pwc-archive/papers-with-abstracts huggingface dataset:
A Market for Accuracy: Classification under Competition
Authors: Ohad Einav, Nir Rosenfeld
Date: 2025-02-25 00:00:00
Tasks: Classification

Machine learning models play a key role for service providers looking to gain market share in consumer markets. However, traditional learning approaches do not take into account the existence of additional providers, who compete with each other for consumers. Our work aims to study learning in this market setting, as it affects providers, consumers, and the market itself. We begin by analyzing such markets through the lens of the learning objective, and show that accuracy cannot be the only consideration. We then propose a method for classification under competition, so that a learner can maximize market share in the presence of competitors. We show that our approach benefits the providers as well as the consumers, and find that the timing of market entry and model updates can be crucial. We display the effectiveness of our approach across a range of domains, from simple distributions to noisy datasets, and show that the market as a whole remains stable by converging quickly to an equilibrium.
A new solution for cooperative game with public externalities: Analysis based on axiomatic method
Authors: Juanjuan Fan, Ying Wang
Date: 2025-02-24 00:00:00

This paper introduces a new solution concept for the Cooperative Game with Public Externalities, called the w-value, which is characterized by three properties (axioms), namely Pareto-optimality (PO), Market-equilbrium (ME) and Fiscal-balance (FB). Additionally, the implementation mechanism for w-value is also provided. The w-value exists and is unique. It belongs to the core. And, more specifically, it belongs to the -core. Meanwhile, the computational cost of w-value is very low. Therefore, the w-value is a theoretically more compelling solution concept than the existing cooperation game solutions when analyzing cooperative games with public externalities. A numerical illustration shows the calculation steps of w-value. Meanwhile, the w-value well explains the reason why the mandatory emission reduction mechanism must be transformed into a "nationally determined contribution" mechanism in current international climate negotiations.
Convergence of Iterative Water-Filling in Multi-User Non-Cooperative Power Control: A Comprehensive Analysis for Sequential, Simultaneous, and Asynchronous Schemes
Authors: Tong Wang
Date: 2025-02-17 00:00:00

Non-cooperative game theory provides a robust framework for analyzing distributed resource allocation in multi-user wireless networks, with \emph{Iterative Water-Filling} (IWF) emerging as a canonical solution for power control problems. Although classical fixed-point theorems guarantee the existence of a Nash Equilibrium (NE) under mild concavity and compactness conditions, the convergence of practical iterative algorithms to that equilibrium remains a challenging endeavor. This challenge intensifies under varying update schedules, interference regimes, and imperfections such as channel estimation errors or feedback delay. In this paper, we present an in-depth examination of IWF in multi-user systems under three different update schemes: (1) synchronous \emph{sequential} updates, (2) synchronous \emph{simultaneous} updates, and (3) \emph{totally asynchronous} updates. We first formulate the water-filling operator in a multi-carrier environment, then recast the iterative process as a fixed-point problem. Using contraction mapping principles, we demonstrate sufficient conditions under which IWF converges to a unique NE and highlight how spectral radius constraints, diagonal dominance, and careful step-size selection are pivotal for guaranteeing convergence. We further discuss robustness to measurement noise, partial updates, and network scaling to emphasize the practical viability of these schemes. This comprehensive analysis unifies diverse threads in the literature while offering novel insights into asynchronous implementations. Our findings enable network designers to ascertain system parameters that foster both stable convergence and efficient spectrum usage.
FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation
Authors: Minh Duc Vu, Jieshan Chen, Zhenchang Xing, Qinghua Lu, Xiwei Xu, QiAn Fu
Date: 2025-02-25 00:00:00

With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills. To address this need, existing automatic fact sheet generation tools offer heuristic-based solutions to extract facts and generate stories. However, they inadequately grasp the semantics of data and struggle to generate narratives that fully capture the semantics of the dataset or align the fact sheet with specific user needs. Addressing these shortcomings, this paper introduces \tool, a novel tool designed for the automatic generation and customisation of fact sheets. \tool applies the concept of collaborative AI workers to transform raw tabular dataset into comprehensive, visually compelling fact sheets. We define effective taxonomy to profile AI worker for specialised tasks. Furthermore, \tool empowers users to refine these fact sheets through intuitive natural language commands, ensuring the final outputs align closely with individual preferences and requirements. Our user evaluation with 18 participants confirms that \tool not only surpasses state-of-the-art baselines in automated fact sheet production but also provides a positive user experience during customization tasks.
Towards Sustainable Web Agents: A Plea for Transparency and Dedicated Metrics for Energy Consumption
Authors: Lars Krupp, Daniel Geißler, Paul Lukowicz, Jakob Karolus
Date: 2025-02-25 00:00:00

Improvements in the area of large language models have shifted towards the construction of models capable of using external tools and interpreting their outputs. These so-called web agents have the ability to interact autonomously with the internet. This allows them to become powerful daily assistants handling time-consuming, repetitive tasks while supporting users in their daily activities. While web agent research is thriving, the sustainability aspect of this research direction remains largely unexplored. We provide an initial exploration of the energy and CO2 cost associated with web agents. Our results show how different philosophies in web agent creation can severely impact the associated expended energy. We highlight lacking transparency regarding the disclosure of model parameters and processes used for some web agents as a limiting factor when estimating energy consumption. As such, our work advocates a change in thinking when evaluating web agents, warranting dedicated metrics for energy consumption and sustainability.
VeriPlan: Integrating Formal Verification and LLMs into End-User Planning
Authors: Christine Lee, David Porfirio, Xinyu Jessica Wang, Kevin Zhao, Bilge Mutlu
Date: 2025-02-25 00:00:00

Automated planning is traditionally the domain of experts, utilized in fields like manufacturing and healthcare with the aid of expert planning tools. Recent advancements in LLMs have made planning more accessible to everyday users due to their potential to assist users with complex planning tasks. However, LLMs face several application challenges within end-user planning, including consistency, accuracy, and user trust issues. This paper introduces VeriPlan, a system that applies formal verification techniques, specifically model checking, to enhance the reliability and flexibility of LLMs for end-user planning. In addition to the LLM planner, VeriPlan includes three additional core features -- a rule translator, flexibility sliders, and a model checker -- that engage users in the verification process. Through a user study (n=12), we evaluate VeriPlan, demonstrating improvements in the perceived quality, usability, and user satisfaction of LLMs. Our work shows the effective integration of formal verification and user-control features with LLMs for end-user planning tasks.
Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
Authors: Yoshee Jain, John Hollander, Amber He, Sunny Tang, Liang Zhang, John Sabatini
Date: 2025-02-25 00:00:00
Tasks: Reading Comprehension

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. There is potential for Large Language Models (LLMs) to automate question difficulty estimation; however, this area remains underexplored. Our study investigates the effectiveness of LLMs, specifically OpenAI's GPT-4o and o1, in estimating the difficulty of reading comprehension questions using the Study Aid and Reading Assessment (SARA) dataset. We evaluated both the accuracy of the models in answering comprehension questions and their ability to classify difficulty levels as defined by IRT. The results indicate that, while the models yield difficulty estimates that align meaningfully with derived IRT parameters, there are notable differences in their sensitivity to extreme item characteristics. These findings suggest that LLMs can serve as the scalable method for automated difficulty assessment, particularly in dynamic interactions between learners and Adaptive Instructional Systems (AIS), bridging the gap between traditional psychometric techniques and modern AIS for reading comprehension and paving the way for more adaptive and personalized educational assessments.
Tip of the Tongue Query Elicitation for Simulated Evaluation
Authors: Yifan He, To Eun Kim, Fernando Diaz, Jaime Arguello, Bhaskar Mitra
Date: 2025-02-25 00:00:00
Tasks: Community Question Answering, Question Answering, Retrieval

Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effectively support TOT scenarios. Research on TOT retrieval is further constrained by the challenge of collecting queries, as current approaches rely heavily on community question-answering (CQA) websites, leading to labor-intensive evaluation and domain bias. To overcome these limitations, we introduce two methods for eliciting TOT queries - leveraging large language models (LLMs) and human participants - to facilitate simulated evaluations of TOT retrieval systems. Our LLM-based TOT user simulator generates synthetic TOT queries at scale, achieving high correlations with how CQA-based TOT queries rank TOT retrieval systems when tested in the Movie domain. Additionally, these synthetic queries exhibit high linguistic similarity to CQA-derived queries. For human-elicited queries, we developed an interface that uses visual stimuli to place participants in a TOT state, enabling the collection of natural queries. In the Movie domain, system rank correlation and linguistic similarity analyses confirm that human-elicited queries are both effective and closely resemble CQA-based queries. These approaches reduce reliance on CQA-based data collection while expanding coverage to underrepresented domains, such as Landmark and Person. LLM-elicited queries for the Movie, Landmark, and Person domains have been released as test queries in the TREC 2024 TOT track, with human-elicited queries scheduled for inclusion in the TREC 2025 TOT track. Additionally, we provide source code for synthetic query generation and the human query collection interface, along with curated visual stimuli used for eliciting TOT queries.
Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions
Authors: Zhe Liu, Taekyu Kang, Haoyu Wang, Seyed Hossein Alavi, Vered Shwartz
Date: 2025-02-24 00:00:00
Tasks: Diversity, Informativeness

Effective conversational systems are expected to dynamically generate contextual follow-up questions to elicit new information while maintaining the conversation flow. While humans excel at asking diverse and informative questions by intuitively assessing both obtained and missing information, existing models often fall short of human performance on this task. To mitigate this, we propose a method that generates diverse and informative questions based on targeting unanswered information using a hypothetical LLM-generated "comprehensive answer". Our method is applied to augment an existing follow-up questions dataset. The experimental results demonstrate that language models fine-tuned on the augmented datasets produce follow-up questions of significantly higher quality and diversity. This promising approach could be effectively adopted to future work to augment information-seeking dialogues for reducing ambiguities and improving the accuracy of LLM answers.
On the usability of generative AI: Human generative AI
Authors: Anna Ravera, Cristina Gena
Date: 2025-02-24 00:00:00

Generative AI systems are transforming content creation, but their usability remains a key challenge. This paper examines usability factors such as user experience, transparency, control, and cognitive load. Common challenges include unpredictability and difficulties in fine-tuning outputs. We review evaluation metrics like efficiency, learnability, and satisfaction, highlighting best practices from various domains. Improving interpretability, intuitive interfaces, and user feedback can enhance usability, making generative AI more accessible and effective.
Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventions
Authors: Sameer Neupane, Poorvesh Dongre, Denis Gracanin, Santosh Kumar
Date: 2025-02-24 00:00:00
Tasks: Chatbot, Management

We use a duoethnographic approach to study how wearable-integrated LLM chatbots can assist with personalized stress management, addressing the growing need for immediacy and tailored interventions. Two researchers interacted with custom chatbots over 22 days, responding to wearable-detected physiological prompts, recording stressor phrases, and using them to seek tailored interventions from their LLM-powered chatbots. They recorded their experiences in autoethnographic diaries and analyzed them during weekly discussions, focusing on the relevance, clarity, and impact of chatbot-generated interventions. Results showed that even though most events triggered by the wearable were meaningful, only one in five warranted an intervention. It also showed that interventions tailored with brief event descriptions were more effective than generic ones. By examining the intersection of wearables and LLM, this research contributes to developing more effective, user-centric mental health tools for real-time stress relief and behavior change.
Socratic: Enhancing Human Teamwork via AI-enabled Coaching
Authors: Sangwon Seo, Bing Han, Rayan E. Harari, Roger D. Dias, Marco A. Zenati, Eduardo Salas, Vaibhav Unhelkar
Date: 2025-02-24 00:00:00
Tasks: Disaster Response

Coaches are vital for effective collaboration, but cost and resource constraints often limit their availability during real-world tasks. This limitation poses serious challenges in life-critical domains that rely on effective teamwork, such as healthcare and disaster response. To address this gap, we propose and realize an innovative application of AI: task-time team coaching. Specifically, we introduce Socratic, a novel AI system that complements human coaches by providing real-time guidance during task execution. Socratic monitors team behavior, detects misalignments in team members' shared understanding, and delivers automated interventions to improve team performance. We validated Socratic through two human subject experiments involving dyadic collaboration. The results demonstrate that the system significantly enhances team performance with minimal interventions. Participants also perceived Socratic as helpful and trustworthy, supporting its potential for adoption. Our findings also suggest promising directions both for AI research and its practical applications to enhance human teamwork.
Brain-to-Text Decoding: A Non-invasive Approach via Typing
Authors: Jarod Lévy, Mingfang Zhang, Svetlana Pinet, Jérémy Rapin, Hubert Banville, Stéphane d'Ascoli, Jean-Rémi King
Date: 2025-02-18 00:00:00
Tasks: EEG

Modern neuroprostheses can now restore communication in patients who have lost the ability to speak or move. However, these invasive devices entail risks inherent to neurosurgery. Here, we introduce a non-invasive method to decode the production of sentences from brain activity and demonstrate its efficacy in a cohort of 35 healthy volunteers. For this, we present Brain2Qwerty, a new deep learning architecture trained to decode sentences from either electro- (EEG) or magneto-encephalography (MEG), while participants typed briefly memorized sentences on a QWERTY keyboard. With MEG, Brain2Qwerty reaches, on average, a character-error-rate (CER) of 32% and substantially outperforms EEG (CER: 67%). For the best participants, the model achieves a CER of 19%, and can perfectly decode a variety of sentences outside of the training set. While error analyses suggest that decoding depends on motor processes, the analysis of typographical errors suggests that it also involves higher-level cognitive factors. Overall, these results narrow the gap between invasive and non-invasive methods and thus open the path for developing safe brain-computer interfaces for non-communicating patients.
Reversal Blessing: Thinking Backward May Outpace Thinking Forward in Multi-choice Questions
Authors: Yizhe Zhang, Richard Bai, Zijin Gu, Ruixiang Zhang, Jiatao Gu, Emmanuel Abbe, Samy Bengio, Navdeep Jaitly
Date: 2025-02-25 00:00:00
Tasks: Inductive Bias, Logical Reasoning, Multiple-choice

Language models usually use left-to-right (L2R) autoregressive factorization. However, L2R factorization may not always be the best inductive bias. Therefore, we investigate whether alternative factorizations of the text distribution could be beneficial in some tasks. We investigate right-to-left (R2L) training as a compelling alternative, focusing on multiple-choice questions (MCQs) as a test bed for knowledge extraction and reasoning. Through extensive experiments across various model sizes (2B-8B parameters) and training datasets, we find that R2L models can significantly outperform L2R models on several MCQ benchmarks, including logical reasoning, commonsense understanding, and truthfulness assessment tasks. Our analysis reveals that this performance difference may be fundamentally linked to multiple factors including calibration, computability and directional conditional entropy. We ablate the impact of these factors through controlled simulation studies using arithmetic tasks, where the impacting factors can be better disentangled. Our work demonstrates that exploring alternative factorizations of the text distribution can lead to improvements in LLM capabilities and provides theoretical insights into optimal factorization towards approximating human language distribution, and when each reasoning order might be more advantageous.
Iterative Counterfactual Data Augmentation
Authors: Mitchell Plyler, Min Chi
Date: 2025-02-25 00:00:00
Tasks: counterfactual, Data Augmentation, Language Modeling, Language Modelling, Large Language Model

Counterfactual data augmentation (CDA) is a method for controlling information or biases in training datasets by generating a complementary dataset with typically opposing biases. Prior work often either relies on hand-crafted rules or algorithmic CDA methods which can leave unwanted information in the augmented dataset. In this work, we show iterative CDA (ICDA) with initial, high-noise interventions can converge to a state with significantly lower noise. Our ICDA procedure produces a dataset where one target signal in the training dataset maintains high mutual information with a corresponding label and the information of spurious signals are reduced. We show training on the augmented datasets produces rationales on documents that better align with human annotation. Our experiments include six human produced datasets and two large-language model generated datasets.
ToMCAT: Theory-of-Mind for Cooperative Agents in Teams via Multiagent Diffusion Policies
Authors: Pedro Sequeira, Vidyasagar Sadhu, Melinda Gervasio
Date: 2025-02-25 00:00:00
Tasks: Denoising, Meta-Learning

In this paper we present ToMCAT (Theory-of-Mind for Cooperative Agents in Teams), a new framework for generating ToM-conditioned trajectories. It combines a meta-learning mechanism, that performs ToM reasoning over teammates' underlying goals and future behavior, with a multiagent denoising-diffusion model, that generates plans for an agent and its teammates conditioned on both the agent's goals and its teammates' characteristics, as computed via ToM. We implemented an online planning system that dynamically samples new trajectories (replans) from the diffusion model whenever it detects a divergence between a previously generated plan and the current state of the world. We conducted several experiments using ToMCAT in a simulated cooking domain. Our results highlight the importance of the dynamic replanning mechanism in reducing the usage of resources without sacrificing team performance. We also show that recent observations about the world and teammates' behavior collected by an agent over the course of an episode combined with ToM inferences are crucial to generate team-aware plans for dynamic adaptation to teammates, especially when no prior information is provided about them.
Smart and Efficient IoT-Based Irrigation System Design: Utilizing a Hybrid Agent-Based and System Dynamics Approach
Authors: Taha Ahmadi Pargo, Mohsen Akbarpour Shirazi, Dawud Fadai
Date: 2025-02-25 00:00:00

Regarding problems like reduced precipitation and an increase in population, water resource scarcity has become one of the most critical problems in modern-day societies, as a consequence, there is a shortage of available water resources for irrigation in arid and semi-arid countries. On the other hand, it is possible to utilize modern technologies to control irrigation and reduce water loss. One of these technologies is the Internet of Things (IoT). Despite the possibility of using the IoT in irrigation control systems, there are complexities in designing such systems. Considering this issue, it is possible to use agent-oriented software engineering (AOSE) methodologies to design complex cyber-physical systems such as IoT-based systems. In this research, a smart irrigation system is designed based on Prometheus AOSE methodology, to reduce water loss by maintaining soil moisture in a suitable interval. The designed system comprises sensors, a central agent, and irrigation nodes. These agents follow defined rules to maintain soil moisture at a desired level cooperatively. For system simulation, a hybrid agent-based and system dynamics model was designed. In this hybrid model, soil moisture dynamics were modeled based on the system dynamics approach. The proposed model, was implemented in AnyLogic computer simulation software. Utilizing the simulation model, irrigation rules were examined. The system's functionality in automatic irrigation mode was tested based on a 256-run, fractional factorial design, and the effects of important factors such as soil properties on total irrigated water and total operation time were analyzed. Based on the tests, the system consistently irrigated nearly optimal water amounts in all tests. Moreover, the results were also used to minimize the system's energy consumption by reducing the system's operational time.