The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?

TL;DR

This paper critically examines the limitations of current NLP benchmarks for LLMs, revealing vulnerabilities like exploitation and bias, and proposes the development of more robust, dynamic evaluation frameworks to better measure true language understanding.

Contribution

The paper systematically analyzes existing evaluation methods, identifies their vulnerabilities, and suggests new adaptive frameworks to improve the accuracy of LLM performance assessment.

Findings

Current benchmarks are vulnerable to exploitation and bias.

Existing evaluation methods often fail to reflect true LLM capabilities.

Proposes new dynamic evaluation frameworks to address limitations.

Abstract

The pursuit of leaderboard rankings in Large Language Models (LLMs) has created a fundamental paradox: models excel at standardized tests while failing to demonstrate genuine language understanding and adaptability. Our systematic analysis of NLP evaluation frameworks reveals pervasive vulnerabilities across the evaluation spectrum, from basic metrics to complex benchmarks like GLUE and MMLU. These vulnerabilities manifest through benchmark exploitation, dataset contamination, and evaluation bias, creating a false perception of progress in language understanding capabilities. Through extensive review of contemporary evaluation approaches, we identify significant limitations in static benchmark designs, human evaluation protocols, and LLM-as-judge frameworks, all of which compromise the reliability of current performance assessments. As LLM capabilities evolve and existing benchmarks become redundant, we lay the groundwork for new evaluation methods that resist manipulation, minimize data contamination, and assess domain-specific tasks. This requires frameworks that are adapted dynamically, addressing current limitations and providing a more accurate reflection of LLM performance.