Papers by Changyu Chen
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models (2024.acl-long)
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Changyu Chen, Xiting Wang, Ting-En Lin, Ang Lv, Yuchuan Wu, Xin Gao, Ji-Rong Wen, Rui Yan, Yongbin Li
| Challenge: | Despite the advances in large language models, they still face difficulties with multi-step reasoning tasks. |
| Approach: | They propose a method that randomly masks certain tokens within the chain of thought to improve model accuracy by 5% over standard supervised fine-tuning. |
| Outcome: | The proposed method improves accuracy and accuracy by 5% over standard fine-tuning with a few codes modified. |
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations (2021.acl-long)
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| Challenge: | Existing methods to train retrieval-based dialogue systems rely on crowd-sourced data . however, it is difficult to collect large-scale dialogues that are grounded on background knowledge . |
| Approach: | They propose to decompose training of knowledge-grounded response selection into three tasks . they propose to combine query-passage matching task with query-dialogue history matching task . |
| Outcome: | Experimental results show that the proposed model can perform comparable to existing methods . the retrieval-based system can leverage background knowledge when conversing with humans . |
Fortify the Shortest Stave in Attention: Enhancing Context Awareness of Large Language Models for Effective Tool Use (2024.acl-long)
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| Challenge: | In this paper, we demonstrate that an inherent waveform pattern in the attention allocation of large language models significantly affects their performance in tasks demanding a high degree of context awareness. |
| Approach: | They propose a method that compensates an attention trough with an attention peak by a process to enhance the model's awareness to various contextual positions. |
| Outcome: | The proposed method improves the performance of a 7B model on the largest tool-use benchmark, comparable to that of GPT-4. |
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment (2024.findings-acl)
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| Challenge: | Existing language models that generate harmful responses are constrained by their inherent capability. |
| Approach: | They propose to align large language models with human preferences from AI feedback. |
| Outcome: | The proposed framework improves the alignment of large language models with human preferences from AI feedback. |
Prototypical Reward Network for Data-Efficient Model Alignment (2024.acl-long)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a reward model that fine-tunes Large Language Models (LLMs) by utilizing Prototypical Networks. |
| Approach: | They propose a framework utilizing Prototypical Networks to enhance reward models under limited human feedback, enabling more stable and reliable structural learning from fewer samples. |
| Outcome: | The proposed framework improves reward models under limited human feedback, surpassing traditional methods, especially in data-limited scenarios. |
Unlocking the Planning Capabilities of Large Language Models with Maximum Diversity Fine-tuning (2025.findings-naacl)
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| Challenge: | Existing studies have shown that LLMs struggle to generate valid plans in the automated planning domain due to weak System 2 competencies. |
| Approach: | They propose a method which uses a random sampling method to select diverse and representative data to enhance sample efficiency and the model’s generalization capability. |
| Outcome: | The proposed method outperforms baseline methods across scales and multiple benchmark domains. |