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  • LLM4SR: A Survey on Large Language Models for Scientific Research
    We analyze the unique roles LLMs play across four critical stages of research: hypothesis discovery, experiment planning and implementation, scientific writing, and peer reviewing Our review comprehensively showcases the task-specific methodologies and evaluation benchmarks
  • A Systematic Survey and Critical Review on Evaluating Large Language . . .
    To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust
  • How to Evaluate LLMs: A Complete Metric Framework - Microsoft Research
    In this article, we are sharing the standard set of metrics that are leveraged by the teams, focusing on estimating costs, assessing customer risk and quantifying the added user value These metrics can be directly computed for any feature that uses OpenAI models and logs their API response
  • Evaluating the effectiveness of large language models in abstract . . .
    This study aimed to evaluate the performance of large language models (LLMs) in the task of abstract screening in systematic review and meta-analysis studies, exploring their effectiveness, efficiency, and potential integration into existing human expert-based workflows
  • Friend or foe? Exploring the implications of large language models on . . .
    The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice
  • Evaluating research quality with Large Language Models: An. . .
    Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises, appointments and promotion It is therefore important to investigate whether Large Language Models (LLMs) can play a role in this process
  • 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
  • Testing and Evaluation of Health Care Applications of Large Language Models
    Our approach categorizes LLM evaluations based on data type, health care task, natural language processing (NLP) and natural language understanding (NLU) tasks, dimension of evaluation, and medical specialty
  • Beyond Factuality: A Comprehensive Evaluation of Large Language Models . . .
    Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge
  • Language Models as Tools for Research Synthesis and Evaluation
    How do we map each paper into the high-dimensional context space? How do we address between competing theories and findings (incommensurability problem)? •We need a more sophisticated meta-analysis tool that goes beyond statistically aggregating the results across papers •Evaluation: most available data from previous studies are collected





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