Papers by Bofei Gao
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback (2025.findings-acl)
Copied to clipboard
Bofei Gao, Zefan Cai, Runxin Xu, Peiyi Wang, Ce Zheng, Runji Lin, Keming Lu, Dayiheng Liu, Chang Zhou, Wen Xiao, Tianyu Liu, Baobao Chang
| Challenge: | Existing mathematical verifiers are trained with binary classification labels, which are not informative enough for the model to accurately assess the solutions. |
| Approach: | They propose a natural language feedback-enhanced verifier that can validate the correctness of response generated by policy models by constructing automatically generated training data and a two-stage training paradigm. |
| Outcome: | The proposed verifier significantly improves in verification and reinforcement learning and alleviates data-demanding problems of the reward model. |
Coarse-to-Fine Dual Encoders are Better Frame Identification Learners (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent efforts to model frame definitions lack sufficient representation learning of definitions or lack efficient frame modeling. |
| Approach: | They propose a frame-target-encoder architecture that uses coarse-to-fine learning to model alignment between frames and targets. |
| Outcome: | The proposed framework outperforms existing models by 0.93 overall scores and 1.53 R@1 without lf. |
Guiding AMR Parsing with Reverse Graph Linearization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a sentence. |
| Approach: | They propose a new framework that allows for reversed linearization of AMR graphs . they propose to combine sequence-to-sequence approaches with a linearized graph . |
| Outcome: | The proposed framework outperforms the best AMR parser by 0.8 and 0.5 Smatch scores on the AMR 2.0 and AMR 3.0 datasets. |
Towards A Better Initial Policy Model For Scalable Long-CoT Reinforcement Learning (2025.findings-acl)
Copied to clipboard
| Challenge: | Long-CoT reasoning and reinforcement learning are demonstrating remarkable performance and scalability, however, there is a lack of systematic guidelines for obtaining a better initial policy model. |
| Approach: | They propose a systematic guideline and a novel Re-RFT method to obtain more efficient reasoning patterns from different initial models. |
| Outcome: | The proposed method surpasses DeepSeek-R1-Distill-Qwen-14B model by 4.6%, demonstrating its effectiveness and superiority. |