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Lastly, we discuss several open problems in this area and point out future research directions. Researchers and engineers face methodological issues such as the sensitivity of models to evaluation setup, dificulty of proper comparisons across methods, and the lack of reproducibility and transparency. Abstract the rapid advancement of large language models (llms) has revolutionized various fields, yet their deployment presents unique evaluation challenges
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To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of llm evaluation. Effective evaluation of language models remains an open challenge in nlp • it has been demonstrated empirically that performing rag on unreliable documents worsen the performance of llm
Can we flip this around and evaluate the reliability of scientific documents, going beyond the traditional scientometrics?
Against the background, this paper conducts an analysis of 105 assessment tools developed by governmental agencies, academic institutions, research groups, and technology corporations. A few months after chatgpt’s launch, we started to see a rapid, linear increase in the usage pattern in academic writing This tells us how quickly these llm technologies diffuse into the community and become adopted by researchers. Utilizing the capabilities and responsibilities of llms for automated evaluation (llm4eval) has recently attracted considerable attention in multiple research communities.
This paper outlines a path toward more reliable and effective evaluation of large language models (llms)
