Papers by Chenghao Liu
Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning (2024.findings-acl)
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| Challenge: | Recent years have witnessed a substantial increase in the demand for legal services, especially for individuals with modest means. |
| Approach: | They propose a diagnostic legal large language model which uses adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback. |
| Outcome: | The proposed model surpasses classical LLMs by providing outstanding performance and a remarkable user experience in the legal domain. |
Reference Network for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation (NMT) generates translations in isolation, resulting in translation inconsistency and ambiguity. |
| Approach: | They propose to incorporate referring process into translation decoding of NMT by using local coordinates coding to obtain global context vectors containing monolingual and bilingual contextual information. |
| Outcome: | The proposed model improves translation quality with lightweight computation cost on Chinese-English and English-German translation tasks. |
Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026.findings-acl)
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| Challenge: | Existing training-free methods are vulnerable to the attention sink phenomenon . Existing methods include contrastive decoding and auxiliary expert models . |
| Approach: | They propose a training-free attention intervention that constructs a PAD map to identify semantically core visual regions and applies per-head Median Absolute Deviation Scaling to adaptively control the intervention strength. |
| Outcome: | The proposed intervention improves visual grounding and reduces hallucinations on multiple LVLMs and benchmarks. |
AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)
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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks. |
| Approach: | They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. |
| Outcome: | The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS. |
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)
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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. |
| Approach: | They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions. |
| Outcome: | The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks. |
Word-level Textual Adversarial Attacking as Combinatorial Optimization (2020.acl-main)
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| Challenge: | Existing word-level attack models are far from perfect because of unsuitable search space reduction methods and inefficient optimization algorithms. |
| Approach: | They propose a novel adversarial adversarialist model that incorporates word substitution and particle swarm optimization to solve two problems separately. |
| Outcome: | The proposed model achieves much higher success rates and crafts more high-quality adversarial examples as compared to baseline methods. |
UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues (2020.emnlp-main)
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| Challenge: | Existing approaches to training dialogue agents separately are not optimized for multi-domain task-oriented dialogues. |
| Approach: | They propose a unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues that jointly trains a bi-level state tracker and a joint dialogue act and response generator. |
| Outcome: | The proposed system outperforms existing systems on the MultiWOZ2.1 benchmark in dialogue state tracking, context-to-text, and end-to end settings. |
MARS-Bench: A Multi-turn Athletic Real-world Scenario Benchmark for Dialogue Evaluation (2025.findings-emnlp)
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Chenghao Yang, Yinbo Luo, Zhoufutu Wen, Qi Chu, Tao Gong, Longxiang Liu, Kaiyuan Zhang, Jianpeng Jiao, Ge Zhang, Wenhao Huang, Nenghai Yu
| Challenge: | Large Language Models (LLMs) have been widely adopted in real-world dialogue applications, but their robustness is criticized all along. |
| Approach: | They propose to use play-by-play text commentary to build a multi-turn athletic real-world scenario dialogue benchmark to evaluate three critical aspects of multi-turned conversations: ultra multi- turn, interactive multi-twist, and cross-turn tasks. |
| Outcome: | The proposed benchmarks outperform open-source LLMs on three critical aspects of multi-turn conversations: ultra multi-turned, interactive multi- turn, and cross-turn tasks. |
Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning (2025.naacl-long)
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| Challenge: | Existing approaches to large language models focus on semantic similarity, neglecting the intricate logical structures and reasoning essential for addressing complex legal issues. |
| Approach: | They propose a Logical-Semantic Integration Model (LSIM) that bridges semantic and logical coherence and a supervised framework that integrates semantic features with in-context learning. |
| Outcome: | The proposed framework significantly improves accuracy and reliability on a real-world legal QA dataset. |
A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated powerful reasoning abilities across multiple domains, but have been underexplored for time-series reasoning (TsR) |
| Approach: | They propose a prompt-based solution for evaluating large language models’ TsR performance. |
| Outcome: | The proposed solution improves performance and costs by 140% and reduces costs by 99%. |
Fusion-in-T5: Unifying Variant Signals for Simple and Effective Document Ranking with Attention Fusion (2024.lrec-main)
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| Challenge: | Current document ranking pipelines involve multiple ranking layers to integrate different information step-by-step. |
| Approach: | They propose a novel re-ranker Fusion-in-T5 which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention. |
| Outcome: | The proposed model significantly improves ranking performance over complex cascade pipelines. |
Semantically Comprehensive Token Pruning in LVLMs via Maximizing Concept Coverage (2026.acl-long)
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| Challenge: | Existing visual token pruning methods leverage simple metrics derived from human experience, such as attention or similarity, to rank and select tokens within a highly entangled feature space. |
| Approach: | They propose a novel visual token pruning method that uses a concept-driven paradigm to quantify the Marginal Semantic Gain of each token's contribution to uncovered concepts. |
| Outcome: | The proposed method outperforms state-of-the-art methods in a concept-driven model while maintaining semantic completeness. |
David vs. Goliath: Cost-Efficient Financial QA via Cascaded Multi-Agent Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models have demonstrated remarkable reasoning capabilities, but performance in FQA remains limited. |
| Approach: | They propose a low-cost yet effective framework that enables small LLMs to perform complex reasoning tasks without expensive models. |
| Outcome: | The proposed framework outperforms the best open-source model on BizBench by 10.46% and achieves competitive performance to GPT-3.5 using significantly fewer parameters. |
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)
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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain. |
| Approach: | FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. |
| Outcome: | FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability. |
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)
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Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Yang Guang, Chenghua Lin
| Challenge: | Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored. |
| Approach: | They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection. |
| Outcome: | The proposed framework provides valuable insights for developing more accurate and customisable human simulacra. |
Beyond One-Size-Fits-All: Inversion Learning for Highly Effective NLG Evaluation Prompts (2026.tacl-1)
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| Challenge: | Evaluating natural language generation systems is challenging due to the diversity of valid outputs. |
| Approach: | They propose an inversion learning method that learns effective reverse mappings from model outputs back to their input instructions. |
| Outcome: | The proposed method requires only a single evaluation sample and eliminates manual prompt engineering. |
PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning (2024.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained language models to various tasks efficiently. |
| Approach: | They propose a parameter-efficient fine-tuning framework that captures transferable knowledge as a weighted combination of adapters trained on source tasks. |
| Outcome: | The proposed method yields stable improvements over full fine-tuning and knowledge transferring methods on a broad range of tasks over 17 datasets. |
When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors (2026.acl-long)
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| Challenge: | Existing metrics fail to distinguish mandatory behaviors required for task success from non-mandatory patterns that reflect a model’s autonomous preferences. |
| Approach: | They propose to use response pattern similarity and action graph similarity to isolate non-mandatory behaviors from mandatory behaviors. |
| Outcome: | Evaluating 18 models from 8 providers on -Bench and 2-Bench against Claude Sonnet 4.5, the authors find that within-family model pairs score 5.9 pp higher in response pattern similarity and action graph similarity . |
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts (2023.emnlp-main)
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Truong Do, Le Khiem, Quang Pham, TrungTin Nguyen, Thanh-Nam Doan, Binh Nguyen, Chenghao Liu, Savitha Ramasamy, Xiaoli Li, Steven Hoi
| Challenge: | Recent studies suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations. |
| Approach: | They propose a method that dynamically generates router parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy. |
| Outcome: | Experiments on a wide range of tasks show that the proposed method performs better than existing methods. |
Modeling Semantic Compositionality with Sememe Knowledge (P19-1)
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| Challenge: | Semantic compositionality (SC) is defined as the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. |
| Approach: | They propose to incorporate sememes into SC models and employ them in learning multiword expressions. |
| Outcome: | The proposed models achieve significant performance boost compared to baseline methods without sememe knowledge. |
Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for locating and editing static knowledge are costly and risk catastrophic forgetting or error. |
| Approach: | They propose a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update. |
| Outcome: | Experiments on standard benchmarks show that FiDAL improves editing effectiveness and knowledge preservation across multiple editing methods. |