Limitations of Using Large Language Models for Relevance Judgments in Information Retrieval Evaluation

Concetti Chiave

Using large language models (LLMs) to generate relevance judgments for information retrieval evaluation is problematic, as it limits the ability to measure systems that may outperform the LLM-generated judgments.

Sintesi

The author discusses the limitations of using large language models (LLMs) to create relevance judgments for information retrieval (IR) evaluation. The key points are:

Relevance judgments in Cranfield-style evaluations represent the "ideal" performance, as they are made by human assessors. Any system that performs better than the relevance judgments will be measured as performing worse, as the evaluation is limited by the ground truth data.

If the LLM used to generate the relevance judgments is also a component of the evaluated systems, those systems will be measured as performing poorly even if they improve upon the LLM, as they will retrieve unjudged or incorrectly judged documents.

The relevance judgments can only measure systems that perform worse than the state-of-the-art model at the time the judgments were created. New, potentially superior models will be measured as performing poorly compared to the older judgments.

Instead of using LLMs to generate the ground truth, the author suggests exploring ways to use LLMs to support the evaluation process, such as automating quality control or assisting in user studies, without using them to create the relevance judgments themselves.

The author concludes that while LLMs have great potential for IR, using them to generate the evaluation ground truth is fundamentally limited and should be avoided.

Statistiche

The paper does not contain any specific metrics or figures. The key insights are conceptual, discussing the limitations of using LLMs for relevance judgments.

Citazioni

"The bottom-line-up-front message is, don't use LLMs to create relevance judgments for TREC-style evaluations."
"If we believed that a model was a good assessor of relevance, then we would just use it as the system. Why would we do otherwise?"
"Obviously, the human that created the relevance judgments is not entirely ideal. The assessor is not all-knowing, all-seeing, all-reading with perfect clarity."

Approfondimenti chiave tratti da

Don't Use LLMs to Make Relevance Judgments

by Ian Soboroff alle arxiv.org 09-24-2024

https://arxiv.org/pdf/2409.15133.pdf

Domande più approfondite

How could LLMs be used to assist in the relevance judgment process without generating the ground truth?

Large Language Models (LLMs) can play a supportive role in the relevance judgment process by enhancing the efficiency and accuracy of human assessors rather than generating the ground truth themselves. One potential application is to use LLMs as a quality control mechanism, where they can analyze the relevance judgments made by human assessors and flag potential inconsistencies or errors. This could involve the LLM reviewing a subset of judgments and providing feedback or suggestions based on patterns it recognizes from prior assessments.
Additionally, LLMs can assist in the training of human assessors by providing examples of relevant and non-relevant documents based on established criteria, thereby helping to calibrate their judgment. They can also facilitate the creation of training datasets by summarizing large volumes of documents, allowing assessors to focus on the most pertinent information. Furthermore, LLMs can be employed to automate the coding of qualitative observations in user studies, thus streamlining the evaluation process without directly influencing the ground truth.

What alternative evaluation methodologies could be explored to overcome the limitations of Cranfield-style evaluations when using advanced models like LLMs?

To address the limitations of Cranfield-style evaluations, which rely heavily on static relevance judgments, several alternative methodologies can be explored. One promising approach is the use of dynamic evaluation frameworks that incorporate user feedback and behavioral data over time. This could involve continuous learning systems that adapt based on real-time user interactions, allowing for a more nuanced understanding of relevance that evolves with user needs.
Another alternative is the implementation of multi-faceted evaluation metrics that go beyond traditional precision and recall. Metrics such as user satisfaction, engagement, and task completion rates can provide a more holistic view of system performance. Additionally, employing ensemble methods that combine outputs from multiple models, including LLMs fine-tuned on relevance data, can yield more robust evaluations by leveraging diverse perspectives on relevance.
Finally, incorporating user studies that assess the effectiveness of retrieval systems in real-world scenarios can provide valuable insights. These studies can utilize A/B testing methodologies to compare different systems based on user interactions, thus capturing the contextual and subjective nature of relevance.

How might the concept of "super-human" performance be redefined or measured in the context of information retrieval evaluation?

The concept of "super-human" performance in information retrieval (IR) evaluation can be redefined by shifting the focus from absolute accuracy in relevance judgments to the ability of a system to discover and rank relevant documents that were previously unjudged or misjudged. Instead of relying solely on traditional metrics that compare system outputs against a fixed set of relevance judgments, a more dynamic approach could involve measuring a system's capacity to identify relevant documents that enhance user satisfaction and task success.
To operationalize this redefinition, performance could be assessed through metrics that account for the discovery of novel relevant documents, such as the inclusion of documents that were not part of the original relevance judgments but are deemed relevant by users in practice. Additionally, evaluating systems based on their ability to adapt to user feedback and improve over time can provide a more accurate measure of their effectiveness.
Moreover, the use of advanced models like LLMs can facilitate the identification of relevance patterns that human assessors may overlook, thus allowing for a more comprehensive evaluation of system performance. By focusing on the practical impact of retrieval systems on user experience rather than strictly adhering to predefined relevance judgments, the notion of "super-human" performance can evolve to reflect a system's true utility in real-world applications.