4 citations · 7 across the 2 of their papers we have counts for
2 papers
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★ 3 cited
Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning
Changtong Zan, Liang Ding, Li Shen +3
Translation-tailored Large language models (LLMs) exhibit remarkable translation capabilities, even competing with supervised-trained commercial translation systems. However, off-t…