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1. Information Retrieval and Interaction System (IRIS): A Toolkit for Investigating Information Retrieval and Interaction Activities

   https://doi.org/10.1145/3176349.3176895  

   Pulliza, Jonathan; Shah, Chirag (January 2018, ACM Conference on Human Information Interaction and Retrieval (CHIIR))

   In this demo we present IRIS, an open-source framework that provides a set of simple and modular document operators that can be combined in various ways to create more interesting and advanced functionality otherwise unavailable during most information search sessions. Those functionalities include summarization, ranking, filtering and query. The goal is to support users looking for, collecting, and synthesizing information. The system is also easily extendable, allowing for customized functionality for users during information sessions and researchers studying higher levels of abstraction for information retrieval. The demo shows the front end interactions using a browser plug-in that offers new interactions with documents during search sessions, as well as the back-end components driving the system. 

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2. IDEAL: An Open-Source Identifier Name Appraisal Tool

   https://doi.org/10.1109/ICSME52107.2021.00064  

   Peruma, Anthony; Arnaoudova, Venera; Newman, Christian D. (September 2021, IEEE International Conference on Software Maintenance and Evolution (ICSME))

   Developers must comprehend the code they will maintain, meaning that the code must be legible and reasonably self-descriptive. Unfortunately, there is still a lack of research and tooling that supports developers in understanding their naming practices; whether the names they choose make sense, whether they are consistent, and whether they convey the information required of them. In this paper, we present IDEAL, a tool that will provide feedback to developers about their identifier naming practices. Among its planned features, it will support linguistic anti-pattern detection, which is what will be discussed in this paper. IDEAL is designed to, and will, be extended to cover further anti-patterns, naming structures, and practices in the near future. IDEAL is open-source and publicly available, with a demo video available at: https://youtu.be/fVoOYGe50zg 

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3. InSCIt : Information-Seeking Conversations with Mixed-Initiative Interactions

   https://doi.org/10.1162/tacl_a_00559  

   Wu, Zeqiu; Parish, Ryu; Cheng, Hao; Min, Sewon; Ammanabrolu, Prithviraj; Ostendorf, Mari; Hajishirzi, Hannaneh (May 2023, Transactions of the Association for Computational Linguistics)

   Abstract In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarification, or provides relevant information to address user queries. The data supports two subtasks, evidence passage identification and response generation, as well as a human evaluation protocol to assess model performance. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both systems significantly underperform humans, suggesting ample room for improvement in future studies.1 

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4. Reduced risk-seeking in chimpanzees in a zero-outcome game

   https://doi.org/10.1098/rstb.2019.0673  

   Keupp, Stefanie; Grueneisen, Sebastian; Ludvig, Elliot A.; Warneken, Felix; Melis, Alicia P. (March 2021, Philosophical Transactions of the Royal Society B: Biological Sciences)

   A key component of economic decisions is the integration of information about reward outcomes and probabilities in selecting between competing options. In many species, risky choice is influenced by the magnitude of available outcomes, probability of success and the possibility of extreme outcomes. Chimpanzees are generally regarded to be risk-seeking. In this study, we examined two aspects of chimpanzees' risk preferences: first, whether setting the value of the non-preferred outcome of a risky option to zero changes chimpanzees’ risk preferences, and second, whether individual risk preferences are stable across two different measures. Across two experiments, we found chimpanzees (Pan troglodytes,n= 23) as a group to be risk-neutral to risk-avoidant with highly stable individual risk preferences. We discuss how the possibility of going empty-handed might reduce chimpanzees' risk-seeking relative to previous studies. This malleability in risk preferences as a function of experimental parameters and individual differences raises interesting questions about whether it is appropriate or helpful to categorize a species as a whole as risk-seeking or risk-avoidant. This article is part of the theme issue ‘Existence and prevalence of economic behaviours among non-human primates’. 

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

   https://doi.org/10.1145/3627508.3638344  

   Wang, Ben; Liu, Jiqun; Karimnazarov, Jamshed; Thompson, Nicolas (March 2024, ACM)

   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. 

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