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cs.CL2024
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment
Heyan Huang, Yinghao Li, Huashan Sun +2
Recent studies have demonstrated that In-Context Learning (ICL), through the use of specific demonstrations, can align Large Language Models (LLMs) with human preferences known as…
cs.CL2024
CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling
Yu Bai, Xiyuan Zou, Heyan Huang +4
Long sequence modeling has gained broad interest as large language models (LLMs) continue to advance. Recent research has identified that a large portion of hidden states within th…
cs.CL2024
Identifying and Analyzing Performance-Critical Tokens in Large Language Models
Yu Bai, Heyan Huang, Cesare Spinoso-Di Piano +4
In-context learning (ICL) has emerged as an effective solution for few-shot learning with large language models (LLMs). However, how LLMs leverage demonstrations to specify a task…