What is it about?
The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement. Contrary to the current understanding that offline evaluations fall short of supporting online evaluations, we indicate that when identifying the most engaging clarification questions from the user’s perspective, online and offline evaluations correspond with each other. We show that the query length does not influence the relationship between online and offline evaluations, and reducing uncertainty in online evaluation strengthens this relationship. We illustrate that an engaging clarification needs to excel from multiple perspectives, and SERP quality and characteristics of the clarification are equally important. We also investigate if human labels can enhance the performance of Large Language Models (LLMs) and Learning-to-Rank (LTR) models in identifying the most engaging clarification questions from the user’s perspective by incorporating offline evaluations as input features. Our results indicate that LTR models do not perform better than individual offline labels. However, GPT, an LLM, emerges as the standout performer, surpassing all LTR models and offline labels.
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Why is it important?
The paper "Online and Offline Evaluation in Search Clarification" addresses a crucial gap in the information retrieval field—understanding the relationship between online user engagement and offline human assessment for search clarifications. By employing both real-time user interaction (online evaluation) and human judgment (offline evaluation), this study reveals that online and offline evaluations align when identifying the most engaging clarification questions, challenging the prevailing notion that offline evaluations fall short. Our findings provide new insights into optimizing search systems by leveraging large language models, enhancing both the quality of user interactions and the efficiency of learning-to-rank models. This research offers practical guidance for improving user engagement and personalization in search engines, benefiting both academic and commercial search systems.
Perspectives
This publication represents a significant step towards bridging the gap between theoretical models and practical implementation in search systems. As a researcher deeply invested in understanding user interaction and engagement, I found the misalignment between online and offline evaluations in search clarification both intriguing and challenging. This work is the culmination of my efforts to untangle this relationship, using rigorous methodologies to reveal unexpected synergies between human judgments and real-time user behavior. Working on this study gave me the opportunity to explore the nuances of user engagement—something that is often difficult to quantify but crucial for effective information retrieval. I am particularly proud of the insights that emerged regarding the potential of large language models to enhance offline evaluations, as these findings could shape the next generation of search interfaces by making them more adaptive and user-centric. For me, this paper is not just an academic contribution, but also a hopeful move towards more human-aligned AI systems that can genuinely understand and respond to user needs.
Leila Tavakoli
Read the Original
This page is a summary of: Online and Offline Evaluation in Search Clarification, ACM Transactions on Information Systems, November 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3681786.
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