Clarifying questions enable Conversational Search (CS) systems to resolve underspecified queries by eliciting missing information from users. However, how prompting strategies shape the quality of clarifying questions and how such questions should be evaluated at scale remains understudied. We present Automatic GENeration and evaluaTion of Clarifying Questions (AGENT-CQ) , a framework for systematically generating and evaluating clarifying questions and simulated user responses using Large Language Models (LLMs) . To support scalable and multi-perspective evaluation, we introduce CrowdLLM , an LLM-based evaluation paradigm that simulates diverse annotator judgments through distinct evaluator personas. Our experiments span both open-domain CS and a regulatory question-answering setting, allowing us to examine the extent to which clarification strategies generalize across domains with different interaction constraints. Across settings, temperature-variation prompting leads to higher quality clarifying questions than baseline prompting and human-authored questions on several dimensions of the task. In addition, LLM-generated clarifying questions lead to improved downstream retrieval performance than human-authored questions in open-domain search. Together, AGENT-CQ and CrowdLLM provide a practical framework for studying and improving clarification strategies in conversational IR systems.

Conversational search (CS) systems increasingly employ clarifying questions to refine user queries and improve the search experience.Previous studies have demonstrated the usefulness of text-based clarifying questions in enhancing both retrieval performance and user experience.While images have been shown to improve retrieval performance in various contexts, their impact on user performance, when incorporated into clarifying questions, remains largely unexplored.We conduct a user study with 73 participants to investigate the role of images in CS, specifically examining their effects on two search-related tasks: (i) answering clarifying questions, and (ii) query reformulation.We compare the effect of multimodal and text-only clarifying questions in both tasks within a CS context from various perspectives.Our findings reveal that while participants showed a strong preference for multimodal questions when answering clarifying questions, preferences were more balanced in the query reformulation task.The impact of images varied with both task type and user expertise: in answering clarifying questions, images helped maintain engagement across different expertise levels, while in query reformulation, they led to more precise queries and improved retrieval performance.Interestingly, for clarifying question answers, text-only setups demonstrated better user performance as they provided more comprehensive textual information in the absence of images.These results provide valuable insights for designing effective multimodal CS systems, highlighting that the benefits of visual augmentation are task-dependent and should be strategically implemented based on the specific search context and user characteristics.

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. Building on the success of previous workshops, which established foundations in automated judgments and RAG evaluation, this third iteration aims to address emerging challenges as IR systems become increasingly personalized and interactive. The main goal of the third LLM4Eval workshop is to bring together researchers from industry and academia to explore three critical areas: the evaluation of personalized IR systems while maintaining fairness, the boundaries between automated and human assessment in subjective scenarios, and evaluation methodologies for systems that combine multiple IR paradigms (search, recommendations, and dialogue). By examining these challenges, we seek to understand how evaluation approaches can evolve to match the sophistication of modern IR applications. The format of the workshop is interactive, including roundtable discussion sessions, fostering dialogue about the future of IR evaluation while avoiding one-sided discussions. This is the third iteration of the workshop series, following successful events at SIGIR 2024 and WSDM 2025, with the first iteration attracting over 50 participants.

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in the context of automated judgments, natural language generation, and retrieval augmented generation systems. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of LLMs with applications to LLM4Eval tasks. The main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. We also plan to run an automated judgment challenge prior to the workshop, where participants will be asked to generate labels for a given dataset while maximising correlation with human judgments. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference. This is the second iteration of the workshop. The first version was held in conjunction with SIGIR 2024, attracting over 50 participants.

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in the context of automated judgments, natural language generation, and retrieval augmented generation systems. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of LLMs with applications to LLM4Eval tasks. The main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. We also plan to run an automated judgment challenge prior to the workshop, where participants will be asked to generate labels for a given dataset while maximising correlation with human judgments. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference.

In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. In a conversational setting such signals are usually unavailable due to the nature of the interactions, and, instead, the evaluation often relies on crowdsourced evaluation labels. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems (TDSs), is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated. We explore and compare two methodologies for assessing TDSs: one includes the user's follow-up utterance and one without. We use both crowdworkers and large language models (LLMs) as annotators to assess system responses across four aspects: relevance, usefulness, interestingness, and explanation quality. Our findings indicate that there is a distinct difference in ratings assigned by both annotator groups in the two setups, indicating that user feedback does influence system evaluation. Workers are more susceptible to user feedback on usefulness and interestingness compared to LLMs on interestingness and relevance. User feedback leads to a more personalized assessment of usefulness by workers, aligning closely with the user's explicit feedback. Additionally, in cases of ambiguous or complex user requests, user feedback improves agreement among crowdworkers. These findings emphasize the significance of user feedback in refining system evaluations and suggest the potential for automated feedback integration in future research.

In mixed-initiative conversational search systems, clarifying questions aid users who struggle to express their intentions in a single query. These questions aim to uncover user's information needs and resolve query ambiguities. We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. Therefore, we propose to add images to clarifying questions and formulate the novel task of asking multimodal clarifying questions in open-domain, mixed-initiative conversational search systems. To facilitate research into this task, we collect a dataset named Melon that contains over 4k multimodal clarifying questions, enriched with over 14k images. We also propose a multimodal query clarification model named Marto and adopt a prompt-based, generative fine-tuning strategy to perform the training of different stages with different prompts. Several analyses are conducted to understand the importance of multimodal contents during the query clarification phase. Experimental results indicate that the addition of images leads to significant improvements of up to 90% in retrieval performance when selecting the relevant images. Extensive analyses are also performed to show the superiority of Marto compared with discriminative baselines.

User satisfaction depicts the effectiveness of a system from the user’s perspective. Understanding and predicting user satisfaction is vital for the design of user-oriented evaluation methods forconversational recommender systems (CRSs). Current approaches rely on turn-level satisfaction ratings to predict a user’s overall satisfaction with CRS. These methods assume that all users perceive satisfaction similarly, failing to capture the broader dialogue aspects that influence overall user satisfaction. We investigate the effect of several dialogue aspects on user satisfaction when interacting with a CRS. To this end, we annotate dialogues based on six aspects (i.e.,relevance,interestingness,understanding,task-completion,interest-arousal, andefficiency) at the turn and dialogue levels. We find that the concept of satisfaction varies per user. At the turn level, a system’s ability to make relevant recommendations is a significant factor in satisfaction. We adopt these aspects as features for predicting response quality and user satisfaction. We achieve an F1-score of 0.80 in classifying dissatisfactory dialogues, and a Pearson’srof 0.73 for turn-level response quality estimation, demonstrating the effectiveness of the proposed dialogue aspects in predicting user satisfaction and being able to identify dialogues where the system is failing. With this article, we release our annotated data. 1

Evaluation is one of the major concerns when developing information retrieval systems. Especially in the field of conversational AI, this topic has been heavily studied in the setting of both non-task and task-oriented conversational agents (dialogue systems).[1] Recently, several automatic metrics e.g., BLEU and ROUGE, proposed for the evaluation of dialogue systems, have shown poor correlation with human judgment and are thus ineffective for the evaluation of dialogue systems. As a consequence, a significant amount of research relies on human evaluation to estimate the effectiveness of dialogue systems[1, 4}.

\beginabstract \AcpDS are evaluated depending on their type and purpose. Two categories are often distinguished: \beginenumerate* \item \acpTDS, which are typically evaluated on utility, i.e., their ability to complete a specified task, and \item open-domain chat-bots, which are evaluated on the user experience, i.e., based on their ability to engage a person. \endenumerate* What is the influence of user experience on the user satisfaction rating of \acpTDS as opposed to, or in addition to, utility ? We collect data by providing an additional annotation layer for dialogues sampled from the ReDial dataset, a widely used conversational recommendation dataset. Unlike prior work, we annotate the sampled dialogues at both the turn and dialogue level on six dialogue aspects: relevance, interestingness, understanding, task completion, efficiency, and interest arousal. The annotations allow us to study how different dialogue aspects influence user satisfaction. We introduce a comprehensive set of user experience aspects derived from the annotators' open comments that can influence users' overall impression. We find that the concept of satisfaction varies across annotators and dialogues, and show that a relevant turn is significant for some annotators, while for others, an interesting turn is all they need. Our analysis indicates that the proposed user experience aspects provide a fine-grained analysis of user satisfaction that is not captured by a monolithic overall human rating. \endabstract