3 papers
cs.CL2026
How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling
Yuhang Liu, Heyan Huang, Yizhe Yang +3
Large language models (LLMs) have achieved strong performance on reasoning benchmarks, yet their ability to solve real-world problems requiring end-to-end workflows remains unclear…
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…