
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering (2024.emnlp-main)
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| Challenge: | Recent advances with LLMs have shown promising results across various tasks, but their use in answering questions from knowledge bases remains largely unexplored. |
| Approach: | They propose a framework that utilizes an LLM-based agent with multiple roles for KBQA tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks yielding F1 scores of 11.8% and 20.7%, respectively. |
Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization (2025.findings-naacl)
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Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari, Jen-Hao Rick Chang, Yin Xia, Xiang Kong, Qi Zhu, Xiaoming Simon Wang, Oncel Tuzel, Raviteja Vemulapalli
| Challenge: | Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. |
| Approach: | They propose Mutual Reinforcing Data Synthesis (MRDS) within large language models to enhance few-shot dialogue summarization task. |
| Outcome: | Empirical results show that the proposed method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings. |
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective (2023.eacl-main)
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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu
| Challenge: | Recent VSE models combine simple pooling methods with hard triplet loss to improve performance. |
| Approach: | They propose an adaptive pooling strategy that allows the model to learn how to aggregate features through a combination of simple pooling methods. |
| Outcome: | The proposed strategy outperforms current state-of-the-art systems on image-to-text and text-toimage retrieval. |
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation (2026.eacl-long)
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| Challenge: | Multi-LLM systems enhance creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. |
| Approach: | They propose a training-free framework that captures the benefits of multi-LLM collaboration by extracting and blending multiple distinct persona vectors directly in the model’s activation space. |
| Outcome: | The proposed framework surpasses model prompting and traditional multi-LLM approaches while significantly reducing inference time and computational costs. |
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%. |