Papers by Yuege Feng

2 papers
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm (2025.emnlp-main)

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Challenge: Existing studies focus on individual quality and do not assess the value of training data.
Approach: They propose a choice-based sample selection framework that evaluates sample quality . they use LLMs to evaluate the value of each option during the selection process .
Outcome: The proposed model outperforms the full dataset and recent studies on a larger medical dataset.
APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport (2025.emnlp-main)

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Challenge: Experimental results show that RLHF improves performance of Large Language Models . BT-based RMs struggle to distinguish between similar preference responses .
Approach: They propose to enhance BT-based reward models by using an adaptive margin mechanism . they use semantic similarity and reward-predicted reward differences to adjust focus .
Outcome: Experimental results show that the proposed method outperforms existing methods in both in-distribution and OOD settings.

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