pwc-archive/papers-with-abstracts huggingface dataset:
Entailed Between the Lines: Incorporating Implication into NLI
Authors: Shreya Havaldar, Hamidreza Alvari, John Palowitch, Mohammad Javad Hosseini, Senaka Buthpitiya, Alex Fabrikant
Date: 2025-01-13 00:00:00
Tasks: Natural Language Inference

Much of human communication depends on implication, conveying meaning beyond literal words to express a wider range of thoughts, intentions, and feelings. For models to better understand and facilitate human communication, they must be responsive to the text's implicit meaning. We focus on Natural Language Inference (NLI), a core tool for many language tasks, and find that state-of-the-art NLI models and datasets struggle to recognize a range of cases where entailment is implied, rather than explicit from the text. We formalize implied entailment as an extension of the NLI task and introduce the Implied NLI dataset (INLI) to help today's LLMs both recognize a broader variety of implied entailments and to distinguish between implicit and explicit entailment. We show how LLMs fine-tuned on INLI understand implied entailment and can generalize this understanding across datasets and domains.
Benchmarking Abstractive Summarisation: A Dataset of Human-authored Summaries of Norwegian News Articles
Authors: Samia Touileb, Vladislav Mikhailov, Marie Kroka, Lilja Øvrelid, Erik Velldal
Date: 2025-01-13 00:00:00
Tasks: Articles, Benchmarking

We introduce a dataset of high-quality human-authored summaries of news articles in Norwegian. The dataset is intended for benchmarking the abstractive summarisation capabilities of generative language models. Each document in the dataset is provided with three different candidate gold-standard summaries written by native Norwegian speakers, and all summaries are provided in both of the written variants of Norwegian -- Bokm{\aa}l and Nynorsk. The paper describes details on the data creation effort as well as an evaluation of existing open LLMs for Norwegian on the dataset. We also provide insights from a manual human evaluation, comparing human-authored to model-generated summaries. Our results indicate that the dataset provides a challenging LLM benchmark for Norwegian summarisation capabilities
Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning
Authors: Karishma Thakrar, Nick Young
Date: 2025-01-13 00:00:00
Tasks: Chunking, RAG, Retrieval-augmented Generation

This paper explores the application of large language models (LLMs) to extract nuanced and complex job features from unstructured job postings. Using a dataset of 1.2 million job postings provided by AdeptID, we developed a robust pipeline to identify and classify variables such as remote work availability, remuneration structures, educational requirements, and work experience preferences. Our methodology combines semantic chunking, retrieval-augmented generation (RAG), and fine-tuning DistilBERT models to overcome the limitations of traditional parsing tools. By leveraging these techniques, we achieved significant improvements in identifying variables often mislabeled or overlooked, such as non-salary-based compensation and inferred remote work categories. We present a comprehensive evaluation of our fine-tuned models and analyze their strengths, limitations, and potential for scaling. This work highlights the promise of LLMs in labor market analytics, providing a foundation for more accurate and actionable insights into job data.
GPT as a Monte Carlo Language Tree: A Probabilistic Perspective
Authors: Kun-Peng Ning, Jia-Yu Yao, Yu-Yang Liu, Mu-Nan Ning, Li Yuan
Date: 2025-01-13 00:00:00
Tasks: Hallucination

Large Language Models (LLMs), such as GPT, are considered to learn the latent distributions within large-scale web-crawl datasets and accomplish natural language processing (NLP) tasks by predicting the next token. However, this mechanism of latent distribution modeling lacks quantitative understanding and analysis. In this paper, we propose a novel perspective that any language dataset can be represented by a Monte Carlo Language Tree (abbreviated as ``Data-Tree''), where each node denotes a token, each edge denotes a token transition probability, and each sequence has a unique path. Any GPT-like language model can also be flattened into another Monte Carlo Language Tree (abbreviated as ``GPT-Tree''). Our experiments show that different GPT models trained on the same dataset exhibit significant structural similarity in GPT-Tree visualization, and larger models converge more closely to the Data-Tree. More than 87\% GPT output tokens can be recalled by Data-Tree. These findings may confirm that the reasoning process of LLMs is more likely to be probabilistic pattern-matching rather than formal reasoning, as each model inference seems to find a context pattern with maximum probability from the Data-Tree. Furthermore, we provide deeper insights into issues such as hallucination, Chain-of-Thought (CoT) reasoning, and token bias in LLMs.
Many-Objective Neuroevolution for Testing Games
Authors: Patric Feldmeier, Katrin Schmelz, Gordon Fraser
Date: 2025-01-14 00:00:00
Tasks: software testing

Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenges by combining search-based software testing principles with neuroevolution to optimise neural networks that serve as test cases. However, since NEATEST is designed as a single-objective algorithm, it may require a long time to cover fairly simple program states or may even get stuck trying to reach unreachable program states. In order to resolve these shortcomings of NEATEST, this work aims to transform the algorithm into a many-objective search algorithm that targets several program states simultaneously. To this end, we combine the neuroevolution algorithm NEATEST with the two established search-based software testing algorithms, MIO and MOSA. Moreover, we adapt the existing many-objective neuroevolution algorithm NEWS/D to serve as a test generator. Our experiments on a dataset of 20 SCRATCH programs show that extending NEATEST to target several objectives simultaneously increases the average branch coverage from 75.88% to 81.33% while reducing the required search time by 93.28%.
Spiking Neural Network Accelerator Architecture for Differential-Time Representation using Learned Encoding
Authors: Daniel Windhager, Lothar Ratschbacher, Bernhard A. Moser, Michael Lunglmayr
Date: 2025-01-14 00:00:00

Spiking Neural Networks (SNNs) have garnered attention over recent years due to their increased energy efficiency and advantages in terms of operational complexity compared to traditional Artificial Neural Networks (ANNs). Two important questions when implementing SNNs are how to best encode existing data into spike trains and how to efficiently process these spike trains in hardware. This paper addresses both of these problems by incorporating the encoding into the learning process, thus allowing the network to learn the spike encoding alongside the weights. Furthermore, this paper proposes a hardware architecture based on a recently introduced differential-time representation for spike trains allowing decoupling of spike time and processing time. Together these contributions lead to a feedforward SNN using only Leaky-Integrate and Fire (LIF) neurons that surpasses 99% accuracy on the MNIST dataset while still being implementable on medium-sized FPGAs with inference times of less than 295us.
A Computational Model of Learning and Memory Using Structurally Dynamic Cellular Automata
Authors: Jeet Singh
Date: 2024-12-20 00:00:00
Tasks: Computational Efficiency

In the fields of computation and neuroscience, much is still unknown about the underlying computations that enable key cognitive functions including learning, memory, abstraction and behavior. This paper proposes a mathematical and computational model of learning and memory based on a small set of bio-plausible functions that include coincidence detection, signal modulation, and reward/penalty mechanisms. Our theoretical approach proposes that these basic functions are sufficient to establish and modulate an information space over which computation can be carried out, generating signal gradients usable for inference and behavior. The computational method used to test this is a structurally dynamic cellular automaton with continuous-valued cell states and a series of recursive steps propagating over an undirected graph with the memory function embedded entirely in the creation and modulation of graph edges. The experimental results show: that the toy model can make near-optimal choices to re-discover a reward state after a single training run; that it can avoid complex penalty configurations; that signal modulation and network plasticity can generate exploratory behaviors in sparse reward environments; that the model generates context-dependent memory representations; and that it exhibits high computational efficiency because of its minimal, single-pass training requirements combined with flexible and contextual memory representation.
Black-box Optimization with Simultaneous Statistical Inference for Optimal Performance
Authors: Teng Lian, Jian-Qiang Hu, Yuhang Wu, Zeyu Zheng
Date: 2025-01-14 00:00:00
Tasks: Computational Efficiency, Decision Making, Management

Black-box optimization is often encountered for decision-making in complex systems management, where the knowledge of system is limited. Under these circumstances, it is essential to balance the utilization of new information with computational efficiency. In practice, decision-makers often face the dual tasks of optimization and statistical inference for the optimal performance, in order to achieve it with a high reliability. Our goal is to address the dual tasks in an online fashion. Wu et al (2022) [arXiv preprint: 2210.06737] point out that the sample average of performance estimates generated by the optimization algorithm needs not to admit a central limit theorem. We propose an algorithm that not only tackles this issue, but also provides an online consistent estimator for the variance of the performance. Furthermore, we characterize the convergence rate of the coverage probabilities of the asymptotic confidence intervals.
ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments
Authors: Wenxuan Guo, Jungho Lee, Panos Toulis
Date: 2025-01-13 00:00:00
Tasks: Causal Inference, Decision Making

Experimentation is widely utilized for causal inference and data-driven decision-making across disciplines. In an A/B experiment, for example, an online business randomizes two different treatments (e.g., website designs) to their customers and then aims to infer which treatment is better. In this paper, we construct randomization tests for complex treatment effects, including heterogeneity and interference. A key feature of our approach is the use of flexible machine learning (ML) models, where the test statistic is defined as the difference between the cross-validation errors from two ML models, one including the treatment variable and the other without it. This approach combines the predictive power of modern ML tools with the finite-sample validity of randomization procedures, enabling a robust and efficient way to detect complex treatment effects in experimental settings. We demonstrate this combined benefit both theoretically and empirically through applied examples.
Fast sampling and model selection for Bayesian mixture models
Authors: M. E. J. Newman
Date: 2025-01-13 00:00:00
Tasks: Model Selection

We describe two Monte Carlo algorithms for sampling from the integrated posterior distributions of a range of Bayesian mixture models. Both algorithms allow us to directly sample not only the assignment of observations to components but also the number of components, thereby fitting the model and performing model selection over the number of components in a single computation. The first algorithm is a traditional collapsed Gibbs sampler, albeit with an unusual move-set; the second builds on the first, adding rejection-free sampling from the prior over component assignments, to create an algorithm that has excellent mixing time in typical applications and outperforms current state-of-the-art methods, in some cases by a wide margin. We demonstrate our methods with a selection of applications to latent class analysis.
TriMod Fusion for Multimodal Named Entity Recognition in Social Media
Authors: Mosab Alfaqeeh
Date: 2025-01-14 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Social media platforms serve as invaluable sources of user-generated content, offering insights into various aspects of human behavior. Named Entity Recognition (NER) plays a crucial role in analyzing such content by identifying and categorizing named entities into predefined classes. However, traditional NER models often struggle with the informal, contextually sparse, and ambiguous nature of social media language. To address these challenges, recent research has focused on multimodal approaches that leverage both textual and visual cues for enhanced entity recognition. Despite advances, existing methods face limitations in capturing nuanced mappings between visual objects and textual entities and addressing distributional disparities between modalities. In this paper, we propose a novel approach that integrates textual, visual, and hashtag features (TriMod), utilizing Transformer-attention for effective modality fusion. The improvements exhibited by our model suggest that named entities can greatly benefit from the auxiliary context provided by multiple modalities, enabling more accurate recognition. Through the experiments on a multimodal social media dataset, we demonstrate the superiority of our approach over existing state-of-the-art methods, achieving significant improvements in precision, recall, and F1 score.
Unsupervised Query Routing for Retrieval Augmented Generation
Authors: Feiteng Mu, Liwen Zhang, Yong Jiang, Wenjie Li, Zhen Zhang, Pengjun Xie, Fei Huang
Date: 2025-01-14 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation

Query routing for retrieval-augmented generation aims to assign an input query to the most suitable search engine. Existing works rely heavily on supervised datasets that require extensive manual annotation, resulting in high costs and limited scalability, as well as poor generalization to out-of-distribution scenarios. To address these challenges, we introduce a novel unsupervised method that constructs the "upper-bound" response to evaluate the quality of retrieval-augmented responses. This evaluation enables the decision of the most suitable search engine for a given query. By eliminating manual annotations, our approach can automatically process large-scale real user queries and create training data. We conduct extensive experiments across five datasets, demonstrating that our method significantly enhances scalability and generalization capabilities.
Constructing Set-Compositional and Negated Representations for First-Stage Ranking
Authors: Antonios Minas Krasakis, Andrew Yates, Evangelos Kanoulas
Date: 2025-01-13 00:00:00
Tasks: Attribute, Negation

Set compositional and negated queries are crucial for expressing complex information needs and enable the discovery of niche items like Books about non-European monarchs. Despite the recent advances in LLMs, first-stage ranking remains challenging due to the requirement of encoding documents and queries independently from each other. This limitation calls for constructing compositional query representations that encapsulate logical operations or negations, and can be used to match relevant documents effectively. In the first part of this work, we explore constructing such representations in a zero-shot setting using vector operations between lexically grounded Learned Sparse Retrieval (LSR) representations. Specifically, we introduce Disentangled Negation that penalizes only the negated parts of a query, and a Combined Pseudo-Term approach that enhances LSRs ability to handle intersections. We find that our zero-shot approach is competitive and often outperforms retrievers fine-tuned on compositional data, highlighting certain limitations of LSR and Dense Retrievers. Finally, we address some of these limitations and improve LSRs representation power for negation, by allowing them to attribute negative term scores and effectively penalize documents containing the negated terms.
Large Language Models: New Opportunities for Access to Science
Authors: Jutta Schnabel
Date: 2025-01-13 00:00:00
Tasks: Information Retrieval, Retrieval, Retrieval-augmented Generation

The adaptation of Large Language Models like ChatGPT for information retrieval from scientific data, software and publications is offering new opportunities to simplify access to and understanding of science for persons from all levels of expertise. They can become tools to both enhance the usability of the open science environment we are building as well as help to provide systematic insight to a long-built corpus of scientific publications. The uptake of Retrieval Augmented Generation-enhanced chat applications in the construction of the open science environment of the KM3NeT neutrino detectors serves as a focus point to explore and exemplify prospects for the wider application of Large Language Models for our science.
Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration
Authors: Zheqi Lv, Keming Ye, Zishu Wei, Qi Tian, Shengyu Zhang, Wenqiao Zhang, Wenjie Wang, Kun Kuang, Tat-Seng Chua, Fei Wu
Date: 2025-01-10 00:00:00
Tasks: Recommendation Systems

Deep neural networks have become foundational to advancements in multiple domains, including recommendation systems, natural language processing, and so on. Despite their successes, these models often contain incompatible parameters that can be underutilized or detrimental to model performance, particularly when faced with specific, varying data distributions. Existing research excels in removing such parameters or merging the outputs of multiple different pretrained models. However, the former focuses on efficiency rather than performance, while the latter requires several times more computing and storage resources to support inference. In this paper, we set the goal to explicitly improve these incompatible parameters by leveraging the complementary strengths of different models, thereby directly enhancing the models without any additional parameters. Specifically, we propose Compatibility-aware Knowledge Integration (CKI), which consists of Parameter Compatibility Assessment and Parameter Splicing, which are used to evaluate the knowledge content of multiple models and integrate the knowledge into one model, respectively. The integrated model can be used directly for inference or for further fine-tuning. We conduct extensive experiments on various datasets for recommendation and language tasks, and the results show that Compatibility-aware Knowledge Integration can effectively optimize incompatible parameters under multiple tasks and settings to break through the training limit of the original model without increasing the inference cost.
Analyzing the Role of the DSO in Electricity Trading of VPPs via a Stackelberg Game Model
Authors: Peng Wang, Xi Zhang, Luis Badesa
Date: 2025-01-13 00:00:00
Tasks: Bilevel Optimization

The increasing penetration of distributed energy resources has sparked interests in participating in power markets. Here, we consider two settings where Virtual Power Plants (VPPs) with some flexible resources participate in the electricity trading, either directly in the wholesale electricity market, or interfaced by the Distribution System Operator (DSO) who is the transaction organizer. In order to study the role of DSO as a stakeholder, a Stackelberg game is represented via a bi-level model: the DSO maximizes profits at the upper level, while the VPPs minimize operating costs at the lower level. To solve this problem, the Karush-Kuhn-Tucker conditions of lower level is deduced to achieve a single-level problem. The results show that the role of the DSO as an intermediary agent leads to a decrease in operating costs of the VPPs by organizing lower-level trading, while making a profit for itself. However, this result comes with interests loss of the wholesale market, implying that stakeholders in the market need to abide by regulatory constraints.
A mechanistic insight into sources of error of visual working memory in multiple sclerosis
Authors: Ali Motahharynia, Ahmad Pourmohammadi, Armin Adibi, Vahid Shaygannejad, Fereshteh Ashtari, Iman Adibi, Mehdi Sanayei
Date: 2023-11-08 00:00:00
Proceeding: eLife 2023 11

Working memory (WM) is one of the most affected cognitive domains in multiple sclerosis (MS), which is mainly studied by the previously established binary model for information storage (slot model). However, recent observations based on the continuous reproduction paradigms have shown that assuming dynamic allocation of WM resources (resource model) instead of the binary hypothesis will give more accurate predictions in WM assessment. Moreover, continuous reproduction paradigms allow for assessing the distribution of error in recalling information, providing new insights into the organization of the WM system. Hence, by utilizing two continuous reproduction paradigms, memory-guided localization (MGL) and analog recall task with sequential presentation, we investigated WM dysfunction in MS. Our results demonstrated an overall increase in recall error and decreased recall precision in MS. While sequential paradigms were better in distinguishing healthy control from relapsing-remitting MS, MGL were more accurate in discriminating MS subtypes (relapsing-remitting from secondary progressive), providing evidence about the underlying mechanisms of WM deficit in progressive states of the disease. Furthermore, computational modeling of the results from the sequential paradigm determined that imprecision in decoding information and swap error (mistakenly reporting the feature of other presented items) was responsible for WM dysfunction in MS. Overall, this study offered a sensitive measure for assessing WM deficit and provided new insight into the organization of the WM system in MS population.
Data and models for sunspots detection in solar images captured with smart telescopes
Authors: Olivier Parisot
Date: 2024-10-21 00:00:00
Proceeding: International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability (ARTIIS) 2024 10
Tasks: Astronomy, Deep Learning

Observing the sun with Electronically Assisted Astronomy allows understanding of solar phenomena, contributes to scientific research, promotes science education, and provides recreational enjoyment, all while requiring strict adherence to safety measures. In this paper, we present an annotated dataset of solar images captured with smart telescopes, and we show how this dataset allows to train Deep Learning YOLOv7 models for the detection of sunspots. Both data and Deep Learning model can be used by the general public to observe sun with contextual information.