Validating Search Query Simulations: A Taxonomy of Measures

Assessing the validity of user simulators when used for the evaluation of information retrieval systems remains an open question, constraining their effective use and the reliability of simulation-based results. To address this issue, we conduct a comprehensive literature review with a particular focus on methods for the validation of simulated user queries with regard to real queries. Based on the review, we develop a taxonomy that structures the current landscape of available measures. We empirically corroborate the taxonomy by analyzing the relationships between the different measures applied to four different datasets representing diverse search scenarios. Finally, we provide concrete recommendations on which measures or combinations of measures should be considered when validating user simulation in different contexts. Furthermore, we release a dedicated library with the most commonly used measures to facilitate future research.

LISP -- A Rich Interaction Dataset and Loggable Interactive Search Platform

We present a reusable dataset and accompanying infrastructure for studying human search behavior in Interactive Information Retrieval (IIR). The dataset combines detailed interaction logs from 61 participants (122 sessions) with user characteristics, including perceptual speed, topic-specific interest, search expertise, and demographic information. To facilitate reproducibility and reuse, we provide a fully documented study setup, a web-based perceptual speed test, and a framework for conducting similar user studies. Our work allows researchers to investigate individual and contextual factors affecting search behavior, and to develop or validate user simulators that account for such variability. We illustrate the datasets potential through an illustrative analysis and release all resources as open-access, supporting reproducible research and resource sharing in the IIR community.

Sim4IA-Bench: A User Simulation Benchmark Suite for Next Query and Utterance Prediction

Validating user simulation is a difficult task due to the lack of established measures and benchmarks, which makes it challenging to assess whether a simulator accurately reflects real user behavior. As part of the Sim4IA Micro-Shared Task at the Sim4IA Workshop, SIGIR 2025, we present Sim4IA-Bench, a simulation benchmark suit for the prediction of the next queries and utterances, the first of its kind in the IR community. Our dataset as part of the suite comprises 160 real-world search sessions from the CORE search engine. For 70 of these sessions, up to 62 simulator runs are available, divided into Task A and Task B, in which different approaches predicted users next search queries or utterances. Sim4IA-Bench provides a basis for evaluating and comparing user simulation approaches and for developing new measures of simulator validity. Although modest in size, the suite represents the first publicly available benchmark that links real search sessions with simulated next-query predictions. In addition to serving as a testbed for next query prediction, it also enables exploratory studies on query reformulation behavior, intent drift, and interaction-aware retrieval evaluation. We also introduce a new measure for evaluating next-query predictions in this task. By making the suite publicly available, we aim to promote reproducible research and stimulate further work on realistic and explainable user simulation for information access: https://github.com/irgroup/Sim4IA-Bench.

Second SIGIR Workshop on Simulations for Information Access (Sim4IA 2025)

Simulations in information access (IA) have recently gained interest, as shown by various tutorials and workshops around that topic. Simulations can be key contributors to central IA research and evaluation questions, especially around interactive settings when real users are unavailable, or their participation is impossible due to ethical reasons. In addition, simulations in IA can help contribute to a better understanding of users, reduce complexity of evaluation experiments, and improve reproducibility. Building on recent developments in methods and toolkits, the second iteration of our Sim4IA workshop aims to again bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.

Evaluating Contrastive Feedback for Effective User Simulations

The use of Large Language Models (LLMs) for simulating user behavior in the domain of Interactive Information Retrieval has recently gained significant popularity. However, their application and capabilities remain highly debated and understudied. This study explores whether the underlying principles of contrastive training techniques, which have been effective for fine-tuning LLMs, can also be applied beneficially in the area of prompt engineering for user simulations. Previous research has shown that LLMs possess comprehensive world knowledge, which can be leveraged to provide accurate estimates of relevant documents. This study attempts to simulate a knowledge state by enhancing the model with additional implicit contextual information gained during the simulation. This approach enables the model to refine the scope of desired documents further. The primary objective of this study is to analyze how different modalities of contextual information influence the effectiveness of user simulations. Various user configurations were tested, where models are provided with summaries of already judged relevant, irrelevant, or both types of documents in a contrastive manner. The focus of this study is the assessment of the impact of the prompting techniques on the simulated user agent performance. We hereby lay the foundations for leveraging LLMs as part of more realistic simulated users.