pwc-archive/papers-with-abstracts huggingface dataset:
Ornithologist: Towards Trustworthy "Reasoning" about Central Bank Communications
Authors: Dominic Zaun Eu Jones
Date: 2025-05-14 00:00:00
Tasks: Hallucination, Language Modeling, Language Modelling, Large Language Model

I develop Ornithologist, a weakly-supervised textual classification system and measure the hawkishness and dovishness of central bank text. Ornithologist uses ``taxonomy-guided reasoning'', guiding a large language model with human-authored decision trees. This increases the transparency and explainability of the system and makes it accessible to non-experts. It also reduces hallucination risk. Since it requires less supervision than traditional classification systems, it can more easily be applied to other problems or sources of text (e.g. news) without much modification. Ornithologist measurements of hawkishness and dovishness of RBA communication carry information about the future of the cash rate path and of market expectations.
S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment
Authors: Jennifer Haase, Paul H. P. Hanel, Sebastian Pokutta
Date: 2025-05-14 00:00:00

This paper introduces S-DAT (Synthetic-Divergent Association Task), a scalable, multilingual framework for automated assessment of divergent thinking (DT) -a core component of human creativity. Traditional creativity assessments are often labor-intensive, language-specific, and reliant on subjective human ratings, limiting their scalability and cross-cultural applicability. In contrast, S-DAT leverages large language models and advanced multilingual embeddings to compute semantic distance -- a language-agnostic proxy for DT. We evaluate S-DAT across eleven diverse languages, including English, Spanish, German, Russian, Hindi, and Japanese (Kanji, Hiragana, Katakana), demonstrating robust and consistent scoring across linguistic contexts. Unlike prior DAT approaches, the S-DAT shows convergent validity with other DT measures and correct discriminant validity with convergent thinking. This cross-linguistic flexibility allows for more inclusive, global-scale creativity research, addressing key limitations of earlier approaches. S-DAT provides a powerful tool for fairer, more comprehensive evaluation of cognitive flexibility in diverse populations and can be freely assessed online: https://sdat.iol.zib.de/.
A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias
Authors: Brandon Smith, Mohamed Reda Bouadjenek, Tahsin Alamgir Kheya, Phillip Dawson, Sunil Aryal
Date: 2025-05-14 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Large Language Model

Large Language Models (LLMs) represent a major step toward artificial general intelligence, significantly advancing our ability to interact with technology. While LLMs perform well on Natural Language Processing tasks -- such as translation, generation, code writing, and summarization -- questions remain about their output similarity, variability, and ethical implications. For instance, how similar are texts generated by the same model? How does this compare across different models? And which models best uphold ethical standards? To investigate, we used 5{,}000 prompts spanning diverse tasks like generation, explanation, and rewriting. This resulted in approximately 3 million texts from 12 LLMs, including proprietary and open-source systems from OpenAI, Google, Microsoft, Meta, and Mistral. Key findings include: (1) outputs from the same LLM are more similar to each other than to human-written texts; (2) models like WizardLM-2-8x22b generate highly similar outputs, while GPT-4 produces more varied responses; (3) LLM writing styles differ significantly, with Llama 3 and Mistral showing higher similarity, and GPT-4 standing out for distinctiveness; (4) differences in vocabulary and tone underscore the linguistic uniqueness of LLM-generated content; (5) some LLMs demonstrate greater gender balance and reduced bias. These results offer new insights into the behavior and diversity of LLM outputs, helping guide future development and ethical evaluation.
Atomic Consistency Preference Optimization for Long-Form Question Answering
Authors: Jingfeng Chen, Raghuveer Thirukovalluru, Junlin Wang, Kaiwei Luo, Bhuwan Dhingra
Date: 2025-05-14 00:00:00
Tasks: Form, Long Form Question Answering, Question Answering

Large Language Models (LLMs) frequently produce factoid hallucinations - plausible yet incorrect answers. A common mitigation strategy is model alignment, which improves factual accuracy by training on curated factual and non-factual pairs. However, this approach often relies on a stronger model (e.g., GPT-4) or an external knowledge base to assess factual correctness, which may not always be accessible. To address this, we propose Atomic Consistency Preference Optimization (ACPO), a self-supervised preference-tuning method that enhances factual accuracy without external supervision. ACPO leverages atomic consistency signals, i.e., the agreement of individual facts across multiple stochastic responses, to identify high- and low-quality data pairs for model alignment. By eliminating the need for costly GPT calls, ACPO provides a scalable and efficient approach to improving factoid question-answering. Despite being self-supervised, empirical results demonstrate that ACPO outperforms FactAlign, a strong supervised alignment baseline, by 1.95 points on the LongFact and BioGen datasets, highlighting its effectiveness in enhancing factual reliability without relying on external models or knowledge bases.
For GPT-4 as with Humans: Information Structure Predicts Acceptability of Long-Distance Dependencies
Authors: Nicole Cuneo, Eleanor Graves, Supantho Rakshit, Adele E. Goldberg
Date: 2025-05-13 00:00:00
Tasks: Sentence

It remains debated how well any LM understands natural language or generates reliable metalinguistic judgments. Moreover, relatively little work has demonstrated that LMs can represent and respect subtle relationships between form and function proposed by linguists. We here focus on a particular such relationship established in recent work: English speakers' judgments about the information structure of canonical sentences predicts independently collected acceptability ratings on corresponding 'long distance dependency' [LDD] constructions, across a wide array of base constructions and multiple types of LDDs. To determine whether any LM captures this relationship, we probe GPT-4 on the same tasks used with humans and new extensions.Results reveal reliable metalinguistic skill on the information structure and acceptability tasks, replicating a striking interaction between the two, despite the zero-shot, explicit nature of the tasks, and little to no chance of contamination [Studies 1a, 1b]. Study 2 manipulates the information structure of base sentences and confirms a causal relationship: increasing the prominence of a constituent in a context sentence increases the subsequent acceptability ratings on an LDD construction. The findings suggest a tight relationship between natural and GPT-4 generated English, and between information structure and syntax, which begs for further exploration.
A suite of LMs comprehend puzzle statements as well as humans
Authors: Adele E Goldberg, Supantho Rakshit, Jennifer Hu, Kyle Mahowald
Date: 2025-05-13 00:00:00
Tasks: Experimental Design

Recent claims suggest that large language models (LMs) underperform humans in comprehending minimally complex English statements (Dentella et al., 2024). Here, we revisit those findings and argue that human performance was overestimated, while LLM abilities were underestimated. Using the same stimuli, we report a preregistered study comparing human responses in two conditions: one allowed rereading (replicating the original study), and one that restricted rereading (a more naturalistic comprehension test). Human accuracy dropped significantly when rereading was restricted (73%), falling below that of Falcon-180B-Chat (76%) and GPT-4 (81%). The newer GPT-o1 model achieves perfect accuracy. Results further show that both humans and models are disproportionately challenged by queries involving potentially reciprocal actions (e.g., kissing), suggesting shared pragmatic sensitivities rather than model-specific deficits. Additional analyses using Llama-2-70B log probabilities, a recoding of open-ended model responses, and grammaticality ratings of other sentences reveal systematic underestimation of model performance. We find that GPT-4o can align with either naive or expert grammaticality judgments, depending on prompt framing. These findings underscore the need for more careful experimental design and coding practices in LLM evaluation, and they challenge the assumption that current models are inherently weaker than humans at language comprehension.
Clicking some of the silly options: Exploring Player Motivation in Static and Dynamic Educational Interactive Narratives
Authors: Daeun Hwang, Samuel Shields, Alex Calderwood, Shi Johnson-Bey, Michael Mateas, Noah Wardrip-Fruin, Edward F. Melcer
Date: 2025-05-13 00:00:00
Tasks: Ethics

Motivation is an important factor underlying successful learning. Previous research has demonstrated the positive effects that static interactive narrative games can have on motivation. Concurrently, advances in AI have made dynamic and adaptive approaches to interactive narrative increasingly accessible. However, limited work has explored the impact that dynamic narratives can have on learner motivation. In this paper, we compare two versions of Academical, a choice-based educational interactive narrative game about research ethics. One version employs a traditional hand-authored branching plot (i.e., static narrative) while the other dynamically sequences plots during play (i.e., dynamic narrative). Results highlight the importance of responsive content and a variety of choices for player engagement, while also illustrating the challenge of balancing pedagogical goals with the dynamic aspects of narrative. We also discuss design implications that arise from these findings. Ultimately, this work provides initial steps to illuminate the emerging potential of AI-driven dynamic narrative in educational games.
LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries
Authors: Zekun Wu, Seonglae Cho, Umar Mohammed, Cristian Munoz, Kleyton Costa, Xin Guan, Theo King, Ze Wang, Emre Kazim, Adriano Koshiyama
Date: 2025-05-13 00:00:00

Open-source AI libraries are foundational to modern AI systems but pose significant, underexamined risks across security, licensing, maintenance, supply chain integrity, and regulatory compliance. We present LibVulnWatch, a graph-based agentic assessment framework that performs deep, source-grounded evaluations of these libraries. Built on LangGraph, the system coordinates a directed acyclic graph of specialized agents to extract, verify, and quantify risk using evidence from trusted sources such as repositories, documentation, and vulnerability databases. LibVulnWatch generates reproducible, governance-aligned scores across five critical domains, publishing them to a public leaderboard for longitudinal ecosystem monitoring. Applied to 20 widely used libraries, including ML frameworks, LLM inference engines, and agent orchestration tools, our system covers up to 88% of OpenSSF Scorecard checks while uncovering up to 19 additional risks per library. These include critical Remote Code Execution (RCE) vulnerabilities, absent Software Bills of Materials (SBOMs), licensing constraints, undocumented telemetry, and widespread gaps in regulatory documentation and auditability. By translating high-level governance principles into practical, verifiable metrics, LibVulnWatch advances technical AI governance with a scalable, transparent mechanism for continuous supply chain risk assessment and informed library selection.
Equilibrio de carga para transformadores de distribución eléctrica mejorando la calidad de servicio en fin de línea
Authors: Juan M. Bordón, Victor A. Jimenez, Adrian Will
Date: 2025-05-14 00:00:00

The distribution of electrical energy faces global challenges, such as increasing demand, the integration of distributed generation, high energy losses, and the need to improve service quality. In particular, load imbalance-where loads are not evenly distributed across the circuit phase-can reduce efficiency, shorten equipment lifespan, and increase susceptibility to service interruptions. While methods that involve shifting loads from one phase to another can be costly, they are effective when smart meters are available and implemented efficiently. This work proposes the use of genetic algorithms to optimally identify which loads should be reassigned in order to improve both phase balance and voltage quality at the end nodes of the network while minimizing the number of required changes. The algorithm was evaluated through simulations using PandaPower, a power flow analysis tool, modeling simple networks based on real-world characteristics of the electrical system in Tucum\'an.
A Standardized Benchmark Set of Clustering Problem Instances for Comparing Black-Box Optimizers
Authors: Diederick Vermetten, Catalin-Viorel Dinu, Marcus Gallagher
Date: 2025-05-14 00:00:00
Tasks: Benchmarking, Clustering

One key challenge in optimization is the selection of a suitable set of benchmark problems. A common goal is to find functions which are representative of a class of real-world optimization problems in order to ensure findings on the benchmarks will translate to relevant problem domains. While some problem characteristics are well-covered by popular benchmarking suites, others are often overlooked. One example of such a problem characteristic is permutation invariance, where the search space consists of a set of symmetrical search regions. This type of problem occurs e.g. when a set of solutions has to be found, but the ordering within this set does not matter. The data clustering problem, often seen in machine learning contexts, is a clear example of such an optimization landscape, and has thus been proposed as a base from which optimization benchmarks can be created. In addition to the symmetry aspect, these clustering problems also contain potential regions of neutrality, which can provide an additional challenge to optimization algorithms. In this paper, we present a standardized benchmark suite for the evaluation of continuous black-box optimization algorithms, based on data clustering problems. To gain insight into the diversity of the benchmark set, both internally and in comparison to existing suites, we perform a benchmarking study of a set of modular CMA-ES configurations, as well as an analysis using exploratory landscape analysis. Our benchmark set is open-source and integrated with the IOHprofiler benchmarking framework to encourage its use in future research.
Distance-aware Self-adaptive Graph Convolution for Fine-grained Hierarchical Recommendation
Authors: Tao Huang, Yihong Chen, Wei Fan, Wei Zhou, Junhao Wen
Date: 2025-05-14 00:00:00

Graph Convolutional Networks (GCNs) are widely used to improve recommendation accuracy and performance by effectively learning the representations of user and item nodes. However, two major challenges remain: (1) the lack of further optimization in the graph representation structure and (2) insufficient attention given to the varying contributions of different convolutional layers.This paper proposes SAGCN, a distance-based adaptive hierarchical aggregation method that refines the aggregation process through differentiated representation metrics. SAGCN introduces a detailed approach to multilayer information aggregation and representation space optimization, enabling the model to learn hierarchical embedding weights based on the distance between hierarchical representations. This innovation allows for more precise cross-layer information aggregation, improves the model's ability to capture hierarchical embeddings, and optimizes the representation space structure. Additionally, the objective loss function is refined to better align with recommendation tasks.Extensive experiments conducted on four real-world datasets demonstrate significant improvements, including over a 5% increase on Yelp and a 5.58% increase in Recall@10 on the ML_1M dataset.
GlobalMood: A cross-cultural benchmark for music emotion recognition
Authors: Harin Lee, Elif Çelen, Peter Harrison, Manuel Anglada-Tort, Pol van Rijn, Minsu Park, Marc Schönwiesner, Nori Jacoby
Date: 2025-05-14 00:00:00
Tasks: Emotion Recognition, Music Emotion Recognition, Music Generation, Recommendation Systems, Specificity

Human annotations of mood in music are essential for music generation and recommender systems. However, existing datasets predominantly focus on Western songs with mood terms derived from English, which may limit generalizability across diverse linguistic and cultural backgrounds. To address this, we introduce `GlobalMood', a novel cross-cultural benchmark dataset comprising 1,180 songs sampled from 59 countries, with large-scale annotations collected from 2,519 individuals across five culturally and linguistically distinct locations: U.S., France, Mexico, S. Korea, and Egypt. Rather than imposing predefined mood categories, we implement a bottom-up, participant-driven approach to organically elicit culturally specific music-related mood terms. We then recruit another pool of human participants to collect 988,925 ratings for these culture-specific descriptors. Our analysis confirms the presence of a valence-arousal structure shared across cultures, yet also reveals significant divergences in how certain mood terms, despite being dictionary equivalents, are perceived cross-culturally. State-of-the-art multimodal models benefit substantially from fine-tuning on our cross-culturally balanced dataset, as evidenced by improved alignment with human evaluations - particularly in non-English contexts. More broadly, our findings inform the ongoing debate on the universality versus cultural specificity of emotional descriptors, and our methodology can contribute to other multimodal and cross-lingual research.
FACTors: A New Dataset for Studying the Fact-checking Ecosystem
Authors: Enes Altuncu, Can Başkent, Sanjay Bhattacherjee, Shujun Li, Dwaipayan Roy
Date: 2025-05-14 00:00:00
Tasks: Fact Checking

Our fight against false information is spearheaded by fact-checkers. They investigate the veracity of claims and document their findings as fact-checking reports. With the rapid increase in the amount of false information circulating online, the use of automation in fact-checking processes aims to strengthen this ecosystem by enhancing scalability. Datasets containing fact-checked claims play a key role in developing such automated solutions. However, to the best of our knowledge, there is no fact-checking dataset at the ecosystem level, covering claims from a sufficiently long period of time and sourced from a wide range of actors reflecting the entire ecosystem that admittedly follows widely-accepted codes and principles of fact-checking. We present a new dataset FACTors, the first to fill this gap by presenting ecosystem-level data on fact-checking. It contains 118,112 claims from 117,993 fact-checking reports in English (co-)authored by 1,953 individuals and published during the period of 1995-2025 by 39 fact-checking organisations that are active signatories of the IFCN (International Fact-Checking Network) and/or EFCSN (European Fact-Checking Standards Network). It contains 7,327 overlapping claims investigated by multiple fact-checking organisations, corresponding to 2,977 unique claims. It allows to conduct new ecosystem-level studies of the fact-checkers (organisations and individuals). To demonstrate the usefulness of FACTors, we present three example applications, including a first-of-its-kind statistical analysis of the fact-checking ecosystem, examining the political inclinations of the fact-checking organisations, and attempting to assign a credibility score to each organisation based on the findings of the statistical analysis and political leanings. Our methods for constructing FACTors are generic and can be used to maintain a live dataset that can be updated dynamically.
HMamba: Hyperbolic Mamba for Sequential Recommendation
Authors: Qianru Zhang, Honggang Wen, Wei Yuan, Crystal Chen, Menglin Yang, Siu-Ming Yiu, Hongzhi Yin
Date: 2025-05-14 00:00:00
Tasks: Mamba, Recommendation Systems, Sequential Recommendation

Sequential recommendation systems have become a cornerstone of personalized services, adept at modeling the temporal evolution of user preferences by capturing dynamic interaction sequences. Existing approaches predominantly rely on traditional models, including RNNs and Transformers. Despite their success in local pattern recognition, Transformer-based methods suffer from quadratic computational complexity and a tendency toward superficial attention patterns, limiting their ability to infer enduring preference hierarchies in sequential recommendation data. Recent advances in Mamba-based sequential models introduce linear-time efficiency but remain constrained by Euclidean geometry, failing to leverage the intrinsic hyperbolic structure of recommendation data. To bridge this gap, we propose Hyperbolic Mamba, a novel architecture that unifies the efficiency of Mamba's selective state space mechanism with hyperbolic geometry's hierarchical representational power. Our framework introduces (1) a hyperbolic selective state space that maintains curvature-aware sequence modeling and (2) stabilized Riemannian operations to enable scalable training. Experiments across four benchmarks demonstrate that Hyperbolic Mamba achieves 3-11% improvement while retaining Mamba's linear-time efficiency, enabling real-world deployment. This work establishes a new paradigm for efficient, hierarchy-aware sequential modeling.
Display Content, Display Methods and Evaluation Methods of the HCI in Explainable Recommender Systems: A Survey
Authors: Weiqing Li, Yue Xu, Yuefeng Li, Yinghui Huang
Date: 2025-05-14 00:00:00
Tasks: Recommendation Systems

Explainable Recommender Systems (XRS) aim to provide users with understandable reasons for the recommendations generated by these systems, representing a crucial research direction in artificial intelligence (AI). Recent research has increasingly focused on the algorithms, display, and evaluation methodologies of XRS. While current research and reviews primarily emphasize the algorithmic aspects, with fewer studies addressing the Human-Computer Interaction (HCI) layer of XRS. Additionally, existing reviews lack a unified taxonomy for XRS and there is insufficient attention given to the emerging area of short video recommendations. In this study, we synthesize existing literature and surveys on XRS, presenting a unified framework for its research and development. The main contributions are as follows: 1) We adopt a lifecycle perspective to systematically summarize the technologies and methods used in XRS, addressing challenges posed by the diversity and complexity of algorithmic models and explanation techniques. 2) For the first time, we highlight the application of multimedia, particularly video-based explanations, along with its potential, technical pathways, and challenges in XRS. 3) We provide a structured overview of evaluation methods from both qualitative and quantitative dimensions. These findings provide valuable insights for the systematic design, progress, and testing of XRS.