Papers by Chuang Li
Tab-CQA: A Tabular Conversational Question Answering Dataset on Financial Reports (2023.acl-industry)
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| Challenge: | Existing conversational question answering datasets are usually constructed from unstructured texts in English. |
| Approach: | They propose a Chinese tabular conversational question answering dataset based on financial reports . they select 2,463 tables and manually generate 2,463, conversations with 35,494 QA pairs . |
| Outcome: | The proposed dataset is based on Chinese financial reports extracted from listed companies in the past 30 years. |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization (2024.findings-acl)
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Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister
| Challenge: | Large language models struggle to capture relevant information located in the middle of their input. |
| Approach: | They propose a calibration mechanism that allows the model to attend to contexts faithfully according to their relevance even when they are in the middle. |
| Outcome: | The proposed calibration mechanism mitigates this positional bias and improves retrieval-augmented generation performance. |
Ada-RS: Adaptive Rejection Sampling for Selective Thinking (2026.acl-industry)
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Yirou Ge, Yixi Li, Alec M. Chiu, Shivani Shekhar, Zijie Pan, Avinash Thangali, Yun-Shiuan Chuang, Chaitanya Kulkarni, Uma Kona, Linsey Pang, Prakhar Mehrotra
| Challenge: | Large language models are increasingly being deployed in cost- and latency-sensitive settings . chain-of-thought improves reasoning, but it can waste tokens on simple requests . |
| Approach: | They introduce an algorithm-agnostic sample filtering framework for learning selective reasoning . they show that Ada-RS reduces average output tokens by 80% and reducing thinking rate by 5% . |
| Outcome: | The proposed framework reduces output tokens by 80% and thinking rate by 95% on a synthetic tool call-oriented e-commerce benchmark. |
Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs (2025.coling-main)
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| Challenge: | Existing benchmarks for evaluating large language models neglect key qualitative data attributes that can significantly impact the final rankings of LLMs. |
| Approach: | They propose a framework with three modules designed to assess diversity, redundancy, and difficulty. |
| Outcome: | The proposed framework systematically incorporates diversity, redundancy, and difficulty attributes and shows that they influence the ranking of LLMs. |
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)
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Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. |
| Approach: | They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation. |
| Outcome: | Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation. |
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)
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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
| 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. |
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query. |
| Approach: | They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering. |
| Outcome: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model. |
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings (2022.naacl-main)
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Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Scott Yih, Yoon Kim, James Glass
| Challenge: | Recent work shows that finetuning pretrained models with contrastive learning makes it possible to learn good sentence embeddings without labeled data. |
| Approach: | They propose an unsupervised contrastive learning framework for learning sentence embeddings . they use a masked language model to mask out the edited sentence . |
| Outcome: | The proposed framework outperforms SimCSE on semantic textual similarity tasks by 2.3 absolute points. |
PAMN: Multi-phase Correlation Modeling for Contrast-Enhanced 3D Medical Image Retrieval (2025.findings-emnlp)
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| Challenge: | Current 3D medical imaging models focus on spatial features, neglecting phase-specific progression detailed in clinical reports. |
| Approach: | They propose a framework that fuses imaging phases with clinical text to enhance 3D medical image retrieval. |
| Outcome: | The proposed framework outperforms state-of-the-art models on a phase-series dataset of 12,230 hospital CT scans. |
ChatCRS: Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems (2025.findings-naacl)
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| Challenge: | Experimental results show that ChatCRS improves language quality and informativeness by 17% and proactivity by 27%. |
| Approach: | They propose a framework to decompose the CRS task into several sub-tasks . they propose 'knowledge retrieval agent' and 'goal-planning agent' |
| Outcome: | The proposed framework improves language quality and informativeness by 17% and proactivity by 27% on two multi-goal CRS datasets. |
Joint Pre-Encoding Representation and Structure Embedding for Efficient and Low-Resource Knowledge Graph Completion (2024.emnlp-main)
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| Challenge: | Existing knowledge graph completion models require longer training and inference times as well as increased memory usage. |
| Approach: | They propose to encode textual descriptions into semantic representations before training and integrate structural embedding with pre-encoded semantic description to improve model's prediction performance on 1-N relations. |
| Outcome: | The proposed model increases inference speed by 30x and reduces training memory by approximately 60% on the WN18RR and UMLS datasets. |
Toward Scalable Verifiable Reward: Proxy State-Based Evaluation for Multi-turn Tool-Calling LLM Agents (2026.acl-industry)
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Yun-Shiuan Chuang, Chaitanya Kulkarni, Alec M. Chiu, Avinash Thangali, Zijie Pan, Shivani Shekhar, Yirou Ge, Yixi Li, Uma Kona, Linsey Pang, Prakhar Mehrotra
| Challenge: | Existing agentic benchmarks rely on deterministic backends and are costly to build and iterate. |
| Approach: | They propose a framework that preserves final state-based evaluation without a deterministic database. |
| Outcome: | The proposed framework produces stable, model-differentiating rankings across families and inference-time reasoning efforts. |
LoRATK: LoRA Once, Backdoor Everywhere in the Share-and-Play Ecosystem (2025.findings-emnlp)
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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
| 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. |
Aligning Large Multimodal Models with Factually Augmented RLHF (2024.findings-acl)
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Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, Trevor Darrell
| Challenge: | Large Multimodal Models (LMMs) are built across modalities and the misalignment between two modality can result in "hallucination" . developing LMMs faces challenges such as a lack of data and a limited number of data sets. |
| Approach: | They propose a new algorithm that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options. |
| Outcome: | The proposed approach improves on the LLaVA-Bench dataset with the 96% performance level of the text-only GPT-4 and an improvement of 60% on MMHAL-BENCH over other baselines. |
On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation (2022.naacl-main)
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| Challenge: | Pre-trained models have not been used to outperform other deep learning models such as CNN in Automated Essay Scoring (AES). |
| Approach: | They propose a novel multi-scale essay representation for BERT that can be jointly learned . they employ multiple losses and transfer learning from out-of-domain essays to further improve performance . |
| Outcome: | The proposed model outperforms existing models in the area of automated essay scoring . the proposed model generalizes well to the CommonLit Readability Prize data set . |
Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression (2026.findings-acl)
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Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, Yanmin Qian
| Challenge: | Existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. |
| Approach: | They propose to use a large-scale, context-aware speech corpus derived from multi-speaker audiobooks to generate a speech that is human-like. |
| Outcome: | The proposed model outperforms existing methods in terms of emotional expression accuracy and naturalness. |
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking (2024.findings-naacl)
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| Challenge: | Existing methods for zero-shot dialogue state tracking (DST) ignore unlabelled data in the target domain. |
| Approach: | They propose to transform zero-shot dialogue state tracking into few-shot DST by utilising unlabelled data via joint and self-training methods. |
| Outcome: | The proposed method improves joint goal accuracy by 8% on general language models in zero-shot scenarios, and can be used in many domains. |
Steering LLM Thinking with Budget Guidance (2026.findings-acl)
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| Challenge: | Existing budget control methods for large language models are inadequate for long reasoning . budget guidance can be used to control reasoning length without fine-tuning . |
| Approach: | They propose a budget guidance method that models a Gamma distribution over remaining thinking length during next-token generation and uses it to guide generation in a soft, token-level manner. |
| Outcome: | The proposed method achieves up to 26% accuracy gain on the MATH-500 benchmark compared to baseline methods while maintaining competitive accuracy with only 63% of the thinking tokens used by the full-thinking model. |