2 citations · 5 across the 8 of their papers we have counts for
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cs.CL2024
Logit Separability-Driven Samples and Multiple Class-Related Words Selection for Advancing In-Context Learning
Zhu Zixiao, Feng Zijian, Zhou Hanzhang +2
Effective organization of in-context learning (ICL) demonstrations is key to improving the quality of large language model (LLM) responses. To create better sample-label pairs that…
cs.CL2024★ 2 cited
Unveiling and Manipulating Prompt Influence in Large Language Models
Zijian Feng, Hanzhang Zhou, Zixiao Zhu +2
Prompts play a crucial role in guiding the responses of Large Language Models (LLMs). However, the intricate role of individual tokens in prompts, known as input saliency, in shapi…
cs.CL2024★ 1 cited
UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation
Hanzhang Zhou, Zijian Feng, Zixiao Zhu +2
Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm. However, their effectiveness is often compromi…