Report on the Workshop on Simulations for Information Access (Sim4IA 2024) at SIGIR 2024

This paper is a report of the Workshop on Simulations for Information Access (Sim4IA) workshop at SIGIR 2024. The workshop had two keynotes, a panel discussion, nine lightning talks, and two breakout sessions. Key takeaways were user simulation's importance in academia and industry, the possible bridging of online and offline evaluation, and the issues of organizing a companion shared task around user simulations for information access. We report on how we organized the workshop, provide a brief overview of what happened at the workshop, and summarize the main topics and findings of the workshop and future work.

AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment

Cognitive biases are systematic deviations in thinking that lead to irrational judgments and problematic decision-making, extensively studied across various fields. Recently, large language models (LLMs) have shown advanced understanding capabilities but may inherit human biases from their training data. While social biases in LLMs have been well-studied, cognitive biases have received less attention, with existing research focusing on specific scenarios. The broader impact of cognitive biases on LLMs in various decision-making contexts remains underexplored. We investigated whether LLMs are influenced by the threshold priming effect in relevance judgments, a core task and widely-discussed research topic in the Information Retrieval (IR) coummunity. The priming effect occurs when exposure to certain stimuli unconsciously affects subsequent behavior and decisions. Our experiment employed 10 topics from the TREC 2019 Deep Learning passage track collection, and tested AI judgments under different document relevance scores, batch lengths, and LLM models, including GPT-3.5, GPT-4, LLaMa2-13B and LLaMa2-70B. Results showed that LLMs tend to give lower scores to later documents if earlier ones have high relevance, and vice versa, regardless of the combination and model used. Our finding demonstrates that LLM%u2019s judgments, similar to human judgments, are also influenced by threshold priming biases, and suggests that researchers and system engineers should take into account potential human-like cognitive biases in designing, evaluating, and auditing LLMs in IR tasks and beyond.

Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability

This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect. It explores how decoy results alter users' interactions on search engine result pages, focusing on metrics like click-through likelihood, browsing time, and perceived document usefulness. By analyzing user interaction logs from multiple datasets, the study demonstrates that decoy results significantly affect users' behavior and perceptions. Furthermore, it investigates how different levels of task difficulty and user knowledge modify the decoy effect's impact, finding that easier tasks and lower knowledge levels lead to higher engagement with target documents. In terms of IR system evaluation, the study introduces the DEJA-VU metric to assess systems' susceptibility to the decoy effect, testing it on specific retrieval tasks. The results show differences in systems' effectiveness and vulnerability, contributing to our understanding of cognitive biases in search behavior and suggesting pathways for creating more balanced and bias-aware IR evaluations.

Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Controversial Topics

When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmically biased search engine result pages (SERPs). We designed three-query search sessions on debated topics under various bias conditions. We recruited 1,321 crowdsourcing participants and explored their attitude changes, search interactions, and the effects of confirmation bias. Three key findings emerged: 1) most attitude changes occur in the initial query of a search session; 2) confirmation bias and result presentation on SERPs affect search behaviors in the current query and perceived familiarity with clicked results in subsequent queries. The bias position also affect attitude changes of users with lower perceived openness to conflicting opinions; 3) Interactions in the first query and and dwell time throughout the session are associated with users' attitude changes in different forms. Our study goes beyond traditional simulation-based evaluation settings and simulated rational users, sheds light on the mixed effects of human biases and algorithmic biases in controversial information retrieval tasks, and can inform the design of bias-aware user models, human-centered bias mitigation techniques, and socially responsible intelligent IR systems.

Task Supportive and Personalized Human-Large Language Model Interaction: A User Study

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.

Decoy Effect in Search Interaction: A Pilot Study

In recent years, the influence of cognitive effects and biases on users' thinking, behaving, and decision-making has garnered increasing attention in the field of interactive information retrieval. The decoy effect, one of the main empirically confirmed cognitive biases, refers to the shift in preference between two choices when a third option (the decoy) which is inferior to one of the initial choices is introduced. However, it is not clear how the decoy effect influences user interactions with and evaluations on Search Engine Result Pages (SERPs). To bridge this gap, our study seeks to understand how the decoy effect at the document level influences users' interaction behaviors on SERPs, such as clicks, dwell time, and usefulness perceptions. We conducted experiments on two publicly available user behavior datasets and the findings reveal that, compared to cases where no decoy is present, the probability of a document being clicked could be improved and its usefulness score could be higher, should there be a decoy associated with the document.

Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However, the current state of conversational recommendation faces numerous challenges due to its relative novelty and limited existing contributions. In this study, we delve into benchmark datasets for developing CRS models and address potential biases arising from the feedback loop inherent in multi-turn interactions, including selection bias and multiple popularity bias variants. Drawing inspiration from the success of generative data via using language models and data augmentation techniques, we present two novel strategies, 'Once-Aug' and 'PopNudge', to enhance model performance while mitigating biases. Through extensive experiments on ReDial and TG-ReDial benchmark datasets, we show a consistent improvement of CRS techniques with our data augmentation approaches and offer additional insights on addressing multiple newly formulated biases.