Papers by Chi Xue

2 papers
Mitigating Selection Bias with Node Pruning and Auxiliary Options (2025.acl-long)

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Challenge: Large language models exhibit systematic preferences for answer choices when answering multiple-choice questions.
Approach: They propose two methods to identify and remove internal sources of selection bias . they propose Choice Kullback-Leibler Divergence (CKLD) to capture distributional imbalances in model predictions.
Outcome: The proposed methods improve answer accuracy while reducing selection bias.
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

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Challenge: Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach: They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome: The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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