Papers by Xudong Weng

2 papers
Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)

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Challenge: Existing methods for enhancing the performance of large language models require expensive manual annotations.
Approach: They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence.
Outcome: The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method.
LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification (2025.findings-acl)

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Challenge: Existing schema linking methods are not able to handle complex SQL queries.
Approach: They propose a new algorithm that transforms SQL queries into grammatically verifiable sub-queries which are arranged sequentially to reflect single-hop reasoning steps.
Outcome: The proposed algorithm achieves significant performance gains on the BIRD dataset and surpasses schema linking methods at comparable or better cost.

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