Papers by Cong Feng
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 . |
CLARity: Reasoning Consistency Alone Can Teach Reinforced Experts (2026.acl-long)
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| Challenge: | Existing solutions to supervise the reasoning process are prohibitively expensive. |
| Approach: | They propose a cost-effective reinforcement learning framework that enhances reasoning quality using a small, general-purpose LLM only. |
| Outcome: | Experiments show that CLARity improves reasoning quality by 16.5% over standard outcome-based reinforcement learning (RL) human evaluations confirm substantial gains in factual correctness and reasoning coherence, leading to more trustworthy model outputs. |
Do Charge Prediction Models Learn Legal Theory? (2022.findings-emnlp)
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| Challenge: | Existing models for charge prediction are sensitive, selective, and presumption of innocence . a recent study has shown that deep learning models can predict the charges accurately, but their reliability and interpretability are still underexplored. |
| Approach: | They propose that trustworthy charge prediction models should take legal theories into consideration . they propose three principles for trustworthy models to follow in this task . |
| Outcome: | The proposed framework evaluates whether existing models learn legal theories . it shows that models meet selective and presumption of innocence principles . |
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction (2022.findings-acl)
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| Challenge: | a Chinese model with whole word masking has no subword because each token is an atomic character. |
| Approach: | They propose to use whole word masking to mask all subwords corresponding to a word at once . they ask models to revise or insert tokens in a masked language modeling manner . |
| Outcome: | The proposed model performs better when one character is inserted or replaced . the model trained with standard character-level masking performs best when one token is masked . |
Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slips (2025.coling-main)
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| Challenge: | Using a multi-modal multi-granularity tokenizer, we analyze ancient Chinese scripts . a large proportion of the characters in ancient Chinese are rare or undeciphered . |
| Approach: | They propose a multi-modal multi-granularity tokenizer specifically designed for ancient Chinese scripts. |
| Outcome: | The proposed tokenizer improves on the part-of-speech tagging task on the Chu bamboo slip script. |