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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.
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.
Grimoire is All You Need for Enhancing Large Language Models
In this paper, we propose a method SLEICL that involves learning from examples using strong language models and then summarizing and transferring these learned skills to weak language models for inference and application.
