Perspectives on Large Language Models for Relevance Judgment (2304.09161v2)

Abstract: When asked, LLMs like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems. In this perspectives paper, we discuss possible ways for LLMs to support relevance judgments along with concerns and issues that arise. We devise a human--machine collaboration spectrum that allows to categorize different relevance judgment strategies, based on how much humans rely on machines. For the extreme point of "fully automated judgments", we further include a pilot experiment on whether LLM-based relevance judgments correlate with judgments from trained human assessors. We conclude the paper by providing opposing perspectives for and against the use of~LLMs for automatic relevance judgments, and a compromise perspective, informed by our analyses of the literature, our preliminary experimental evidence, and our experience as IR researchers.

An Overview of "Perspectives on LLMs for Relevance Judgment"

The paper "Perspectives on LLMs for Relevance Judgment" discusses the utility and challenges of employing LLMs in the context of relevance judgments in information retrieval (IR). Relevance judgments are critical for evaluating IR systems, but they have traditionally required significant human effort. This paper explores how LLMs could potentially support or enhance relevance judgment tasks.

Key Considerations in Relevance Judgment

1. Human vs. Machine Collaboration: The authors introduce a spectrum of human-machine collaboration for relevance judgments, ranging from fully manual to fully automated systems. This spectrum includes various degrees of LLM assistance alongside human judgment.
2. Feasibility of Fully Automated Judgments: The paper discusses a pilot experiment assessing the capability of LLMs to replace human assessors. It evaluates whether judgments made by LLMs align with those made by human experts. The experiment investigates different LLMs like GPT-3.5 and YouChat, assessing their effectiveness on collections such as TREC-8 and TREC Deep Learning 2021.
3. Challenges and Concerns: While LLMs exhibit potential, the paper highlights several challenges, including the inherent biases of LLMs, the opaque nature of their decision-making processes, and the need for explainability. It questions the reliability of LLMs in scenarios where factual accuracy is paramount, such as determining the relevance of a document that espouses potentially misleading claims.
4. Open Issues and Opportunities: The authors identify open issues such as the cost and quality balance of LLM judgments, human verification needs, truthfulness of LLM-generated content, and inherent biases. Furthermore, they explore potential future roles of LLMs, possibly surpassing human capabilities in certain relevance judgment contexts.

Numerical Results and Implications

The paper presents experimental results indicating a promising, albeit inconsistent, correlation between LLM-based judgments and human judgments across different datasets. It further analyzes the implications of these findings for the development and deployment of LLMs in IR evaluation. The results suggest the potential of LLMs to reduce the burden of relevance judgments, although significant hurdles remain before achieving fully automated systems.

Future Directions in AI and IR

This research prompts several speculative considerations regarding future AI developments. First, optimizing the integration of LLMs in human-machine collaboration domains could lead to more efficient and consistent IR evaluation processes. Secondly, addressing the limitations and biases of LLMs may enhance their trustworthiness for critical tasks involving factual relevance judgments.

The paper concludes with a balanced discussion, juxtaposing optimism about LLM technology's impact on IR processes against caution regarding premature reliance on these models in complex or high-stakes scenarios. It emphasizes the need for ongoing research into LLM applications in relevance judgment tasks and highlights both the theoretical developments and practical applications that could arise from future advancements in AI.