Temporal information is crucial for information retrieval, yet most dense retrieval systems focus exclusively on semantic similarity while neglecting temporal alignment between queries and documents. We propose TempRetriever, a lightweight framework that explicitly incorporates temporal information into dense passage retrieval through learned fusion techniques. Unlike existing approaches requiring extensive architectural modifications or specialized pre-training, TempRetriever enhances standard dense retrievers by combining semantic embeddings with temporal representations using four fusion strategies: Feature Stacking, Vector Summation, Relative Embeddings, and Element-Wise Interaction. Our approach introduces a learned temporal encoder and time-based negative sampling strategy to address temporal misalignment during training. We evaluate TempRetriever on three temporal question answering datasets (ArchivalQA, ChroniclingAmericaQA, NobelPrize) spanning altogether years from 1800 to 2022. TempRetriever achieves substantial improvements over standard DPR: 6.86% on ArchivalQA (Recall@1) and 4.40% on ChroniclingAmericaQA (Recall@1). Our method also outperforms state-of-the-art temporal retrieval systems, obtaining 9.62% improvement over BiTimeBERT and 5.16% over TS-Retriever. Notably, TempRetriever's fusion techniques can enhance existing temporal methods, improving BiTimeBERT by 5.12% and TS-Retriever by 6.17%, demonstrating modularity and practical value. Zero-shot evaluation confirms strong generalization across domains, and integration with retrieval-augmented generation shows consistent end-to-end improvements.

Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a pre-computed document similarity graph in reranking. However, as such graphs are often unavailable, or incur quadratic memory costs even when available, graph-free rerankers leverage large language model (LLM) calls to achieve competitive performance. We introduce L2G, a novel framework that implicitly induces document graphs from listwise reranker logs. By converting reranker signals into a graph structure, L2G enables scalable graph-based retrieval without the overhead of explicit graph computation. Results on the TREC-DL and BEIR subset show that L2G matches the effectiveness of oracle-based graph methods, while incurring zero additional LLM calls.

Modern retrieval pipelines increasingly rely on query reformulation and neural reranking to improve effectiveness, but this comes at a significant computational cost and introduces a fundamental tradeoff between recall and query drift. Generating many reformulated queries can substantially increase recall, yet naïvely merging or exhaustively reranking their results is prohibitively expensive. In this work, we argue that the core challenge is not reformulation generation itself, but the adaptive selection of reformulations and their retrieved documents under a strict inference budget. We propose ReformIR, a budget-aware retrieval framework that treats query reformulations as first-class features and performs online relevance estimation using a strong reranker as a teacher. Given multiple reformulated queries, ReformIR constructs a large candidate pool and learns a lightweight surrogate model that estimates document utility from reformulation-specific retrieval signals. Under a fixed reranking budget, the surrogate adaptively prioritizes both reformulations and documents, selectively querying a teacher reranker anchored to the original query. This process increases recall while actively suppressing drift through online feature selection over reformulations. We conduct extensive experiments on the MSMARCO passage corpora and TREC Deep Learning benchmarks (DL19-DL22), evaluating effectiveness under realistic reranking budgets. Our results show that ReformIR consistently outperforms existing reformulation strategies, particularly as the number of reformulations increases, where prior methods suffer from severe quality degradation due to drift. Our findings also suggest a shift in retrieval system design: rather than using large language models as rerankers, their capacity is more effectively leveraged in the reformulation stage with feedback-driven optimization. Our code: https://github.com/VenkteshV/ReformIR

Retrieval-Augmented Generation (RAG) has emerged as a standard framework for knowledge-intensive NLP tasks, combining large language models (LLMs) with document retrieval from external corpora. Despite its widespread use, most RAG pipelines continue to treat retrieval and reasoning as isolated components—retrieving documents once and then generating answers without further interaction. This static design often limits performance on complex tasks that require iterative evidence gathering or high-precision retrieval. Recent work in both the information retrieval (IR) and NLP communities has begun to close this gap by introducing adaptive retrieval and ranking methods that incorporate feedback. In this survey, we present a structured overview of advanced retrieval and ranking mechanisms that integrate such feedback. We categorize feedback signals based on their source and role in improving the query, retrieved context, or document pool. By consolidating these developments, we aim to bridge IR and NLP perspectives and highlight retrieval as a dynamic, learnable component of end-to-end RAG systems.

As models grow more complex and societal demands for transparency increase with emerging regulations, explainability has become an even more important research area. However, despite its recognized relevance, explainability research in IR has seen slower progress than in related fields. This full day workshop aims to advance research in explainable information retrieval by providing a more in-depth platform to reflect on recent developments and facilitate discussions to address new and persistent challenges. Our goal is to bring together a diverse group of researchers to build a shared understanding of key tasks and challenges that will lay the foundation for the future of explainable IR research.

Large Language Models (LLMs) have demonstrated immense advances in a wide range of natural language tasks. However, these models are susceptible to hallucinations and errors on particularly temporal understanding tasks involving multiple entities in answers. In such tasks, they fail to associate entities with accurate time intervals, generate a complete list of entities in answers or reason about events associated with specific temporal bounds. Existing works do not extensively evaluate the abilities of the model to perform implicit and explicit temporal understanding in a list answer construction setup. To bridge this gap, we propose the Time referenced List based Question Answering or TLQA benchmark that requires structured answers in list format aligned with corresponding time periods. Our TLQA benchmark, requires both list construction and temporal understanding simultaneously, which to the best of our knowledge has not been explored in prior benchmarks. We investigate the temporal understanding and list construction capabilities of state-of-the-art generative models on TLQA in closed-book and open-domain settings. Our findings reveal significant shortcomings in current models, particularly their inability to provide complete answers and temporally align facts in a closed-book setup and the need to improve retrieval in open-domain setup, providing clear future directions for research on TLQA. The benchmark and code can be publicly accessed at https://github.com/elixir-research-group/TLQA.

While recent advancements in Neural Ranking Models have resulted in significant improvements over traditional statistical retrieval models, it is generally acknowledged that the use of large neural architectures and the application of complex language models in Information Retrieval (IR) have reduced the transparency of retrieval methods. Consequently, Explainability and Interpretability have emerged as important research topics in IR. Several axiomatic and post-hoc explanation methods, as well as approaches that attempt to be interpretable-by-design, have been proposed. We present ir_explain, an open-source Python library that implements a variety of well-known techniques for Explainable IR (ExIR) within a common, extensible framework. It supports the three standard categories of post-hoc explanations, namely pointwise, pairwise, and listwise explanations. The library is designed to make it easy to reproduce state-of-the-art ExIR baselines on standard test collections, as well as to explore new approaches to explaining IR models and methods. To facilitate adoption, ir_explain is well-integrated with widely-used toolkits such as Pyserini, PyTerrier (work in progress) and ir_datasets. Downstream applications of ir_explain include explaining the Retrieval-Augmented Generation (RAG) pipeline. The development version of the library is available on GitHub. We release the library as a pip package (https://pypi.org/project/ir-explain/); source code is available from https://github.com/souravsaha/ir_explain.

Large language models (LLMs) have transformed information retrieval through chat interfaces, but their hallucination tendencies pose significant risks. While Retrieval Augmented Generation (RAG) with citations has emerged as a solution by allowing users to verify responses through source attribution, current evaluation approaches focus primarily on citation correctness - whether cited documents support the corresponding statements. This is insufficient and we introduce citation faithfulness - whether the model's reliance on cited documents is genuine rather than post-rationalized to fit pre-existing knowledge. Our contributions are threefold: (i) we introduce coherent notions of attribution and introduce the concept of citation faithfulness; (ii) we propose desiderata for citations beyond correctness and accuracy needed for trustworthy systems; and (iii) we emphasize evaluating citation faithfulness by studying post-rationalization. Through experimentation, we reveal prevalent post-rationalization issues, finding that up to 57% of citations lack faithfulness. This undermines reliable attribution and may result in misplaced trust, highlighting a critical gap in current LLM-based IR systems. We demonstrate why both citation correctness and faithfulness must be considered when deploying LLMs in IR applications, contributing to a broader discussion of building more reliable and transparent information access systems.