Tootfinder

This https://arxiv.org/abs/2403.06769 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents, which are expected to engage in strategic conversations with diverse users, for securing a mutual agreement that leans favorably towards the system's objectives. This poses two main challenges for existing dialogue agents: 1) The inability to integrate user-specific characteristics into the strategic planning, and 2) The difficulty of training strategic planners that can be generalized to diverse users. To address these challenges, we propose Tr…

This https://arxiv.org/abs/2403.02525 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

An Analysis of Intent-Based Markets
Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents called \emph{solvers} compete to satisfy user orders, which may include complicated user-specified conditions. We provide two formal models…

This https://arxiv.org/abs/2403.02525 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

An Analysis of Intent-Based Markets
Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents called \emph{solvers} compete to satisfy user orders, which may include complicated user-specified conditions. We provide two formal models…

This https://arxiv.org/abs/2312.10170 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

UINav: A Practical Approach to Train On-Device Automation Agents
Automation systems that can autonomously drive application user interfaces to complete user tasks are of great benefit, especially when users are situationally or permanently impaired. Prior automation systems do not produce generalizable models while AI-based automation agents work reliably only in simple, hand-crafted applications or incur high computation costs. We propose UINav, a demonstration-based approach to train automation agents that fit mobile devices, yet achieving high success rat…

Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. de Vries, Jeff Dalton, Faegheh Hasibi
https://arxiv.org/abs/2405.03480

Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in gener…

Related to that last boost (https://mastodon.social/@SteveFaulkner/112212980289113651), I have already encountered orgs who have scaled back their accessibility efforts or plans because they feel automation that can do it *all* for them is right around the corner.…

KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
Fangyuan Xu, Kyle Lo, Luca Soldaini, Bailey Kuehl, Eunsol Choi, David Wadden
https://arxiv.org/abs/2403.03866

KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
Large language models (LLMs) adapted to follow user instructions are now widely deployed as conversational agents. In this work, we examine one increasingly common instruction-following task: providing writing assistance to compose a long-form answer. To evaluate the capabilities of current LLMs on this task, we construct KIWI, a dataset of knowledge-intensive writing instructions in the scientific domain. Given a research question, an initial model-generated answer and a set of relevant papers…

This https://arxiv.org/abs/2403.14680 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Trust in AI: Progress, Challenges, and Future Directions
The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. T…

This https://arxiv.org/abs/2312.10170 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

UINav: A Practical Approach to Train On-Device Automation Agents
Automation systems that can autonomously drive application user interfaces to complete user tasks are of great benefit, especially when users are situationally or permanently impaired. Prior automation systems do not produce generalizable models while AI-based automation agents work reliably only in simple, hand-crafted applications or incur high computation costs. We propose UINav, a demonstration-based approach to train automation agents that fit mobile devices, yet achieving high success rat…

TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
Mohammad Aliannejadi, Zahra Abbasiantaeb, Shubham Chatterjee, Jeffery Dalton, Leif Azzopardi
https://arxiv.org/abs/2405.02637

TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
Conversational information seeking has evolved rapidly in the last few years with the development of Large Language Models (LLMs), providing the basis for interpreting and responding in a naturalistic manner to user requests. The extended TREC Interactive Knowledge Assistance Track (iKAT) collection aims to enable researchers to test and evaluate their Conversational Search Agents (CSA). The collection contains a set of 36 personalized dialogues over 20 different topics each coupled with a Pers…

An Analysis of Intent-Based Markets
Tarun Chitra, Kshitij Kulkarni, Mallesh Pai, Theo Diamandis
https://arxiv.org/abs/2403.02525 https://

An Analysis of Intent-Based Markets
Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents called \emph{solvers} compete to satisfy user orders, which may include complicated user-specified conditions. We provide two formal models…

Evolving Agents: Interactive Simulation of Dynamic and Diverse Human Personalities
Jiale Li, Jiayang Li, Jiahao Chen, Yifan Li, Shijie Wang, Hugo Zhou, Minjun Ye, Yunsheng Su
https://arxiv.org/abs/2404.02718

Evolving Agents: Interactive Simulation of Dynamic and Diverse Human Personalities
Human-like Agents with diverse and dynamic personality could serve as an important design probe in the process of user-centered design, thereby enabling designers to enhance the user experience of interactive application.In this article, we introduce Evolving Agents, a novel agent architecture that consists of two systems: Personality and Behavior. The Personality system includes three modules: Cognition, Emotion and Character Growth. The Behavior system comprises two modules: Planning and Acti…

This https://arxiv.org/abs/2404.12486 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Follow-Me AI: Energy-Efficient User Interaction with Smart Environments
This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments. Through AI agents that accompany users, Follow-Me AI negotiates data management based on user consent, aligns environmental controls as well as user communication and computes resources available in the environment with user preferences, and predicts user behavior to proactively adjust the smart e…

Evolving Agents: Interactive Simulation of Dynamic and Diverse Human Personalities
Jiale Li, Jiayang Li, Jiahao Chen, Yifan Li, Shijie Wang, Hugo Zhou, Minjun Ye, Yunsheng Su
https://arxiv.org/abs/2404.02718

Evolving Agents: Interactive Simulation of Dynamic and Diverse Human Personalities
Human-like Agents with diverse and dynamic personality could serve as an important design probe in the process of user-centered design, thereby enabling designers to enhance the user experience of interactive application.In this article, we introduce Evolving Agents, a novel agent architecture that consists of two systems: Personality and Behavior. The Personality system includes three modules: Cognition, Emotion and Character Growth. The Behavior system comprises two modules: Planning and Acti…

BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Ruiyang Ren, Peng Qiu, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Hua Wu, Ji-Rong Wen, Haifeng Wang
https://arxiv.org/abs/2402.17505

BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e.g., privacy issues) of real user data, in this paper, we conduct large-scale user simulation for web search, to improve the analysis and modeling of user search behavior. Specially, we propose BASES, a novel user simulation framework with LLM-based agents, designed to facilitate comprehensive simulations of web searc…

This https://arxiv.org/abs/2306.11980 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

LLM-based Smart Reply (LSR): Enhancing Collaborative Performance with ChatGPT-mediated Smart Reply System
Interactive user interfaces have increasingly explored AI's role in enhancing communication efficiency and productivity in collaborative tasks. The emergence of Large Language Models (LLMs) such as ChatGPT has revolutionized conversational agents, employing advanced deep learning techniques to generate context-aware, coherent, and personalized responses. Consequently, LLM-based AI assistants provide a more natural and efficient user experience across various scenarios. In this paper, we study h…

This https://arxiv.org/abs/2310.07968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation
Zero-Shot Object Navigation (ZSON) enables agents to navigate towards open-vocabulary objects in unknown environments. The existing works of ZSON mainly focus on following individual instructions to find generic object classes, neglecting the utilization of natural language interaction and the complexities of identifying user-specific objects. To address these limitations, we introduce Zero-shot Interactive Personalized Object Navigation (ZIPON), where robots need to navigate to personalized go…

This https://arxiv.org/abs/2307.14551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

How to Train Your YouTube Recommender to Avoid Unwanted Videos
YouTube provides features for users to indicate disinterest when presented with unwanted recommendations, such as the "Not interested" and "Don't recommend channel" buttons. These buttons purportedly allow the user to correct "mistakes" made by the recommendation system. Yet, relatively little is known about the empirical efficacy of these buttons. Neither is much known about users' awareness of and confidence in them. To address these gaps, we simulated YouTube users with sock puppet agents. E…

This https://arxiv.org/abs/2312.10170 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

UINav: A Practical Approach to Train On-Device Automation Agents
Automation systems that can autonomously drive application user interfaces to complete user tasks are of great benefit, especially when users are situationally or permanently impaired. Prior automation systems do not produce generalizable models while AI-based automation agents work reliably only in simple, hand-crafted applications or incur high computation costs. We propose UINav, a demonstration-based approach to train automation agents that fit mobile devices, yet achieving high success rat…

This https://arxiv.org/abs/2403.14963 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Enabling Physical Localization of Uncooperative Cellular Devices
In cellular networks, it can become necessary for authorities to physically locate user devices for tracking criminals or illegal devices. While cellular operators can provide authorities with cell information the device is camping on, fine-grained localization is still required. Therefore, the authorized agents trace the device by monitoring its uplink signals. However, tracking the uplink signal source without its cooperation is challenging even for operators and authorities. Particularly, th…

Effort and Size Estimation in Software Projects with Large Language Model-based Intelligent Interfaces
Claudionor N. Coelho Jr, Hanchen Xiong, Tushar Karayil, Sree Koratala, Rex Shang, Jacob Bollinger, Mohamed Shabar, Syam Nair
https://arxiv.org/abs/2402.07158

Effort and Size Estimation in Software Projects with Large Language Model-based Intelligent Interfaces
The advancement of Large Language Models (LLM) has also resulted in an equivalent proliferation in its applications. Software design, being one, has gained tremendous benefits in using LLMs as an interface component that extends fixed user stories. However, inclusion of LLM-based AI agents in software design often poses unexpected challenges, especially in the estimation of development efforts. Through the example of UI-based user stories, we provide a comparison against traditional methods and…

This https://arxiv.org/abs/2404.16244 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The Ethics of Advanced AI Assistants
This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of application…

Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations
Carlos Jose Xavier Cruz
https://arxiv.org/abs/2403.07769

Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations
This article explores the dynamic influence of computational entities based on multi-agent systems theory (SMA) combined with large language models (LLM), which are characterized by their ability to simulate complex human interactions, as a possibility to revolutionize human user interaction from the use of specialized artificial agents to support everything from operational organizational processes to strategic decision making based on applied knowledge and human orchestration. Previous invest…

This https://arxiv.org/abs/2309.11653 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted…

This https://arxiv.org/abs/2402.18498 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration
Although recent developments in generative AI have greatly enhanced the capabilities of conversational agents such as Google's Gemini (formerly Bard) or OpenAI's ChatGPT, it's unclear whether the usage of these agents aids users across various contexts. To better understand how access to conversational AI affects productivity and trust, we conducted a mixed-methods, task-based user study, observing 76 software engineers (N=76) as they completed a programming exam with and without access to Bard…

Enabling Physical Localization of Uncooperative Cellular Devices
Taekkyung Oh, Sangwook Bae, Junho Ahn, Yonghwa Lee, Dinh-Tuan Hoang, Min Suk Kang, Nils Ole Tippenhauer, Yongdae Kim
https://arxiv.org/abs/2403.14963

Enabling Physical Localization of Uncooperative Cellular Devices
In cellular networks, it can become necessary for authorities to physically locate user devices for tracking criminals or illegal devices. While cellular operators can provide authorities with cell information the device is camping on, fine-grained localization is still required. Therefore, the authorized agents trace the device by monitoring its uplink signals. However, tracking the uplink signal source without its cooperation is challenging even for operators and authorities. Particularly, th…

Multi-Agent Collaboration Framework for Recommender Systems
Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, Min Zhang
https://arxiv.org/abs/2402.15235 ht…

Multi-Agent Collaboration Framework for Recommender Systems
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unlike existing work on using agents for user/item simulation, we aim to deploy multi-agents to tackle recommendation tasks directly. In our f…

This https://arxiv.org/abs/2402.09015 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks. However, a significant gap remains in assessing whether LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the pressing need for methods to verify utility of LLM-powered applications, particularly by ensuring alignment between the application's functi…

This https://arxiv.org/abs/2403.14680 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Trust in AI: Progress, Challenges, and Future Directions
The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. T…

This https://arxiv.org/abs/2402.12814 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Exploring the Interaction of Creative Writers with AI-Powered Writing Tools
AI-based virtual assistants are increasingly used to support daily ideation tasks. The values or bias present in these agents can influence output in hidden ways. They may also affect how people perceive the ideas produced with these AI agents and lead to implications for the design of AI-based tools. We explored the effects of AI agents with different values on the ideation process and user perception of idea quality, ownership, agent competence, and values present in the output. Our study tas…

This https://arxiv.org/abs/2310.07968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation
Zero-Shot Object Navigation (ZSON) enables agents to navigate towards open-vocabulary objects in unknown environments. The existing works of ZSON mainly focus on following individual instructions to find generic object classes, neglecting the utilization of natural language interaction and the complexities of identifying user-specific objects. To address these limitations, we introduce Zero-shot Interactive Personalized Object Navigation (ZIPON), where robots need to navigate to personalized go…

Multi-Agent Collaboration Framework for Recommender Systems
Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, Min Zhang
https://arxiv.org/abs/2402.15235 ht…

Multi-Agent Collaboration Framework for Recommender Systems
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabilities for multi-agent collaboration in recommendation systems, we introduce MACRec, a novel framework designed to enhance recommendation systems through multi-agent collaboration. Unlike existing work on using agents for user/item simulation, we aim to deploy multi-agents to tackle recommendation tasks directly. In our f…

This https://arxiv.org/abs/2401.16167 has been replaced.
link: https://scholar.google.com/scholar?q=a

"You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations
Conversational agents are increasingly used to address emotional needs on top of information needs. One use case of increasing interest are counselling-style mental health and behaviour change interventions, with large language model (LLM)-based approaches becoming more popular. Research in this context so far has been largely system-focused, foregoing the aspect of user behaviour and the impact this can have on LLM-generated texts. To address this issue, we share a dataset containing text-base…

The Ethics of Advanced AI Assistants
Iason Gabriel, Arianna Manzini, Geoff Keeling, Lisa Anne Hendricks, Verena Rieser, Hasan Iqbal, Nenad Toma\v{s}ev, Ira Ktena, Zachary Kenton, Mikel Rodriguez, Seliem El-Sayed, Sasha Brown, Canfer Akbulut, Andrew Trask, Edward Hughes, A. Stevie Bergman, Renee Shelby, Nahema Marchal, Conor Griffin, Juan Mateos-Garcia, Laura Weidinger, Winnie Street, Benjamin Lange, Alex Ingerman, Alison Lentz, Reed Enger, Andrew Barakat, Victoria Krakovna, John Oliver…

The Ethics of Advanced AI Assistants
This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of application…

This https://arxiv.org/abs/2401.16167 has been replaced.
link: https://scholar.google.com/scholar?q=a

"You tell me": A Dataset of GPT-4-Based Behaviour Change Support Conversations
Conversational agents are increasingly used to address emotional needs on top of information needs. One use case of increasing interest are counselling-style mental health and behaviour change interventions, with large language model (LLM)-based approaches becoming more popular. Research in this context so far has been largely system-focused, foregoing the aspect of user behaviour and the impact this can have on LLM-generated texts. To address this issue, we share a dataset containing text-base…

Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations
Carlos Jose Xavier Cruz
https://arxiv.org/abs/2403.07769

Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations
This article explores the dynamic influence of computational entities based on multi-agent systems theory (SMA) combined with large language models (LLM), which are characterized by their ability to simulate complex human interactions, as a possibility to revolutionize human user interaction from the use of specialized artificial agents to support everything from operational organizational processes to strategic decision making based on applied knowledge and human orchestration. Previous invest…

The Ethics of Advanced AI Assistants
Iason Gabriel, Arianna Manzini, Geoff Keeling, Lisa Anne Hendricks, Verena Rieser, Hasan Iqbal, Nenad Toma\v{s}ev, Ira Ktena, Zachary Kenton, Mikel Rodriguez, Seliem El-Sayed, Sasha Brown, Canfer Akbulut, Andrew Trask, Edward Hughes, A. Stevie Bergman, Renee Shelby, Nahema Marchal, Conor Griffin, Juan Mateos-Garcia, Laura Weidinger, Winnie Street, Benjamin Lange, Alex Ingerman, Alison Lentz, Reed Enger, Andrew Barakat, Victoria Krakovna, John Oliver…

The Ethics of Advanced AI Assistants
This paper focuses on the opportunities and the ethical and societal risks posed by advanced AI assistants. We define advanced AI assistants as artificial agents with natural language interfaces, whose function is to plan and execute sequences of actions on behalf of a user, across one or more domains, in line with the user's expectations. The paper starts by considering the technology itself, providing an overview of AI assistants, their technical foundations and potential range of application…

Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
Tong Zhang, Chen Huang, Yang Deng, Hongru Liang, Jia Liu, Zujie Wen, Wenqiang Lei, Tat-Seng Chua
https://arxiv.org/abs/2403.06769

Strength Lies in Differences! Towards Effective Non-collaborative Dialogues via Tailored Strategy Planning
We investigate non-collaborative dialogue agents that must engage in tailored strategic planning for diverse users to secure a favorable agreement. This poses challenges for existing dialogue agents due to two main reasons: their inability to integrate user-specific characteristics into their strategic planning and their training paradigm's failure to produce strategic planners that can generalize to diverse users. To address these challenges, we propose TRIP to enhance the capability in tailor…

This https://arxiv.org/abs/2401.10935 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To alleviate this issue, we propose a novel visual GUI agent -- SeeClick, which only relies on screenshots for task automation. In our preliminary study, we have discovered a key challe…

Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration
Crystal Qian, James Wexler
https://arxiv.org/abs/2402.18498 https:…

Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration
Although recent developments in generative AI have greatly enhanced the capabilities of conversational agents such as Google's Bard or OpenAI's ChatGPT, it's unclear whether the usage of these agents aids users across various contexts. To better understand how access to conversational AI affects productivity and trust, we conducted a mixed-methods, task-based user study, observing 76 software engineers (N=76) as they completed a programming exam with and without access to Bard. Effects on perfo…

Trust in AI: Progress, Challenges, and Future Directions
Saleh Afroogh, Ali Akbari, Evan Malone, Mohammadali Kargar, Hananeh Alambeigi
https://arxiv.org/abs/2403.14680

Trust in AI: Progress, Challenges, and Future Directions
The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. T…

Loyal Wingman Assessment: Social Navigation for Human-Autonomous Collaboration in Simulated Air Combat
Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama
https://arxiv.org/abs/2405.00073

Loyal Wingman Assessment: Social Navigation for Human-Autonomous Collaboration in Simulated Air Combat
This study proposes social navigation metrics for autonomous agents in air combat, aiming to facilitate their smooth integration into pilot formations. The absence of such metrics poses challenges to safety and effectiveness in mixed human-autonomous teams. The proposed metrics prioritize naturalness and comfort. We suggest validating them through a user study involving military pilots in simulated air combat scenarios alongside autonomous loyal wingmen. The experiment will involve setting up s…

This https://arxiv.org/abs/2402.09015 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks. However, a significant gap remains in assessing whether LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the pressing need for methods to verify utility of LLM-powered applications, particularly by ensuring alignment between the application's functi…

A Conceptual Framework for Conversational Search and Recommendation: Conceptualizing Agent-Human Interactions During the Conversational Search Process
Leif Azzopardi, Mateusz Dubiel, Martin Halvey, Jeffery Dalton
https://arxiv.org/abs/2404.08630

A Conceptual Framework for Conversational Search and Recommendation: Conceptualizing Agent-Human Interactions During the Conversational Search Process
The conversational search task aims to enable a user to resolve information needs via natural language dialogue with an agent. In this paper, we aim to develop a conceptual framework of the actions and intents of users and agents explaining how these actions enable the user to explore the search space and resolve their information need. We outline the different actions and intents, before discussing key decision points in the conversation where the agent needs to decide how to steer the convers…

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Juhye Ha, Hyeon Jeon, DaEun Han, Jinwook Seo, Changhoon Oh
https://arxiv.org/abs/2402.15265

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with…

Exploring the Interaction of Creative Writers with AI-Powered Writing Tools
Alicia Guo, Pat Pataranutaporn, Pattie Maes
https://arxiv.org/abs/2402.12814 ht…

Exploring the Interaction of Creative Writers with AI-Powered Writing Tools
AI-based virtual assistants are increasingly used to support daily ideation tasks. The values or bias present in these agents can influence output in hidden ways. They may also affect how people perceive the ideas produced with these AI agents and lead to implications for the design of AI-based tools. We explored the effects of AI agents with different values on the ideation process and user perception of idea quality, ownership, agent competence, and values present in the output. Our study tas…

Influence of Personality and Communication Behavior of a Conversational Agent on User Experience and Social Presence in Augmented Reality
Katerina Koleva, Maurizio Vergari, Tanja Koji\'c, Sebastian M\"oller, Jan-Niklas Voigt-Antons
https://arxiv.org/abs/2403.09883

Influence of Personality and Communication Behavior of a Conversational Agent on User Experience and Social Presence in Augmented Reality
A virtual embodiment can benefit conversational agents, but it is unclear how their personalities and non-verbal behavior influence the User Experience and Social Presence in Augmented Reality (AR). We asked 30 users to converse with a virtual assistant who gives recommendations about city activities. The participants interacted with two different personalities: Sammy, a cheerful blue mouse, and Olive, a serious green human-like agent. Each was presented with two body languages - happy/friendly…

Effect of external characteristics of a virtual human being during the use of a computer-assisted therapy tool
Navid Ashrafi, Vanessa Neuhaus, Francesco Vona, Nicolina Laura Peperkorn, Youssef Shiban, Jan-Niklas Voigt-Antons
https://arxiv.org/abs/2403.09544

Effect of external characteristics of a virtual human being during the use of a computer-assisted therapy tool
Identification within media, whether with real or fictional characters, significantly impacts users, shaping their behavior and enriching their social and emotional experiences. Immersive media, like video games, utilize virtual entities such as agents, avatars, or NPCs to connect users with virtual worlds, fostering a heightened sense of immersion and identification. However, challenges arise in visually representing these entities, with design decisions crucial for enhancing user interaction.…