Papers by Chaoxu Pang

2 papers
Uncovering Limitations of Large Language Models in Information Seeking from Tables (2024.findings-acl)

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Challenge: Existing benchmarks for Table Information Seeking (TabIS) are lacking in reliable evaluation.
Approach: They propose a benchmark to evaluate the table information seeking abilities of large language models . they use a single-choice question format instead of a text-based evaluation .
Outcome: The proposed benchmark is more reliable than existing models and is available online.
Guideline Learning for In-Context Information Extraction (2023.emnlp-main)

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Challenge: Large language models can perform a task by conditioning on task instructions and a few input-output examples without optimizing any parameters.
Approach: They propose a guideline learning framework for In-context IE which reflectively learns and follows guidelines.
Outcome: The proposed framework improves the performance of in-context IE by synthesizing and following guidelines.

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