Accepted Papers

The standard practice of query performance prediction (QPP) evaluation is to measure a set-level correlation between the estimated retrieval qualities and the true ones. However, this correlation-based evaluation measure quantifies QPP effectiveness at the level of individual queries, nor does this ...

Large Language Models (LLMs) are increasingly promoted for knowledge-intensive reasoning tasks. Effective oversight requires faithful reasoning traces which show how answers are actually produced. Chain-of-Thought (CoT) prompting is positioned as a technique to promote both accuracy and transparency...

To overcome the problem of data sparsity in recommendation systems, cross-domain recommendation (CDR) models leverage user data from a source domain to improve recommendations in a target domain. However, different CDR models capture unique and often complementary aspects of user preferences. This p...

Real-world use cases often present RAG systems with complex queries for which relevant information is missing from the corpus or is incomplete. In these settings, RAG systems must be able to reject unanswerable, out-of-scope queries and identify failures of retrieval and multi-hop reasoning. Despite...

Comprehending genomic information is essential for biomedical research, yet extracting data from complex distributed databases remains challenging. Large language models (LLMs) offer potential for genomic Question Answering (QA) but face limitations due to restricted access to domain-specific databa...

Municipal meeting minutes are official documents of local governance, exhibiting heterogeneous formats and writing styles. Effective information retrieval (IR) requires identifying metadata such as meeting number, date, location, participants, and start/end times, elements that are rarely standardiz...

Event extraction (EE) aims to identify event triggers and their corresponding arguments from unstructured text, providing structured knowledge essential for many downstream tasks. Despite the success of instruction-tuned large language models (LLMs), current methods often produce inconsistent format...

Retrieval-Augmented Generation (RAG) systems are susceptible to factual inconsistencies when retrieved evidence is conflicting, a common issue with open-domain sources. Prevailing multi-agent approaches attempt to resolve this through unstructured debates that treat all information sources as equall...

Building a user model that incorporates diverse tasks remains a big challenge. While continual learning offers an alternative to multi-task learning by eliminating the need for retraining on all past tasks, prior works train the whole network backbone along with task-specific masks, which becomes co...

Traditionally, the task of relevance judgment has relied on human annotators, but recent advances in Large Language Models (LLMs) have prompted growing interest in their use as proxy to provide fully- or semi-automated judgments of relevance. In this setting, a key yet underexplored factor is the ro...

Generative retrieval (GR) models encode a corpus within model parameters and generate relevant document identifiers directly for a given query. While this paradigm shows promise in retrieval tasks, existing GR models struggle with complex queries in numerical contexts, such as those involving semant...

Composing messages in chatbot interactions is often time-consuming, making autocompletion an appealing way to reduce user effort. Different users have different preferences and therefore different expectations from autocompletion solutions. We study how personalization can improve the autocompletion...

Query performance prediction (QPP) methods have primarily been tailored to mainstream users, thus relying on the traditional concept of relevance. In the case of children, however, relevance goes beyond content-based resource-query matching, which is why we gauge the performance of existing QPP meth...

Many analyses have been performed on Information Retrieval (IR) evaluation benchmarks, with many different approaches. Benchmarking also plays a central role in evaluating the capabilities of Large Language Models (LLMs). However, recent concerns have emerged regarding the robustness of benchmarks a...

The presence of social biases in large language models (LLMs) has become a significant concern in AI research. These biases, often embedded in training data, can perpetuate harmful stereotypes and distort decision-making processes. When LLMs are integrated into ranking systems, they can propagate th...

Effective video retrieval has historically relied heavily on textual descriptions and metadata. However, recent advances in vision-language models (VLMs) prompt the question: Are text features still essential for effective video retrieval? In this work, we investigate this question using a unified m...

Large Language Models are increasingly used as retrieval and reasoning agents in specialized domains. This study evaluates their performance on cybersecurity Capture-the-Flag challenges, reframed as structured retrieval tasks where models must infer information from textual and code-based evidence. ...

Text retrieval using learned sparse representations of queries and documents has, over the years, evolved into a highly effective approach to search. It is thanks to recent advances in approximate nearest neighbor search---with the emergence of highly efficient algorithms such as the inverted index-...

Query expansion is a long-standing technique to mitigate vocabulary mismatch in ad hoc Information Retrieval. Pseudo-relevance feedback methods, such as RM3, estimate an expanded query model from the top-ranked documents, but remain vulnerable to topic drift when early results include noisy or tange...

This paper investigates the potential of LLMs for automatically annotating the usefulness, supportiveness, and credibility of search results. These aspects, while essential to the construction of misinformation benchmarks, are expensive and difficult to obtain at scale. Our comparative study suggest...

Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet...

We present ReFormeR, a pattern-guided approach for query reformulation. Instead of prompting a language model to generate reformulations of a query directly, ReFormeR first elicits short reformulation patterns from pairs of initial queries and empirically stronger reformulations, consolidates them i...