Large Language Models (LLMs) are increasingly used to replace human judges to assess the relevance of information objects, raising concerns about circularity, bias, and whether simulated preferences can substitute for human judgement. This work presents experiments using multiple LLMs to label passages for relevance. It examines their gullibility – how easily they are misled into labelling irrelevant passages as relevant. It also compares LLMs with human judges in ranking systems, analysing differences in discriminative power and whether some systems benefit under LLM-based evaluation. Results show that LLMs are influenced by the presence of query terms, even with irrelevant or random passages. Moreover, LLM-generated rankings are highly correlated with those of human judges, with strong agreement on which system is better in pairwise comparisons. However, LLMs may exhibit lower discriminative power, as seen in flatter ranking slopes and missed significance for meaningful improvements. Yet, there are no cases where capable LLMs and human judges reach opposing conclusions with significance. LLMs may boost traditional systems more than neural ones, adding a new concern of system bias. These findings highlight the strong potential of LLMs for relevance labelling, while also highlighting failure cases that call for careful adoption and further research to maintain evaluation integrity.1 1

This meet-up will bring together members of the ACM SIGIR Information Interaction and Retrieval (CHIIR) community and the broader CHI audience to strengthen cross-community dialogue on interactive information retrieval (IIR), search interfaces, and human-centered evaluation methods. Through structured activities, networking opportunities, and collaborative brainstorming, the session aims to identify common challenges, inspire collaborations, and chart a forward-looking research agenda that connects CHI and CHIIR communities.

IoT devices are becoming more embedded within our lives, but the competitive nature of the industry requires quick time to market with security being an afterthought. One way to keep IoT devices secure is to regularly patch them of known vulnerabilities. This relies on knowing what firmware versions IoT devices are running, and when they are updating. In this paper we complete an update analysis for 50 IoT devices within a lab that simulates a smart home. We analyse the passive network traffic of these devices, documenting the prevalence of automatic updates, frequency of updates, and whether encrypted channels are used to send updates. We find that a significant proportion of the devices analysed can only be updated manually; and for those devices that have automatic updates, many typically require user confirmation and can be disabled. We also see that many devices are not updated over periods of several months, and often update insecurely. Based on this analysis, we discuss how the automatic identification of IoT devices-including version identification-can be improved. We also consider what our analysis means for the problem of update detection and provide a step-by-step outline for detecting when a device is updating based on its traffic.

Prior research on memory mechanism in RAG-based conversational system has emphasized how memory is stored and retrieved. However, far less is known about how memories with different functional roles influence response quality. Specifically, how they shape an agent's responses under varying conversational contexts and whether they lead to substantively different response behaviors. Existing evaluations in conversational system are also largely reference-based, insufficiently capturing the nuances in responses that may address users' preferences differently. In this work, we probe the impact of different memory types in shaping agents' responses. We present a fine-grained taxonomy of conversational memory, classify retrieved memories into different role types, and design a user-centric evaluation framework that simulates user perspectives. Through comparative experiments on long-term datasets and frontier LLMs, our analysis reveal many differentiated effects of memories: e.g., clarifying memory improves responses' factual accuracy and constraint awareness, making them more correct and personalized; irrelevant memory reduces topic relevance and degrades constraint awareness. Despite the power of frontier LLMs, these findings shed light on how different memory types can be leveraged to produce more personalized responses and inspire further research in this direction.

The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference 2024 in the USA (SIGIR 2024). The aim was to bring information retrieval researchers together around the topic of LLMs for evaluation in information retrieval that gathered attention with the advancement of large language models and generative AI. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers. Date: 18 July 2024. Website: https://llm4eval.github.io.

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.

This study proposes a method to diversify queries in existing test collections to reflect some of the diversity of search engine users, aligning with an earlier vision of an 'ideal' test collection. A Large Language Model (LLM) is used to create query variants: alternative queries that have the same meaning as the original. These variants represent user profiles characterised by different properties, such as language and domain proficiency, which are known in the Information Retrieval (IR) literature to influence query formulation. The LLM's ability to generate query variants that align with user profiles is empirically validated, and the variants' utility is further explored for IR system evaluation. Results demonstrate that the variants impact how systems are ranked and show that user profiles experience significantly different levels of system effectiveness. This method enables an alternative perspective on system evaluation where we can observe both the impact of user profiles on system rankings and how system performance varies across users.

Large-scale test collections play a crucial role in Information Retrieval (IR) research. However, according to the Cranfield paradigm and the research into publicly available datasets, the existing information retrieval research studies are commonly developed on small-scale datasets that rely on human assessors for relevance judgments - a time-intensive and expensive process. Recent studies have shown the strong capability of Large Language Models (LLMs) in producing reliable relevance judgments with human accuracy but at a greatly reduced cost. In this paper, to address the missing large-scale ad-hoc document retrieval dataset, we extend the TREC Deep Learning Track (DL) test collection via additional language model synthetic labels to enable researchers to test and evaluate their search systems at a large scale. Specifically, such a test collection includes more than 1,900 test queries from the previous years of tracks. We compare system evaluation with past human labels from past years and find that our synthetically created large-scale test collection can lead to highly correlated system rankings.

Large language models (LLMs) have demonstrated increasing tasksolving 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.

The field of Information Retrieval (IR) changed profoundly at the end of the 1990s with the rise of Web Search, and there are parallels with developments in Artificial Intelligence (AI) happening today with the advent of ChatGPT, Large Language Models, and Generative AI. We acknowledge that there are clear differences between IR and AI. For example, IR is a much smaller field, and new problems arise, like data contamination that may affect benchmark-based evaluation of AI systems. But looking through the lens of an IR researcher, there are many striking similarities between the two fields of IR (25 years ago) and AI (today), and many topics appearing in discussions in AI resemble those of 25 years ago in IR: benchmark reliability and robust evaluation, reproducibility of results for non-public models, privacy and copyright issues, efficiency and scalability, etc. In this paper, we discuss similarities and differences between IR and AI and then derive some lessons learned in the field of IR as a list of recommendations - urging the IR community to reflect on, discuss, and convey these lessons to the AI field. We believe that a joint community effort by all IR researchers is both necessary and dutiful to obtain a fruitful discussion and research advancements with the AI community.

Using Large Language Models (LLMs) for relevance assessments offers promising opportunities to improve Information Retrieval (IR), Natural Language Processing (NLP), and related fields. Indeed, LLMs hold the promise of allowing IR experimenters to build evaluation collections with a fraction of the manual human labor currently required. This could help with fresh topics on which there is still limited knowledge and could mitigate the challenges of evaluating ranking systems in low-resource scenarios, where it is challenging to find human annotators. Given the fast-paced recent developments in the domain, many questions concerning LLMs as assessors are yet to be answered. Among the aspects that require further investigation, we can list the impact of various components in a relevance judgment generation pipeline, such as the prompt used or the LLM chosen. This paper benchmarks and reports on the results of a large-scale automatic relevance judgment evaluation, the LLMJudge challenge at SIGIR 2024, where different relevance assessment approaches were proposed. In detail, we release and benchmark 42 LLM-generated labels of the TREC 2023 Deep Learning track relevance judgments produced by eight international teams who participated in the challenge. Given their diverse nature, these automatically generated relevance judgments can help the community not only investigate systematic biases caused by LLMs but also explore the effectiveness of ensemble models, analyze the trade-offs between different models and human assessors, and advance methodologies for improving automated evaluation techniques. The released resource is available at the following link: https://llm4eval.github.io/LLMJudge-benchmark/

As the Internet of Things (IoT) continues to grow, networks are increasingly at risk from vulnerable devices that allow access to attackers. Two particular threats are posed by rogue devices (i.e. devices present on a network that should not be) and unpatched devices (devices with out-of-date software or firmware). A growing body of research attempts to address these risks: automated IoT device identification. By using methods to quickly and easily identify IoT devices on a network, vulnerable devices can be identified, improving network security. Although there have been publications that survey this research, they are typically broad, discussing IoT device identification only in passing, and do not provide a methodology to clearly compare existing (or future) research. Our novel approach in this paper is to provide a simple methodology for assessing and comparing research into IoT device identification, bypassing the need to delve into granular details such as specific algorithmic choices or feature selections, which are attributes not all papers have, and instead to focus on common attributes shared across papers. We provide a comprehensive literature review for the topic of identifying IoT devices in networks using passive network traffic, resulting in 69 publications examined. We systematically analyse the literature for key elements common across the studies that can allow a comparative analysis, and define five we determine to be most important. We state why these five elements in particular are important, and discuss trends in these elements across the studies. We then produce a summary table containing just the information for the five elements for each study, and how they can be used to understand and compare techniques, considering their context. This gives security professionals and researchers the necessary tools to compare studies, both current and future, to understand how to secure their networks and what they must consider when completing further research.

As the Internet of Things (IoT) becomes more embedded within our daily lives, there is growing concern about the risk `smart' devices pose to network security. To address this, one avenue of research has focused on automated IoT device identification. Research has however largely neglected the identification of IoT device firmware versions. There is strong evidence that IoT security relies on devices being on the latest version patched for known vulnerabilities. Identifying when a device has updated (has changed version) or not (is on a stable version) is therefore useful for IoT security. Version identification involves challenges beyond those for identifying the model, type, and manufacturer of IoT devices, and traditional machine learning algorithms are ill-suited for effective version identification due to being limited by the availability of data for training. In this paper, we introduce an effective technique for identifying IoT device versions based on transfer learning. This technique relies on the idea that we can use a Twin Neural Network (TNN) - trained at distinguishing devices - to detect differences between a device on different versions. This facilitates real-world implementation by requiring relatively little training data. We extract statistical features from on-wire packet flows, convert these features into greyscale images, pass these images into a TNN, and determine version changes based on the Hedges' g effect size of the similarity scores. This allows us to detect the subtle changes present in on-wire traffic when a device changes version. To evaluate our technique, we set up a lab containing 12 IoT devices and recorded their on-wire packet captures for 11 days across multiple firmware versions. For testing data held out from training, our best performing model is shown to be 95.83

The purpose of the Strategic Workshop on Information Retrieval in Lorne (SWIRL)1 is to explore the long-range issues of the information retrieval (IR) field, to recognise challenges that are on – or even over – the horizon, to build consensus on key challenges, and to disseminate the resulting information to the research community. The intent is that this description of open problems will help to inspire researchers and graduate students to address the questions and will provide funding agencies with data to focus and coordinate support for IR research. Date: 10–12 February 2025. Website: https://sites.google.com/view/swirl2025/home.

Existing methods for evaluating the factuality of large language model (LLM) responses treat all claims as equally important. This results in misleading evaluations when vital information is missing or incorrect as it receives the same weight as peripheral details, raising the question: how can we reliably detect such differences when there are errors in key information? Current approaches that measure factuality tend to be insensitive to omitted or false key information. To investigate this lack of sensitivity, we construct VITALERRORS, a benchmark of 6,733 queries with minimally altered LLM responses designed to omit or falsify key information. Using this dataset, we demonstrate the insensitivities of existing evaluation metrics to key information errors. To address this gap, we introduce VITAL, a set of metrics that provide greater sensitivity in measuring the factuality of responses by incorporating the relevance and importance of claims with respect to the query. Our analysis demonstrates that VITAL metrics more reliably detect errors in key information than previous methods. Our dataset, metrics, and analysis provide a foundation for more accurate and robust assessment of LLM factuality.

Recent work has shown that Large Language Models (LLMs) can produce relevance judgements for English retrieval that are useful as a basis for system comparison, and they do so at vastly reduced cost compared to human assessors. Using relevance judgements and ranked retrieval runs from the TREC NeuCLIR track, this paper shows that LLMs can also produce reliable assessments in other languages, even when the topic description or the prompt are in a language different from the documents. Results with Chinese, Persian and Russian documents show that although document language affects both agreement with human assessors on graded relevance and on preference ordering among systems, prompt-language and topic-language effects are negligible. This has implications for the design of multilingual test collections, suggesting that prompts and topic descriptions can be developed in any convenient language.

Information retrieval (IR) has a large literature on evaluation, dating back decades and forming a central part of the research culture. The largest proportion of this literature discusses techniques to turn a sequence of relevance labels into a single number, reflecting the system's performance: precision or cumulative gain, for example, or dozens of alternatives. Those techniques-metrics-are themselves evaluated, commonly by reference to sensitivity and validity. In our experience measuring search in industrial settings, a measurement regime needs many other qualities to be practical. For example, we must also consider how much a metric costs; how robust it is to the happenstance of sampling; whether it is debuggable; and what activities are incentivised when a metric is taken as a goal. In this perspective paper we discuss what makes a search metric successful in large-scale settings, including factors which are not often canvassed in IR research but which are important in "real-world" use. We illustrate this with examples, including from industrial settings, and offer suggestions for metrics as part of a working system.

Conversational Agents are increasingly integrated into our daily routines, assisting us with various tasks, from simple commands such as scheduling events to more complex conversational search interactions. Such conversational search systems are traditionally evaluated with word-overlap metrics such as F1 score and accuracy. The full-day workshop on Search-Oriented Conversational Artificial Intelligence (SCAI) at CHIIR 2024 explored the evaluation of conversational search systems from the user's perspective. This interactive workshop included multiple panel discussions and working groups focused on developing and discussing innovative, user-centered evaluation methods for these systems. This paper, co-authored by both organizers and participants of the workshop, presents a summary of the insights gathered from the panel discussions and working groups. Date : 14 March 2024. Website : https://scai.info/scai-2024/.