Challenges in Trustworthy Human Evaluation of Chatbots (2412.04363v1)

Abstract: Open community-driven platforms like Chatbot Arena that collect user preference data from site visitors have gained a reputation as one of the most trustworthy publicly available benchmarks for LLM performance. While now standard, it is tricky to implement effective guardrails to collect high-quality annotations from humans. In this paper, we demonstrate that three sources of bad annotations, both malicious and otherwise, can corrupt the reliability of open leaderboard rankings. In particular, we show that only 10\% of poor quality votes by apathetic (site visitors not appropriately incentivized to give correct votes) or adversarial (bad actors seeking to inflate the ranking of a target model) annotators can change the rankings of models by up to 5 places on the leaderboard. Finally, we discuss open challenges in ensuring high-quality human annotations.

• The paper finds that even 10% low-quality votes can shift chatbot model rankings by up to five positions, undermining evaluation reliability.
• It scrutinizes three primary sources of annotation errors: apathetic votes, adversarial manipulations, and subjective, arbitrary judgments.
• The study advocates for improved methodologies, including rationale-based feedback and machine learning anomaly detection, to reinforce data integrity.

Challenges in Trustworthy Human Evaluation of Chatbots

The paper "Challenges in Trustworthy Human Evaluation of Chatbots" addresses the reliability and validity issues in human-generated annotations for chatbot evaluations, particularly those collected from open, community-driven platforms like Chatbot Arena. These platforms have gained significant traction as benchmarks in evaluating LLMs due to their broad accessibility and large data sets. However, the paper articulates and examines the potential pitfalls in ensuring the trustworthiness of such community-driven evaluations, given the susceptibilities to poor-quality annotations.

One of the paper's core revelations is the vulnerability of collected preference data to manipulation from three primary sources: apathetic annotators, adversarial actors, and the inherent arbitrariness in annotating open-ended queries. Through empirical analyses, the authors ascertain that a mere 10% of low-quality votes—whether due to disinterest or strategic manipulation—can alter model rankings on leaderboards by up to five places. Such findings underscore the delicate balance between openness of platforms and the integrity of collected data, raising crucial concerns about the dependence on such rankings for model evaluation and comparison.

In exploring each source of poor-quality annotations, the authors provide a deeper dive into their potential impacts and the challenges in mitigating them. Apathetic voting, wherein users are unincentivized and provide random votes, poses significant detriments to the ranking system, yet remains difficult to effectively detect post-hoc. Similarly, adversarial voting through targeted rank manipulations can be executed with relative ease given the current lax guardrails, pointing to vulnerabilities in the systems that these evaluations rely upon. The paper also illustrates that arbitrary voting results from the subjective nature of evaluating open-ended queries and can further distort the rankings, particularly in scenarios lacking significant model differentiability.

Highlighting the implications of these findings, the authors advocate for richer, more nuanced feedback processes to enhance the quality of human annotations. Strategies such as requesting rationale-based annotations, implementing reputation-based systems, and employing machine learning-driven anomaly detection are considered potential pathways to enhance annotation reliability. Moreover, they call for the integration of stronger guardrails without compromising user engagement, proposing future research directions that might encompass advanced quality control mechanisms and incentive structures to uphold the integrity of community-driven benchmarks.

From a theoretical perspective, the paper challenges assumptions about the sufficiency of current annotation methods, urging the community to rethink and reevaluate methodologies for capturing human preferences in model evaluation. Practically, the outcomes of this research demand a reassessment of open platform designs to bolster the credibility of their outputs, necessitating a recalibration of current protocols.

The implications of this work are extensive, spanning both the development of automatic evaluators that rely on such data as ground truth and the broader NLP research community's trust in these benchmarks. The insights provided are vital for the future of AI evaluations, wherein the interplay between human input and model scoring systems must be refined to account for potential biases and inaccuracies.

In summary, the paper offers a substantial contribution to the discourse on human evaluation in LLMs, emphasizing the need for vigilant oversight and robust methodologies to ensure trustworthy benchmarking in community-driven settings. This work signals an imperative for evolving quality control strategies and encourages a more nuanced understanding of human input in AI assessments.