Papers by Kangcheng Luo
ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal Advice (2024.acl-demos)
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| Challenge: | Large Language Models (LLMs) have shown impressive performance in various tasks, showing great potential for specific domains, such as law (Lai et al., 2023), finance (Zeng e e al. 2023) and law (Lam elms, 2024). |
| Approach: | They propose to use large language models to provide interpretable, accurate, and informative legal advice by visually presenting the correlation between legal articles and LLM's response by calculating their similarities. |
| Outcome: | The proposed model provides users with an intuitive legal basis for the responses and retrieves relevant legal cases for user reference. |
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model (2024.findings-acl)
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| Challenge: | Prompt with Actor-Critic Editing (PACE) for LLMs improves performance of different human-written prompts, resulting in significant performance discrepancies. |
| Approach: | They propose to use LLMs as actors and critics to enable automatic prompt editing by taking feedback from both actors performing prompt and criticizing response into account. |
| Outcome: | The proposed model improves the performance of human-written prompts by 98% and compares to high-quality human-writing prompts. |
Automating Legal Interpretation with LLMs: Retrieval, Generation, and Evaluation (2025.acl-long)
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| Challenge: | a novel framework for automated legal interpretation is proposed to alleviate the burden on legal experts. |
| Approach: | They propose a framework for automated legal interpretation that uses large language models to extract concept-related information and interpret legal concepts. |
| Outcome: | The proposed framework eliminates the need for legal experts to interpret legal concepts . it uses large language models to extract concept-related information and interpret legal concept interpretations . |
D2Plan: Dual-Agent Dynamic Global Planning for Complex Retrieval-Augmented Reasoning (2026.acl-long)
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| Challenge: | Recent advances in reinforcement learning (RL) have empowered Large Language Models (LLMs) with the capability to perform autonomous retrieval during reasoning tasks. |
| Approach: | They propose a "D2Plan" paradigm for retrieval-augmented reasoning that integrates a 'Reasoner' and a'Purifier' |
| Outcome: | Experiments show that the proposed paradigm improves on QA benchmarks. |