19 citations · 22 across the 7 of their papers we have counts for
4 papers · 2 filters
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
Qingchen Yu, Shichao Song, Ke Fang +5
As the application of Large Language Models (LLMs) expands, the demand for reliable evaluations increases. Existing LLM evaluation benchmarks primarily rely on static datasets, mak…
Internal Consistency and Self-Feedback in Large Language Models: A Survey
Xun Liang, Shichao Song, Zifan Zheng +8
Large language models (LLMs) often exhibit deficient reasoning or generate hallucinations. To address these, studies prefixed with "Self-" such as Self-Consistency, Self-Improve, a…
xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation
Qingchen Yu, Zifan Zheng, Shichao Song +4
The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their perfo…
Grimoire is All You Need for Enhancing Large Language Models
Ding Chen, Shichao Song, Qingchen Yu +4
In-context Learning (ICL) is one of the key methods for enhancing the performance of large language models on specific tasks by providing a set of few-shot examples. However, the I…