arXiv subjects

Falk Scholer

Publications and source records attributed to Falk Scholer.

At least 19 recordsLinked to original sources

arXiv2026

RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition

This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused retrieval-augmented generation (RAG) architecture composed of lightweight components that dynamically adapt the retrieval strategy based on inferred query complexity and evidence sufficiency. The system uses smaller LLMs, enabling operation on a single consumer-grade GPU while supporting complex research tasks. It builds on the G-RAG system, winner of the ACM~SIGIR~2025 LiveRAG Challenge, and extends it with modules informed by qualitative review of outputs. R2RAG won the Best Dynamic Evaluation award in the Open Source category, demonstrating high effectiveness with careful design and efficient use of resources.

cs.IR↗

arXiv2025

Stairway to Fairness: Connecting Group and Individual Fairness

Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relationship between the two fairness types, as prior work on both types has used different evaluation measures or evaluation objectives for each fairness type, thereby not allowing for a proper comparison of the two. As a result, it is currently not known how increasing one type of fairness may affect the other. To fill this gap, we study the relationship of group and individual fairness through a comprehensive comparison of evaluation measures that can be used for both fairness types. Our experiments with 8 runs across 3 datasets show that recommendations that are highly fair for groups can be very unfair for individuals. Our finding is novel and useful for RS practitioners aiming to improve the fairness of their systems. Our code is available at: https://github.com/theresiavr/stairway-to-fairness.

cs.IR↗

arXiv2025

Demographically-Inspired Query Variants Using an LLM

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 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.

cs.IR↗

arXiv2025

SenseSeek Dataset: Multimodal Sensing to Study Information Seeking Behaviors

Information processing tasks involve complex cognitive mechanisms that are shaped by various factors, including individual goals, prior experience, and system environments. Understanding such behaviors requires a sophisticated and personalized data capture of how one interacts with modern information systems (e.g., web search engines). Passive sensors, such as wearables, capturing physiological and behavioral data, have the potential to provide solutions in this context. This paper presents a novel dataset, SenseSeek, designed to evaluate the effectiveness of consumer-grade sensors in a complex information processing scenario: searching via systems (e.g., search engines), one of the common strategies users employ for information seeking. The SenseSeek dataset comprises data collected from 20 participants, 235 trials of the stimulated search process, 940 phases of stages in the search process, including the realization of Information Need (IN), Query Formulation (QF), Query Submission by Typing (QS-T) or Speaking (QS-S), and Relevance Judgment by Reading (RJ-R) or Listening (RJ-L). The data includes Electrodermal Activities (EDA), Electroencephalogram (EEG), PUPIL, GAZE, and MOTION data, which were captured using consumer-grade sensors. It also contains 258 features extracted from the sensor data, the gaze-annotated screen recordings, and task responses. We validate the usefulness of the dataset by providing baseline analysis on the impacts of different cognitive intents and interaction modalities on the sensor data, and effectiveness of the data in discriminating the search stages. To our knowledge, SenseSeek is the first dataset that characterizes the multiple stages involved in information seeking with physiological signals collected from multiple sensors. We hope this dataset can serve as a reference for future research on information-seeking behaviors.

cs.HC↗

arXiv2025

The Effects of Demographic Instructions on LLM Personas

Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized definitions, but often neglect the subjective nature of sexist language and fail to consider individual users' perspectives. To address this gap, we adopt a perspectivist approach, retaining diverse annotations rather than enforcing gold-standard labels or their aggregations, allowing models to account for personal or group-specific views of sexism. Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism.

cs.IR↗

arXiv2025

Control Search Rankings, Control the World: What is a Good Search Engine?

This paper examines the ethical question, 'What is a good search engine?' Since search engines are gatekeepers of global online information, it is vital they do their job ethically well. While the Internet is now several decades old, the topic remains under-explored from interdisciplinary perspectives. This paper presents a novel role-based approach involving four ethical models of types of search engine behavior: Customer Servant, Librarian, Journalist, and Teacher. It explores these ethical models with reference to the research field of information retrieval, and by means of a case study involving the COVID-19 global pandemic. It also reflects on the four ethical models in terms of the history of search engine development, from earlier crude efforts in the 1990s, to the very recent prospect of Large Language Model-based conversational information seeking systems taking on the roles of established web search engines like Google. Finally, the paper outlines considerations that inform present and future regulation and accountability for search engines as they continue to evolve. The paper should interest information retrieval researchers and others interested in the ethics of search engines.

cs.IR↗

arXiv2025

Can Generative LLMs Create Query Variants for Test Collections? An Exploratory Study

This paper explores the utility of a Large Language Model (LLM) to automatically generate queries and query variants from a description of an information need. Given a set of information needs described as backstories, we explore how similar the queries generated by the LLM are to those generated by humans. We quantify the similarity using different metrics and examine how the use of each set would contribute to document pooling when building test collections. Our results show potential in using LLMs to generate query variants. While they may not fully capture the wide variety of human-generated variants, they generate similar sets of relevant documents, reaching up to 71.1% overlap at a pool depth of 100.

cs.IR↗

arXiv2025

LLMs can be Fooled into Labelling a Document as Relevant (best caf\'e near me; this paper is perfectly relevant)

LLMs are increasingly being used to assess the relevance of information objects. This work reports on experiments to study the labelling of short texts (i.e., passages) for relevance, using multiple open-source and proprietary LLMs. While the overall agreement of some LLMs with human judgements is comparable to human-to-human agreement measured in previous research, LLMs are more likely to label passages as relevant compared to human judges, indicating that LLM labels denoting non-relevance are more reliable than those indicating relevance. This observation prompts us to further examine cases where human judges and LLMs disagree, particularly when the human judge labels the passage as non-relevant and the LLM labels it as relevant. Results show a tendency for many LLMs to label passages that include the original query terms as relevant. We, therefore, conduct experiments to inject query words into random and irrelevant passages, not unlike the way we inserted the query "best caf\'e near me" into this paper. The results show that LLMs are highly influenced by the presence of query words in the passages under assessment, even if the wider passage has no relevance to the query. This tendency of LLMs to be fooled by the mere presence of query words demonstrates a weakness in our current measures of LLM labelling: relying on overall agreement misses important patterns of failures. There is a real risk of bias in LLM-generated relevance labels and, therefore, a risk of bias in rankers trained on those labels. We also investigate the effects of deliberately manipulating LLMs by instructing them to label passages as relevant, similar to the instruction "this paper is perfectly relevant" inserted above. We find that such manipulation influences the performance of some LLMs, highlighting the critical need to consider potential vulnerabilities when deploying LLMs in real-world applications.

cs.IR↗

arXiv2025

Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessment

The effectiveness of search systems is evaluated using relevance labels that indicate the usefulness of documents for specific queries and users. While obtaining these relevance labels from real users is ideal, scaling such data collection is challenging. Consequently, third-party annotators are employed, but their inconsistent accuracy demands costly auditing, training, and monitoring. We propose an LLM-based modular classification pipeline that divides the relevance assessment task into multiple stages, each utilising different prompts and models of varying sizes and capabilities. Applied to TREC Deep Learning (TREC-DL), one of our approaches showed an 18.4% Krippendorff's $\alpha$ accuracy increase over OpenAI's GPT-4o mini while maintaining a cost of about 0.2 USD per million input tokens, offering a more efficient and scalable solution for relevance assessment. This approach beats the baseline performance of GPT-4o (5 USD). With a pipeline approach, even the accuracy of the GPT-4o flagship model, measured in $\alpha$, could be improved by 9.7%.

cs.IR↗

arXiv2024

Towards Investigating Biases in Spoken Conversational Search

Voice-based systems like Amazon Alexa, Google Assistant, and Apple Siri, along with the growing popularity of OpenAI's ChatGPT and Microsoft's Copilot, serve diverse populations, including visually impaired and low-literacy communities. This reflects a shift in user expectations from traditional search to more interactive question-answering models. However, presenting information effectively in voice-only channels remains challenging due to their linear nature. This limitation can impact the presentation of complex queries involving controversial topics with multiple perspectives. Failing to present diverse viewpoints may perpetuate or introduce biases and affect user attitudes. Balancing information load and addressing biases is crucial in designing a fair and effective voice-based system. To address this, we (i) review how biases and user attitude changes have been studied in screen-based web search, (ii) address challenges in studying these changes in voice-based settings like SCS, (iii) outline research questions, and (iv) propose an experimental setup with variables, data, and instruments to explore biases in a voice-based setting like Spoken Conversational Search.

cs.HC↗

arXiv2024

Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search

Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only channels -- as is the case for Spoken Conversational Search (SCS) -- are harder to characterize, given the lack of instruments to effectively and precisely capture interactions. Furthermore, in this era of information overload, cognitive bias can significantly impact how we seek and consume information -- especially in the context of controversial topics or multiple viewpoints. This paper draws upon insights from multiple disciplines (including information seeking, psychology, cognitive science, and wearable sensors) to provoke novel conversations in the community. To this end, we discuss future opportunities and propose a framework including multimodal instruments and methods for experimental designs and settings. We demonstrate preliminary results as an example. We also outline the challenges and offer suggestions for adopting this multimodal approach, including ethical considerations, to assist future researchers and practitioners in exploring cognitive biases in SCS.

cs.HC↗

arXiv2024

Characterizing Information Seeking Processes with Multiple Physiological Signals

Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observational studies or surveys. Leveraging the advances in sensing technologies, our study aims to characterize user behaviors with physiological signals, particularly in relation to cognitive load, affective arousal, and valence. We conduct a controlled lab study with 26 participants, and collect data including Electrodermal Activities, Photoplethysmogram, Electroencephalogram, and Pupillary Responses. This study examines informational search with four stages: the realization of Information Need (IN), Query Formulation (QF), Query Submission (QS), and Relevance Judgment (RJ). We also include different interaction modalities to represent modern systems, e.g., QS by text-typing or verbalizing, and RJ with text or audio information. We analyze the physiological signals across these stages and report outcomes of pairwise non-parametric repeated-measure statistical tests. The results show that participants experience significantly higher cognitive loads at IN with a subtle increase in alertness, while QF requires higher attention. QS involves demanding cognitive loads than QF. Affective responses are more pronounced at RJ than QS or IN, suggesting greater interest and engagement as knowledge gaps are resolved. To the best of our knowledge, this is the first study that explores user behaviors in a search process employing a more nuanced quantitative analysis of physiological signals. Our findings offer valuable insights into user behavior and emotional responses in information seeking processes. We believe our proposed methodology can inform the characterization of more complex processes, such as conversational information seeking.

cs.IR↗

arXiv2024

Online and Offline Evaluation in Search Clarification

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement.

cs.IR↗

arXiv2024

Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot

Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of modern user experiences is the integration of chatbots or voice assistants. The rapid evolution of Large Language Models (LLMs) has provided a powerful tool to build conversational applications. We present Walert, a customized LLM-based conversational agent able to answer frequently asked questions about computer science degrees and programs at RMIT University. Our demo aims to showcase how conversational information-seeking researchers can effectively communicate the benefits of using best practices to stakeholders interested in developing and deploying LLM-based chatbots. These practices are well-known in our community but often overlooked by practitioners who may not have access to this knowledge. The methodology and resources used in this demo serve as a bridge to facilitate knowledge transfer from experts, address industry professionals' practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.

cs.IR↗

arXiv2023

i-Align: an interpretable knowledge graph alignment model

Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is needed to mitigate this problem. One of the strategies to address this problem is KG alignment, i.e., forming a more complete KG by merging two or more KGs. This paper proposes i-Align, an interpretable KG alignment model. Unlike the existing KG alignment models, i-Align provides an explanation for each alignment prediction while maintaining high alignment performance. Experts can use the explanation to check the correctness of the alignment prediction. Thus, the high quality of a KG can be maintained during the curation process (e.g., the merging process of two KGs). To this end, a novel Transformer-based Graph Encoder (Trans-GE) is proposed as a key component of i-Align for aggregating information from entities' neighbors (structures). Trans-GE uses Edge-gated Attention that combines the adjacency matrix and the self-attention matrix to learn a gating mechanism to control the information aggregation from the neighboring entities. It also uses historical embeddings, allowing Trans-GE to be trained over mini-batches, or smaller sub-graphs, to address the scalability issue when encoding a large KG. Another component of i-Align is a Transformer encoder for aggregating entities' attributes. This way, i-Align can generate explanations in the form of a set of the most influential attributes/neighbors based on attention weights. Extensive experiments are conducted to show the power of i-Align. The experiments include several aspects, such as the model's effectiveness for aligning KGs, the quality of the generated explanations, and its practicality for aligning large KGs. The results show the effectiveness of i-Align in these aspects.

cs.AI↗

arXiv2023

Designing and Evaluating Presentation Strategies for Fact-Checked Content

With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking have made significant advancements. However, limited guidance exists regarding the presentation of fact-checked content to effectively convey verified information to users. We address this research gap by exploring the critical design elements in fact-checking reports and investigating whether credibility and presentation-based design improvements can enhance users' ability to interpret the report accurately. We co-developed potential content presentation strategies through a workshop involving fact-checking professionals, communication experts, and researchers. The workshop examined the significance and utility of elements such as veracity indicators and explored the feasibility of incorporating interactive components for enhanced information disclosure. Building on the workshop outcomes, we conducted an online experiment involving 76 crowd workers to assess the efficacy of different design strategies. The results indicate that proposed strategies significantly improve users' ability to accurately interpret the verdict of fact-checking articles. Our findings underscore the critical role of effective presentation of fact reports in addressing the spread of misinformation. By adopting appropriate design enhancements, the effectiveness of fact-checking reports can be maximized, enabling users to make informed judgments.

cs.IR↗

arXiv2023

Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing Activities

Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the signals are highly sensitive, and many controls are required in laboratory user studies. To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). Meanwhile, we collected data on blood volume pulse, electrodermal activity, and pupil responses. We then used machine learning approaches as a mechanism to examine the influence of controlled and uncontrolled variables that commonly arise in user studies. Task duration was found to have a substantial effect on the model performance, suggesting it represents individual differences rather than giving insight into the target variables. This work contributes to our understanding of such variables in using physiological signals in information retrieval user studies.

cs.IR↗

arXiv2022

Imagining Future Digital Assistants at Work: A Study of Task Management Needs

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings, and we offer some recommendations for reduction to practice.

cs.HC↗