Papers by Jun Kuang
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator (2025.naacl-long)
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| Challenge: | Domain Large Language Models (LLMs) are developed for domain-specific tasks based on general LLMs, but it still requires professional knowledge to facilitate the expertise for some domain- specific tasks. |
| Approach: | They propose a pipeline to solve domain-specific calculation problems with KIPG . they use it to extract key variables and calculate outcomes dependent on domain knowledge . |
| Outcome: | The proposed pipeline solves domain-specific calculation problems more effectively . it generates knowledge-intensive programs according to the domain- specific documents . |
De-Biased Court’s View Generation with Causality (2020.emnlp-main)
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Yiquan Wu, Kun Kuang, Yating Zhang, Xiaozhong Liu, Changlong Sun, Jun Xiao, Yueting Zhuang, Luo Si, Fei Wu
| Challenge: | Existing approaches to court’s view generation can be used to address this problem, but neglecting the confounding bias in data can limit the model performance and pollute learning outcomes. |
| Approach: | They propose a novel Attentional and Counterfactual based Natural Language Generation method consisting of an attentional encoder and a pair of innovative counterfactual decoders to generate judgment-discriminative court's views. |
| Outcome: | The proposed method is able to generate judgment-discriminative court's views (both supportive and non-supportive views) under both quantitative and qualitative evaluation metrics. |
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)
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| Challenge: | Existing models lack interpretability due to the neglect of rationale in the prediction process. |
| Approach: | They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability. |
| Outcome: | The proposed framework provides good interactivity and interpretability which enables practical use. |
Why Not Act on What You Know? Unleashing Safety Potential of LLMs via Self-Aware Guard Enhancement (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks but are vulnerable to meticulously crafted jailbreak attacks. |
| Approach: | They propose a training-free defense strategy to align LLMs’ strong safety discrimination performance with their relatively weaker safety generation ability. |
| Outcome: | The proposed strategy achieves an average 99% success rate against numerous complex and covert jailbreak methods while maintaining helpfulness on general benchmarks. |
RED: Unleashing Token-Level Rewards from Holistic Feedback via Reward Redistribution (2025.emnlp-main)
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| Challenge: | Experimental results demonstrate the superiority of our approach to aligning large language models with human preferences. |
| Approach: | They propose a method that evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
| Outcome: | The proposed method evaluates and assigns specific credit to each token using an off-the-shelf reward model. |
Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning (2024.emnlp-main)
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| Challenge: | Recent research has developed algorithms for reinforcement learning from human feedback and AI-generated feedback. |
| Approach: | They propose a method for reinforcement learning from human feedback and AI-generated feedback that incorporates weighting, ranking, and constraining to handle disparate rewards. |
| Outcome: | The proposed method reduces disparity and enhances stability among rewards . empirical results show that the proposed method is efficient and straightforward . |
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)
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Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao
| Challenge: | Existing models lack the ability to adhere to instructions, resulting in suboptimal performance. |
| Approach: | They propose an automated iterative instruction-following benchmark with integrated feedback mechanism. |
| Outcome: | The proposed benchmark identifies erroneous components in model responses and provides feedback accurately. |
LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation (2026.acl-long)
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Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu
| Challenge: | Current systems for legal consultation are insufficient to handle the knowledge-intensive nature of real-world consultations. |
| Approach: | They propose a multi-turn benchmark dataset to evaluate LLMs in legal consultation settings. |
| Outcome: | The proposed framework assesses LLMs’ consultation capabilities in terms of (1) clarification capability and (2) professional advice quality. |
Chain-of-Quizzes: Pedagogy-inspired Example Selection in In-Context-Learning (2024.findings-acl)
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| Challenge: | In-context learning (ICL) is a powerful tool for enhancing large language models (LLMs) by mimicking the human learning process. |
| Approach: | They propose a Chain-of-Quizzes framework that uses LLMs to answer a quiz to sift 'good' examples, combine them iteratively with the increasing complexity, and utilize a final exam to gauge the combined example chains. |
| Outcome: | The proposed framework outperforms baseline models on diverse reasoning datasets and shows that it is scalable and can be used in future research. |
A Wolf in Sheep’s Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily (2024.naacl-long)
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| Challenge: | Existing methods for generating 'jailbreaks' suffer from manual design or require optimization on other white-box models, which compromises either generalization or efficiency. |
| Approach: | They propose a framework that leverages LLMs to generate effective jailbreak prompts and a generalized framework that can be used to generate prompts. |
| Outcome: | The proposed framework improves the attack success rate while reducing the time cost compared to baselines. |
Latent Learningscape Guided In-context Learning (2024.findings-acl)
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| Challenge: | Existing methods to select demonstrations based on surface-level semantic similarities fall short of identifying the most fitting ones. |
| Approach: | They propose a method that characterizes latent learningscape features of demonstrations and uses them to create more effective prompts. |
| Outcome: | The proposed method outperforms leading models in arithmetic, commonsense, and symbolic reasoning tasks showing an average increase in scores by 7.4 percentage points. |
Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)
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| Challenge: | Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus. |
| Approach: | They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses. |
| Outcome: | The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets. |
Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing prompt-based NER models fail to detect entity boundaries, causing performance degradation. |
| Approach: | They propose a model which consists of a BART encoder and a parabiotic decoder and propose ' boundary expansion strategy' to enhance the model's capability in entity type classification. |
| Outcome: | The proposed model can achieve significant performance gains over state-of-the-art models. |