|
- Evaluating Large Language Models —Principles, Approaches, and Applications
Abstract The rapid advancement of Large Language Models (LLMs) has revolutionized various fields, yet their deployment presents unique evaluation challenges This whitepaper details the
- A Systematic Survey and Critical Review on Evaluating Large Language . . .
This shift has revolutionized the development of real- world applications powered by LLMs With the advancements and broad applicabil- ity of LLMs, it is essential to properly evaluate them to ensure they are safe to use This is in- deed important not only for academic benchmarks
- How to Evaluate Large Language Models: An Overview of Modern Evaluation . . .
Modern LLM evaluation frameworks employ sophisticated technical architectures to ensure consistent, reliable assessment of model capabilities These benchmarks differ significantly in their methodological approaches, implementation details, and resource requirements
- Moving LLM evaluation forward: lessons from human judgment research
This paper outlines a path toward more reliable and effective evaluation of Large Language Models (LLMs) It argues that insights from the study of human judgment and decision-making can illuminate current challenges in LLM assessment and help close critical gaps in how models are evaluated
- Holistic Evaluation of Language Models (HELM)
A reproducible and transparent framework for evaluating foundation models Find leaderboards with many scenarios, metrics, and models with support for multimodality and model-graded evaluation The Holistic Evaluation of Language Models (HELM) serves as a living benchmark for transparency in language models
- Evaluating Large Language Models: A Comprehensive Survey
This survey endeavors to offer a panoramic perspective on the evaluation of LLMs We categorize the evaluation of LLMs into three major groups: knowledge and capability evaluation, alignment evaluation and safety evaluation
- A Survey on Evaluation of Large Language Models
This paper presents a comprehensive review of these eval-uation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate
- Language Models as Tools for Research Synthesis and Evaluation
• 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?
|
|
|