LLM-Driven Usefulness Judgment for Web Search Evaluation

Evaluation is fundamental in optimizing search experiences and supporting diverse user intents in Information Retrieval (IR). Traditional search evaluation methods primarily rely on relevance labels, which assess how well retrieved documents match a user's query. However, relevance alone fails to capture a search system's effectiveness in helping users achieve their search goals, making usefulness a critical evaluation criterion. In this paper, we explore an alternative approach: LLM-generated usefulness labels, which incorporate both implicit and explicit user behavior signals to evaluate document usefulness. We propose Task-aware Rubric-based Usefulness Evaluation (TRUE), a rubric-driven evaluation method that employs iterative sampling and reasoning to model complex search behavior patterns. Our findings show that (i) LLMs can generate moderate usefulness labels by leveraging comprehensive search session history incorporating personalization and contextual understanding, and (ii) fine-tuned LLMs improve usefulness judgments when provided with structured search session contexts. Additionally, we examine whether LLMs can distinguish between relevance and usefulness, particularly in cases where this divergence impacts search success. We also conduct an ablation study to identify key metrics for accurate usefulness label generation, optimizing for token efficiency and cost-effectiveness in real-world applications. This study advances LLM-based usefulness evaluation by refining key user metrics, exploring LLM-generated label reliability, and ensuring feasibility for large-scale search systems.

LLM-Driven Usefulness Labeling for IR Evaluation

In the information retrieval (IR) domain, evaluation plays a crucial role in optimizing search experiences and supporting diverse user intents. In the recent LLM era, research has been conducted to automate document relevance labels, as these labels have traditionally been assigned by crowd-sourced workers - a process that is both time and consuming and costly. This study focuses on LLM-generated usefulness labels, a crucial evaluation metric that considers the user's search intents and task objectives, an aspect where relevance falls short. Our experiment utilizes task-level, query-level, and document-level features along with user search behavior signals, which are essential in defining the usefulness of a document. Our research finds that (i) pre-trained LLMs can generate moderate usefulness labels by understanding the comprehensive search task session, (ii) pre-trained LLMs perform better judgement in short search sessions when provided with search session contexts. Additionally, we investigated whether LLMs can capture the unique divergence between relevance and usefulness, along with conducting an ablation study to identify the most critical metrics for accurate usefulness label generation. In conclusion, this work explores LLM-generated usefulness labels by evaluating critical metrics and optimizing for practicality in real-world settings.

ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs

In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter. Many fact-checking approaches and tools have been developed, but they often lack appropriate explainability or granularity to be useful in various contexts. A text validation method that is easy to use, accessible, and can perform fine-grained evidence attribution has become crucial. More importantly, building user trust in such a method requires presenting the rationale behind each prediction, as research shows this significantly influences people's belief in automated systems. It is also paramount to localize and bring users' attention to the specific problematic content, instead of providing simple blanket labels. In this paper, we present $\textit{ClaimVer, a human-centric framework}$ tailored to meet users' informational and verification needs by generating rich annotations and thereby reducing cognitive load. Designed to deliver comprehensive evaluations of texts, it highlights each claim, verifies it against a trusted knowledge graph (KG), presents the evidence, and provides succinct, clear explanations for each claim prediction. Finally, our framework introduces an attribution score, enhancing applicability across a wide range of downstream tasks.