Human relevance assessment is time-consuming and cognitively intensive, limiting the scalability of Information Retrieval evaluation. This has led to growing interest in using large language models (LLMs) as proxies for human judges. However, it remains an open question whether LLM-based relevance judgments are reliable, stable, and rigorous enough to match humans for relevance assessment. In this work, we conduct a study of overrating behavior in LLM-based relevance judgments across model backbones, evaluation paradigms (pointwise and pairwise), and passage modification strategies. We show that models consistently assign inflated relevance scores-often with high confidence-to passages that do not genuinely satisfy the underlying information need, revealing a system-wide bias rather than random fluctuations in judgment. Furthermore, controlled experiments show that LLM-based relevance judgments can be highly sensitive to passage length and surface-level lexical cues. These results raise concerns about the usage of LLMs as drop-in replacements for human relevance assessors, and highlight the urgent need for careful diagnostic evaluation frameworks when applying LLMs for relevance assessments. Our code and results are publicly available. https://github.com/chutingyu/Exploring-Overrating

While large amounts of potentially useful agricultural resources (journal articles, manuals, reports) are available, their value cannot be realised if they cannot be easily searched and presented to the agriculture users in a digestible form.AgAsk is a conversational search system for the agricultural domain, providing tailored answers to growers questions. AgAsk is underpinned by an efficient and effective neural passage ranking model fine-tuned on real world growers' questions. An adaptable, messaging-style user interface is deployed via the Telegram messaging platform, allowing users to ask natural language questions via text or voice, and receive short natural language answers as replies.

Boolean query construction is often critical for medical systematic review literature search. To create an effective Boolean query, systematic review researchers typically spend weeks coming up with effective query terms and combinations. One challenge to creating an effective systematic review Boolean query is the selection of effective MeSH Terms to include in the query. In our previous work, we created neural MeSH term suggestion methods and compared them to state-of-the-art MeSH term suggestion methods. We found neural MeSH term suggestion methods to be highly effective.

Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of bag-of-words retrievers. At the same time, deep language models have been shown to outperform traditional bag-of-words rerankers. However, it is unclear how to integrate PRF directly with emergent deep language models. This article addresses this gap by investigating methods for integrating PRF signals with rerankers and dense retrievers based on deep language models. We consider text-based, vector-based and hybrid PRF approaches and investigate different ways of combining and scoring relevance signals. An extensive empirical evaluation was conducted across four different datasets and two task settings (retrieval and ranking). Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets. We found that the best effectiveness was achieved when (i) directly concatenating each PRF passage with the query, searching with the new set of queries, and then aggregating the scores; (ii) using Borda to aggregate scores from PRF runs. Vector-based PRF results show that the use of PRF enhanced the effectiveness of deep rerankers and dense retrievers over several evaluation metrics. We found that higher effectiveness was achieved when (i) the query retains either the majority or the same weight within the PRF mechanism, and (ii) a shallower PRF signal (i.e., a smaller number of top-ranked passages) was employed, rather than a deeper signal. Our vector-based PRF method is computationally efficient; thus, this represents a general PRF method others can use with deep rerankers and dense retrievers.

Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF methods may well depend on the quality of the feedback signal and thus on the effectiveness of the first-stage ranker. This aspect however has received little attention before.

Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches such as bag-of-words methods, e.g., BM25) and dense retrievers. Each of these categories appears to capture different characteristics of relevance. Previous work has investigated how relevance signals from sparse retrievers could be combined with those from dense retrievers via interpolation. Such interpolation would generally lead to higher retrieval effectiveness.

In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is available as implicit feedback. We then exploit such historic implicit interactions to improve the effectiveness of a DR. A key challenge that we study is the effect that biases in the click signal, such as position bias, have on the DRs. To overcome the problems associated with the presence of such bias, we propose the Counterfactual Rocchio (CoRocchio) algorithm for exploiting implicit feedback in Dense Retrievers. We demonstrate both theoretically and empirically that dense query representations learnt with CoRocchio are unbiased with respect to position bias and lead to higher retrieval effectiveness. We make available the implementations of the proposed methods and the experimental framework, along with all results at https://github.com/ielab/Counterfactual-DR.

Systematic reviews are used widely in the biomedical and healthcare domains. Systematic reviews aim to provide a complete and exhaustive overview of the medical literature for a specific research question. Core to the construction of a systematic review is the search strategy. The main component of a search strategy is a complex Boolean query, typically developed by information specialists (e.g., librarians). The aim of the search strategy is to retrieve relevant studies that will contribute to the outcomes of the systematic review. One barrier information specialists face when developing a search strategy is the enormous amount of medical literature that exists in databases. This vast amount of literature means that search strategies often suffer from biases (e.g., lack of expertise, overconfidence, limited knowledge of the domain) and are incomplete, or retrieve far too many studies (possibly as a result of the biases, but also due to the tools used to develop search strategies). Retrieving too many studies impacts the time and financial costs of the review, and retrieving too few studies may impact the outcomes of the review. Therefore, it is vital to support expert searchers develop effective search strategies. In this paper, we present a novel end-to-end set of advanced tools for information specialists. These tools are tightly integrated into an existing Open Source search strategy refining package (searchrefiner). These tools aim to address the problems associated with search strategy development by providing a complete framework from query development, to refinement, to documentation. The implementation of these tools also offers a glimpse at the ease at which related tools may be implemented within the searchrefiner ecosystem. More information about the tools including installation, documentation, and screenshots is made available on the searchrefiner website: https://ielab.io/searchrefiner.