Generative Information Retrieval Evaluation (2404.08137v2)

Abstract: This paper is a draft of a chapter intended to appear in a forthcoming book on generative information retrieval, co-edited by Chirag Shah and Ryen White. In this chapter, we consider generative information retrieval evaluation from two distinct but interrelated perspectives. First, LLMs themselves are rapidly becoming tools for evaluation, with current research indicating that LLMs may be superior to crowdsource workers and other paid assessors on basic relevance judgement tasks. We review past and ongoing related research, including speculation on the future of shared task initiatives, such as TREC, and a discussion on the continuing need for human assessments. Second, we consider the evaluation of emerging LLM-based generative information retrieval (GenIR) systems, including retrieval augmented generation (RAG) systems. We consider approaches that focus both on the end-to-end evaluation of GenIR systems and on the evaluation of a retrieval component as an element in a RAG system. Going forward, we expect the evaluation of GenIR systems to be at least partially based on LLM-based assessment, creating an apparent circularity, with a system seemingly evaluating its own output. We resolve this apparent circularity in two ways: 1) by viewing LLM-based assessment as a form of "slow search", where a slower IR system is used for evaluation and training of a faster production IR system; and 2) by recognizing a continuing need to ground evaluation in human assessment, even if the characteristics of that human assessment must change.

Evaluating Generative Information Retrieval Systems: Challenges and Opportunities

Introduction to Generative Information Retrieval Systems

The advent of Generative Information Retrieval (GenIR) systems, such as Retrieval Augmented Generation (RAG) systems, introduces both challenges and opportunities for the Information Retrieval (IR) community. These systems, which leverage LLMs for both generating responses and evaluating IR systems, require a reevaluation of traditional evaluation methodologies. This post explores the implications of GenIR systems for IR evaluation, highlighting the dual perspective of using LLMs for evaluation and the evaluation of LLM-based GenIR systems themselves.

LLMs in IR System Evaluation

The integration of LLMs into the IR evaluation process signifies a pivotal shift. Research findings indicate that LLMs can potentially surpass human annotators in generating relevance judgments, offering a more cost-effective and consistent alternative. This shift not only challenges the necessity of traditional document pooling approaches but also opens avenues for refining relevance judgments to incorporate multiple dimensions of document utility, addressing diverse user needs. The historical comparison to the democratization of aluminum underscores the potential transformation in IR evaluation efficiency and accessibility, paralleling the significant reduction in costs reminiscent of the Hall-Héroult process for aluminum production.

Advancements and Challenges in GenIR System Evaluation

Evaluating GenIR Systems

The novel structure of GenIR systems, characterized by their departure from the traditional 'ten blue links' format to a more conversational interaction and synthesized information presentation, necessitates a reimagined approach to evaluation. This includes the end-to-end evaluation of system output and the scrutiny of components within a RAG system. The autonomy of LLMs in generating document relevance labels and the exploration of personalized relevance criteria pose both practical advantages and conceptual challenges, particularly in maintaining the relevance and validity of human-grounded evaluation benchmarks.

The Role of RAG Architecture

The intricate architecture of RAG systems, featuring a blend of retrieval components and generative models, complicates traditional evaluation strategies. While one can evaluate the retrieval component akin to standard IR systems, assessing the generative component requires a nuanced understanding of the 'infinite corpus' it operates within. This highlights the need for innovative evaluation metrics that account for the Generative IR system's ability to synthesize responses from an expansive, dynamic information source.

The Future of IR Evaluation and GenIR Systems

The evolution of GenIR systems and the integration of LLMs in the IR evaluation process certainly foreshadow a transformative period for the IR field, challenging established doctrines and inviting a reevaluation of foundational principles. The speculative discussion on the future of shared task initiatives, like TREC, in light of these developments underscores the potential for a paradigm shift in how IR researchers collaborate, share resources, and operationalize evaluation.

Speculative Considerations and Grounding Simulations

As the IR community navigates these changes, critical considerations emerge regarding the circularity in using LLMs for evaluating IR systems, reminiscent of the pseudo relevance feedback approach. Moreover, this progression raises pertinent questions about the extent to which GenIR systems can independently evaluate their output without sacrificing the objectivity and reliability historically attributed to human-grounded relevance judgments. The proposition of a 'Slow Search' model for evaluation, emphasizing a trade-off between retrieval efficiency and effectiveness, encapsulates the ongoing deliberations over balancing technological advancements with ethical and practical considerations in system evaluation.

Concluding Remarks

In conclusion, the integration of LLMs into IR evaluations and the advent of GenIR systems represent a pivotal juncture for the IR community, challenging traditional evaluation paradigms and necessitating a forward-looking perspective on the field’s foundational principles. As the capabilities of LLMs continue to evolve, so too will the methodologies and frameworks for evaluating both existing and emergent IR systems, underscoring the need for continuous innovation, critical examination of new challenges, and the ethical considerations underlying the deployment of these advanced technologies in real-world contexts.