Emerging Trends in LLM Benchmarking

Traditionally, machine learning models that focused on specialized tasks facilitated straightforward evaluation. However, the evolution of Large Language Models, has increased the complexity w.r.t. performance measurement. Evaluation and benchmarking of large language model is a significant challenge due to their versatility and improved capability to perform a wide range of tasks. This manuscript examines existing literature for various benchmarks and identifies a comprehensive overview of the emerging trends in benchmarking methodology.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825845
Citations
3
Primary Topic
Digital Rights Management and Security
Type
article
Field-Weighted Citation Impact
2.34
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Emerging Trends in LLM Benchmarking

Pranjul Yadav, Riddhi B. Prajapati, Akshar Prabhu Desai, Mohammad Luqman et al.
3 citations
Digital Rights Management and Security
2.34
article

Emerging Trends in LLM Benchmarking

Pranjul Yadav, Riddhi B. Prajapati, Akshar Prabhu Desai, Mohammad Luqman, Tejasvi Ravi
article en
3 citations

Abstract

Traditionally, machine learning models that focused on specialized tasks facilitated straightforward evaluation. However, the evolution of Large Language Models, has increased the complexity w.r.t. performance measurement. Evaluation and benchmarking of large language model is a significant challenge due to their versatility and improved capability to perform a wide range of tasks. This manuscript examines existing literature for various benchmarks and identifies a comprehensive overview of the emerging trends in benchmarking methodology.

Openalex Percentile: Top 8%
Digital Rights Management and Security
2.34
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Emerging Trends in LLM Benchmarking — Pranjul Yadav, Riddhi B. Prajapati, et al. · (2024) | TGRS Research Map | TGRS