20 citations · 45 across the 12 of their papers we have counts for
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cs.CL2024★ 4 cited
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction
Ziyang Xu, Keqin Peng, Liang Ding +2
Recent research shows that pre-trained language models (PLMs) suffer from "prompt bias" in factual knowledge extraction, i.e., prompts tend to introduce biases toward specific labe…
cs.CL2024★ 2 cited
Revisiting Demonstration Selection Strategies in In-Context Learning
Keqin Peng, Liang Ding, Yancheng Yuan +4
Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL), where a few examples are used to describe a task to…