Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates (2410.07137v2)

Abstract: Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench, have become popular for evaluating LLMs due to their cost-effectiveness and scalability compared to human evaluation. Achieving high win rates on these benchmarks can significantly boost the promotional impact of newly released LLMs. This promotional benefit may motivate tricks, such as manipulating model output length or style to game win rates, even though several mechanisms have been developed to control length and disentangle style to reduce gameability. Nonetheless, we show that even a "null model" that always outputs a constant response (irrelevant to input instructions) can cheat automatic benchmarks and achieve top-ranked win rates: an 86.5% LC win rate on AlpacaEval 2.0; an 83.0 score on Arena-Hard-Auto; and a 9.55 score on MT-Bench. Moreover, the crafted cheating outputs are transferable because we assume that the instructions of these benchmarks (e.g., 805 samples of AlpacaEval 2.0) are private and cannot be accessed. While our experiments are primarily proof-of-concept, an adversary could use LLMs to generate more imperceptible cheating responses, unethically benefiting from high win rates and promotional impact. Our findings call for the development of anti-cheating mechanisms for reliable automatic benchmarks. The code is available at https://github.com/sail-sg/Cheating-LLM-Benchmarks.

• The paper demonstrates that null models, which output constant responses, can exploit LLM benchmarks with up to an 86.5% win rate.
• It reveals that cheating responses effectively transfer across various benchmarks using adversarial random search optimization.
• The findings emphasize the urgent need for robust anti-cheating mechanisms to ensure reliable evaluation of LLM performance.

Analysis of "Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates"

This paper examines the vulnerabilities in automated evaluation systems for LLMs by demonstrating how "null models," which output constant, irrelevant text, can exploit these systems to achieve deceptively high scores. The authors critique the reliability of such benchmarks and propose the urgent need for anti-cheating mechanisms.

Overview

The research aims to shed light on the potential manipulation of automatic benchmarks like AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench. These benchmarks leverage LLM-based auto-annotators to assess the performance of LLMs instead of relying on labor-intensive human evaluation. However, the authors argue that such systems, despite their efficiency and high correlation with human judgment, are susceptible to gaming.

Key Findings

• Null Model Effectiveness: The null model, which produces a fixed response regardless of input, was shown to exploit automatic benchmarks effectively. It achieved an 86.5% LC win rate on AlpacaEval 2.0, an 83.0 score on Arena-Hard-Auto, and a 9.55 score on MT-Bench.
• Transferability of Cheating Outputs: These cheating responses are crafted without access to specific benchmark instructions, yet they maintain effectiveness across various benchmarks, indicating a general weakness in these evaluation systems.
• Optimization via Random Search: The researchers utilized a random search algorithm to enhance the null model's constant outputs with adversarial prefixes, further improving their win rates.

Implications

The paper's findings have significant implications for both researchers and developers of LLMs:

• Reliability of Benchmarks: The demonstrated vulnerability questions the reliability of automated benchmarks as definitive measures of model performance, urging reconsideration in their application and development.
• Potential for Malicious Exploitation: An adversary could leverage these techniques unethically, gaining undeserved promotional benefits by achieving falsely inflated benchmark scores.
• Need for Robust Mechanisms: There's a clear need to incorporate robust anti-cheating measures into these systems to ensure their reliability and integrity.

Future Directions

The paper proposes several avenues for further research and development:

• Development of Anti-Cheating Mechanisms: Future work should focus on creating more secure and robust evaluation frameworks to withstand adversarial manipulation.
• Exploration of LLM Vulnerabilities: Understanding intrinsic weaknesses in LLMs that contribute to such vulnerabilities is crucial for improving both model alignment and evaluation systems.
• Broader Benchmark Analysis: Extending this analysis to other emerging benchmarks could provide a more comprehensive understanding of the issue.

In conclusion, while automatic LLM benchmarks offer an efficient means of evaluation, this paper highlights their current limitations and the need for refined methodologies to mitigate potential exploitation. Addressing these challenges is vital for ensuring that LLM evaluations reflect true model capabilities and support the broader AI research community effectively.