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Found 4 papers, 4 papers with code
TurtleBench: Evaluating Top Language Models via Real-World Yes/No Puzzles
This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations.
Attention Heads of Large Language Models: A Survey
Our survey aims to shed light on the internal reasoning processes of LLMs by concentrating on the underlying mechanisms of attention heads.
Internal Consistency and Self-Feedback in Large Language Models: A Survey
In this paper, we use a unified perspective of internal consistency, offering explanations for reasoning deficiencies and hallucinations.
xFinder: Robust and Pinpoint Answer Extraction for Large Language Models
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 performance.
