Papers by Ang Gao

3 papers
Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models (2024.acl-long)

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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.
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations (2023.acl-long)

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Challenge: a context leads to various responses, and a response answers multiple contexts.
Approach: They propose a method that augments open-domain dialogue generation from a many-to-many perspective.
Outcome: The proposed method can augment open-domain dialogue generation tasks with automatic and human evaluation.
Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning (2025.findings-emnlp)

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Challenge: Existing methods for in-context learning (ICL) performance rely on quality and ordering of demonstrations.
Approach: They propose a method that models iterative demonstration selection as a Markov Decision Process and craft hybrid reward signals.
Outcome: The proposed method combines outcome-based accuracy signals with process-oriented signals like stepwise influence and label entropy improvement.

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