pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction
Authors: Saurabh Srivastava, Ziyu Yao
Date: 2025-04-10 00:00:00
Tasks: Event Extraction, Prompt Engineering

Large Reasoning Models (LRMs) such as DeepSeek-R1 and OpenAI o1 have demonstrated remarkable capabilities in various reasoning tasks. Their strong capability to generate and reason over intermediate thoughts has also led to arguments that they may no longer require extensive prompt engineering or optimization to interpret human instructions and produce accurate outputs. In this work, we aim to systematically study this open question, using the structured task of event extraction for a case study. We experimented with two LRMs (DeepSeek-R1 and o1) and two general-purpose Large Language Models (LLMs) (GPT-4o and GPT-4.5), when they were used as task models or prompt optimizers. Our results show that on tasks as complicated as event extraction, LRMs as task models still benefit from prompt optimization, and that using LRMs as prompt optimizers yields more effective prompts. Finally, we provide an error analysis of common errors made by LRMs and highlight the stability and consistency of LRMs in refining task instructions and event guidelines.
Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMs
Authors: Taibiao Zhao, Xiaobing Chen, Mingxuan Sun
Date: 2025-04-10 00:00:00
Tasks: Multimodal Large Language Model, Time Series, Time Series Forecasting

The adaptation of large language models (LLMs) to time series forecasting poses unique challenges, as time series data is continuous in nature, while LLMs operate on discrete tokens. Despite the success of LLMs in natural language processing (NLP) and other structured domains, aligning time series data with language-based representations while maintaining both predictive accuracy and interpretability remains a significant hurdle. Existing methods have attempted to reprogram time series data into text-based forms, but these often fall short in delivering meaningful, interpretable results. In this paper, we propose a multi-level text alignment framework for time series forecasting using LLMs that not only improves prediction accuracy but also enhances the interpretability of time series representations. Our method decomposes time series into trend, seasonal, and residual components, which are then reprogrammed into component-specific text representations. We introduce a multi-level alignment mechanism, where component-specific embeddings are aligned with pre-trained word tokens, enabling more interpretable forecasts. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art models in accuracy while providing good interpretability.
negativas: a prototype for searching and classifying sentential negation in speech data
Authors: Túlio Sousa de Gois, Paloma Batista Cardoso
Date: 2025-04-05 00:00:00
Tasks: Negation

Negation is a universal feature of natural languages. In Brazilian Portuguese, the most commonly used negation particle is n\~ao, which can scope over nouns or verbs. When it scopes over a verb, n\~ao can occur in three positions: pre-verbal (NEG1), double negation (NEG2), or post-verbal (NEG3), e.g., n\~ao gosto, n\~ao gosto n\~ao, gosto n\~ao ("I do not like it"). From a variationist perspective, these structures are different forms of expressing negation. Pragmatically, they serve distinct communicative functions, such as politeness and modal evaluation. Despite their grammatical acceptability, these forms differ in frequency. NEG1 dominates across Brazilian regions, while NEG2 and NEG3 appear more rarely, suggesting its use is contextually restricted. This low-frequency challenges research, often resulting in subjective, non-generalizable interpretations of verbal negation with n\~ao. To address this, we developed negativas, a tool for automatically identifying NEG1, NEG2, and NEG3 in transcribed data. The tool's development involved four stages: i) analyzing a dataset of 22 interviews from the Falares Sergipanos database, annotated by three linguists, ii) creating a code using natural language processing (NLP) techniques, iii) running the tool, iv) evaluating accuracy. Inter-annotator consistency, measured using Fleiss' Kappa, was moderate (0.57). The tool identified 3,338 instances of n\~ao, classifying 2,085 as NEG1, NEG2, or NEG3, achieving a 93% success rate. However, negativas has limitations. NEG1 accounted for 91.5% of identified structures, while NEG2 and NEG3 represented 7.2% and 1.2%, respectively. The tool struggled with NEG2, sometimes misclassifying instances as overlapping structures (NEG1/NEG2/NEG3).
FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness
Authors: Chandan Kumar Sah, Xiaoli Lian, Tony Xu, Li Zhang
Date: 2025-04-10 00:00:00
Tasks: Fairness, Recommendation Systems, Sensitivity

Recent advances in Large Language Models (LLMs) have enabled their application to recommender systems (RecLLMs), yet concerns remain regarding fairness across demographic and psychological user dimensions. We introduce FairEval, a novel evaluation framework to systematically assess fairness in LLM-based recommendations. FairEval integrates personality traits with eight sensitive demographic attributes,including gender, race, and age, enabling a comprehensive assessment of user-level bias. We evaluate models, including ChatGPT 4o and Gemini 1.5 Flash, on music and movie recommendations. FairEval's fairness metric, PAFS, achieves scores up to 0.9969 for ChatGPT 4o and 0.9997 for Gemini 1.5 Flash, with disparities reaching 34.79 percent. These results highlight the importance of robustness in prompt sensitivity and support more inclusive recommendation systems.
A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
Authors: Tian Xie, Menghui Jiang, Huanfeng Shen, Huifang Li, Chao Zeng, Jun Ma, Guanhao Zhang, Liangpei Zhang
Date: 2025-04-10 00:00:00
Tasks: Physics-informed machine learning, Retrieval

Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inverse problem, which becomes particularly severe when only a single band is available. In this paper, we propose a deeply coupled framework integrating mechanistic modeling and machine learning to enhance the accuracy and generalizability of single-channel LST retrieval. Training samples are generated using a physically-based radiative transfer model and a global collection of 5810 atmospheric profiles. A physics-informed machine learning framework is proposed to systematically incorporate the first principles from classical physical inversion models into the learning workflow, with optimization constrained by radiative transfer equations. Global validation demonstrated a 30% reduction in root-mean-square error versus standalone methods. Under extreme humidity, the mean absolute error decreased from 4.87 K to 2.29 K (53% improvement). Continental-scale tests across five continents confirmed the superior generalizability of this model.
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Authors: Qi Liu, Jiaxin Mao, Ji-Rong Wen
Date: 2025-04-10 00:00:00
Tasks: Document Ranking, Information Retrieval

Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment generation. However, the internal mechanisms by which off-the-shelf LLMs understand and operationalize relevance remain largely unexplored. In this paper, we systematically investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability. Using activation patching techniques, we analyze the roles of various model components and identify a multi-stage, progressive process in generating either pointwise or pairwise relevance judgment. Specifically, LLMs first extract query and document information in the early layers, then process relevance information according to instructions in the middle layers, and finally utilize specific attention heads in the later layers to generate relevance judgments in the required format. Our findings provide insights into the mechanisms underlying relevance assessment in LLMs, offering valuable implications for future research on leveraging LLMs for IR tasks.
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
Authors: Moritz Rempe, Fabian Hörst, Helmut Becker, Marco Schlimbach, Lukas Rotkopf, Kevin Kröninger, Jens Kleesiek
Date: 2025-04-10 00:00:00
Tasks: Diagnostic, MRI Reconstruction, Skull Stripping, Tumor Segmentation

Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magnitude images, discarding the phase data despite its potential for downstream tasks, such as tumor segmentation and classification. In this work, we introduce $\textit{PhaseGen}$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice. This enables the creation of artificial complex-valued raw data, allowing pretraining for models that require k-Space information. We evaluate PhaseGen on two tasks: skull-stripping directly in k-Space and MRI reconstruction using the publicly available FastMRI dataset. Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data, with an increased segmentation accuracy from $41.1\%$ to $80.1\%$, and enhances MRI reconstruction when combined with limited real-world data. This work presents a step forward in utilizing generative AI to bridge the gap between magnitude-based datasets and the complex-valued nature of MRI raw data. This approach allows researchers to leverage the vast amount of avaliable image domain data in combination with the information-rich k-Space data for more accurate and efficient diagnostic tasks. We make our code publicly $\href{https://github.com/TIO-IKIM/PhaseGen}{\text{available here}}$.
What nudges residents' funding-participation behavior in urban settlement regeneration? A perspective of evolving social network
Authors: Huili Li, Xiaodong Yang*, Shuyi Niu
Date: 2025-03-01 00:00:00
Proceeding: Environmental Impact Assessment Review 2025 3

Urban settlement regeneration faces financial pressures, while the economic participation potential of residents, the primary beneficiaries, remains largely unexploited. Residents are embedded in complex social networks, and their funding-participation behavior is dynamically influenced by network structures. However, developing behavior nudging methods that adapt to evolving social networks remain challenging. This study constructs a conceptual model based on the extended theory of planned behavior, integrating factors from individual psy-chological cognition and social practice motivation to explain funding-participation behavior. The model was validated through an empirical survey of 505 respondents in Chongqing, China. Subsequently, a scale-free network was employed to characterize the evolving social network formed through opinion interactions. An interaction model was further developed to reveal the dynamic nudging effects of influencing factors embedded in the social network. Simulation experiments were conducted to elucidate the dynamic process of residents adopting funding-participation behavior. Empirical results indicate that stimulating psychological cognitive and practice motivation could significantly influence funding-participation behavior. Simulation results demonstrate that evolving social networks serve as a crucial medium for nudging this behavior. It also advocates leveraging social learning, opinion dissemination, and moral contagion processes within networks to shape funding- participation propensity, and promoting behavior by overcoming path dependence, activating social capital support, and strengthening benefit perception. To nudge funding-participation behavior, policy implications are proposed to advance inclusive settlement regeneration and construct sustainable participation mechanisms.
AROMA: Autonomous Rank-one Matrix Adaptation
Authors: Hao Nan Sheng, Zhi-Yong Wang, Mingrui Yang, Hing Cheung So
Date: 2025-04-06 00:00:00
Tasks: Natural Language Understanding, parameter-efficient fine-tuning

As large language models continue to grow in size, parameter-efficient fine-tuning has become increasingly crucial. While low-rank adaptation (LoRA) offers a solution through low-rank updates, its static rank allocation may yield suboptimal results. Adaptive low-rank adaptation (AdaLoRA) improves this with dynamic allocation but remains sensitive to initial and target rank configurations. We introduce AROMA, a framework that automatically constructs layer-specific updates by iteratively building up rank-one components with very few trainable parameters that gradually diminish to zero. Unlike existing methods that employ rank reduction mechanisms, AROMA introduces a dual-loop architecture for rank growth. The inner loop extracts information from each rank-one subspace, while the outer loop determines the number of rank-one subspaces, i.e., the optimal rank. We reset optimizer states to maintain subspace independence. AROMA significantly reduces parameters compared to LoRA and AdaLoRA while achieving superior performance on natural language understanding and commonsense reasoning tasks, offering new insights into adaptive parameter-efficient fine-tuning. The code is available at \href{https://github.com/ShuDun23/AROMA}{AROMA}.
Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides
Authors: David Medina-Ortiz 1, 2, Seba Contreras 3, *OrcID, Diego Fernández 1, Nicole Soto-García 1ORCID, Iván Moya 1, 4ORCID, Gabriel Cabas-Mora 1ORCID andÁlvaro Olivera-Nappa
Date: 2024-08-14 00:00:00
Proceeding: International Journal of Molecular Sciences 2024 8
Tasks: Binary Classification, Drug Discovery

Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides’ functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.
iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities
Authors: Jing Xu, Fuyi Li, Chen Li, Xudong Guo, Cornelia Landersdorfer, Hsin-Hui Shen, Anton Y Peleg, Jian Li, Seiya Imoto, Jianhua Yao, Tatsuya Akutsu, Jiangning Song
Date: 2024-06-27 00:00:00
Proceeding: Briefings in Bioinformatics 2024 6
Tasks: Benchmarking

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens’ increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.
Quantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis
Authors: Riya Bansal, Nikhil Kumar Rajput
Date: 2025-04-10 00:00:00
Tasks: Quantum Machine Learning

Quantum Machine Learning (QML) is the intersection of two revolutionary fields: quantum computing and machine learning. It promises to unlock unparalleled capabilities in data analysis, model building, and problem-solving by harnessing the unique properties of quantum mechanics. This research endeavors to conduct a comprehensive bibliometric analysis of scientific information pertaining to QML covering the period from 2000 to 2023. An extensive dataset comprising 9493 scholarly works is meticulously examined to unveil notable trends, impact factors, and funding patterns within the domain. Additionally, the study employs bibliometric mapping techniques to visually illustrate the network relationships among key countries, institutions, authors, patent citations and significant keywords in QML research. The analysis reveals a consistent growth in publications over the examined period. The findings highlight the United States and China as prominent contributors, exhibiting substantial publication and citation metrics. Notably, the study concludes that QML, as a research subject, is currently in a formative stage, characterized by robust scholarly activity and ongoing development.
EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features
Authors: Jujuan Zhuang, Wanquan Gao, and Rui Su
Date: 2024-02-01 00:00:00
Proceeding: Journal of Bioinformatics and Computational Biology 2024 2

Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have been proposed to predict AMPs and achieved good results. In this work, we combine two kinds of word embedding features with the statistical features of peptide sequences to develop an ensemble classifier, named EnAMP, in which, two deep neural networks are trained based on Word2vec and Glove word embedding features of peptide sequences, respectively, meanwhile, we utilize statistical features of peptide sequences to train random forest and support vector machine classifiers. The average of four classifiers is the final prediction result. Compared with other state-of-the-art algorithms on six datasets, EnAMP outperforms most existing models with similar computational costs, even when compared with high computational cost algorithms based on Bidirectional Encoder Representation from Transformers (BERT), the performance of our model is comparable. EnAMP source code and the data are available at https://github.com/ruisue/EnAMP
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge
Authors: Riccardo Cantini, Alessio Orsino, Massimo Ruggiero, Domenico Talia
Date: 2025-04-10 00:00:00
Tasks: Adversarial Robustness, Benchmarking, Fairness, Machine Translation

Large Language Models (LLMs) have revolutionized artificial intelligence, driving advancements in machine translation, summarization, and conversational agents. However, their increasing integration into critical societal domains has raised concerns about embedded biases, which can perpetuate stereotypes and compromise fairness. These biases stem from various sources, including historical inequalities in training data, linguistic imbalances, and adversarial manipulation. Despite mitigation efforts, recent studies indicate that LLMs remain vulnerable to adversarial attacks designed to elicit biased responses. This work proposes a scalable benchmarking framework to evaluate LLM robustness against adversarial bias elicitation. Our methodology involves (i) systematically probing models with a multi-task approach targeting biases across various sociocultural dimensions, (ii) quantifying robustness through safety scores using an LLM-as-a-Judge approach for automated assessment of model responses, and (iii) employing jailbreak techniques to investigate vulnerabilities in safety mechanisms. Our analysis examines prevalent biases in both small and large state-of-the-art models and their impact on model safety. Additionally, we assess the safety of domain-specific models fine-tuned for critical fields, such as medicine. Finally, we release a curated dataset of bias-related prompts, CLEAR-Bias, to facilitate systematic vulnerability benchmarking. Our findings reveal critical trade-offs between model size and safety, aiding the development of fairer and more robust future language models.