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检索条件"任意字段=Conference on empirical methods in natural language processing"

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Inductive Learning of Logical Theories with LLMs: An Expressivity-graded Analysis 39

Inductive Learning of Logical Theories with LLMs: An Express...

引用

39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： de Souza, João Pedro Gandarela Carvalho, Danilo Freitas, André Idiap Research Institute Switzerland National Biomarker Centre CRUK-MI University of Manchester United Kingdom Department of Computer Science University of Manchester United Kingdom

ISBN: (纸本)157735897X

This work presents a novel systematic methodology to analyse the capabilities and limitations of Large language Models (LLMs) with feedback from a formal inference engine, on logic theory induction. The analysis is complexity-graded w.r.t. rule dependency structure, allowing quantification of specific inference challenges on LLM performance. Integrating LLMs with formal methods is a promising frontier in the natural language processing field, as an important avenue for improving model inference control and explainability. In particular, inductive learning over complex sets of facts and rules, poses unique challenges for current autoregressive models, as they lack explicit symbolic grounding. While they can be complemented by formal systems, the properties delivered by LLMs regarding inductive learning, are not well understood and quantified. empirical results indicate that the largest LLMs can achieve competitive results against a SOTA Inductive Logic Programming (ILP) system baseline, but also that tracking long predicate relationship chains is a more difficult obstacle than theory complexity for LLMs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： natural language processing systems

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VisualHow: Multimodal Problem Solving

VisualHow: Multimodal Problem Solving

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Jinhui Chen, Xianyu Jiang, Ming Chen, Shi Wang, Louis Zhao, Qi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent progress in the interdisciplinary studies of computer vision (CV) and natural language processing (NLP) has enabled the development of intelligent systems that can describe what they see and answer questions accordingly. However, despite showing usefulness in performing these vision-language tasks, existing methods still struggle in understanding real-life problems (i.e., how to do something) and suggesting step-by-step guidance to solve them. With an overarching goal of developing intelligent systems to assist humans in various daily activities, we propose VisualHow, a free form and open-ended research that focuses on understanding a real-life problem and deriving its solution by incorporating key components across multiple modalities. We develop a new dataset with 20,028 real-life problems and 102,933 steps that constitute their solutions, where each step consists of both a visual illustration and a textual description that guide the problem solving. To establish better understanding of problems and solutions, we also provide annotations of multimodal attention that localizes important components across modalities and solution graphs that encapsulate different steps in structured representations. These data and annotations enable a family of new vision-language tasks that solve real-life problems. Through extensive experiments with representative models, we demonstrate their effectiveness on training and testing models for the new tasks, and there is significant scope for improvement by learning effective attention mechanisms.

关键词： Training Computer vision Visualization Technological innovation Annotations natural language processing Pattern recognition

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Predictive Modeling for Early Alzheimer's Disease Using natural language processing

Predictive Modeling for Early Alzheimer's Disease Using Natu...

引用

2024 IEEE International conference on Big Data, BigData 2024

作者： Emekci, Azra Pioneer High School San JoseCA United States

ISBN: (纸本)9798350362480

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects millions worldwide, leading to cognitive decline and placing a substantial burden on families and healthcare systems. Early detection is crucial for managing the disease and improving patient outcomes, yet current diagnostic methods are often invasive, expensive, or insufficiently sensitive in the early stages. Utilizing data from the DementiaBank Pitt Corpus, we developed a machine learning model capable of differentiating between individuals with AD and healthy controls based on their text. The Multilayer Perceptron (MLP) model achieved an accuracy of 86.93%, with a precision of 0.87 for detecting AD cases and 0.84 for identifying non-AD cases. The recall rates were 0.83 for AD and 0.88 for non-AD, indicating the model's robustness in correctly identifying both conditions. These results highlight the potential of NLP-based models as effective diagnostic tools, offering a non-invasive and cost-effective alternative to traditional methods. The implementation of such technology could revolutionize the early detection of AD, enabling timely interventions that may slow disease progression and improve the quality of life for patients. By integrating this model into clinical settings or remote health applications, we can enhance accessibility to diagnostic services, particularly in under served communities. © 2024 IEEE.

关键词： Diagnosis

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Sequential Alignment methods for Ensemble Part-of-Speech Tagging

Sequential Alignment Methods for Ensemble Part-of-Speech Tag...

引用

IEEE International conference on Big Data and Smart Computing (BigComp)

作者： Jung, Jeesu Jung, Sangkeun Roh, Yoon-hyung Chungnam Natl Univ Comp Sci Engn Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781665421973

Part-of-speech tagging is a fundamental task that provides the elemental structure and content information for additional natural language processing. Although Part-of-speech tagging problems have traditionally been formulated as sequential labeling tasks, none of the proposed ensemble approaches have focused on sequence alignment during post-processing. Herein, we present a weighted ensemble technique using a sequence alignment approach for a Part-of-speech tagger. Through this technique, we introduce a simple but powerful post-processor, which is a sub-sequence selector using a similarity score calculated through sequence alignment methods. Such methods are based on an existing DNA alignment approach applied toward natural language. Experiments were conducted using an ensemble of sequence alignment methods with three different sub-sequence units, i.e., the sequence, word, and character span. Experiments on English and Korean datasets show that our sequence alignment ensemble technique outperforms a basic hard voting method. Most of the results of the ensemble sequence alignment approach with various sub-sequence units showed an increase in F1-score over hard voting. F1-score increased up to 0.36 for the general hard voting method on the test dataset.

关键词： sequence alignment ensemble part-of-speech tagger

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Enhanced Graph-Based Model with Mathematical Knowledge Embedding for Math Word Problem Solving

Enhanced Graph-Based Model with Mathematical Knowledge Embed...

引用

Intelligent Education and Intelligent Research (IEIR), International conference on

作者： Xin He Chao Sun Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China

ISBN: (数字)9798331519827

ISBN: (纸本)9798331519834

Math Word Problems (MWPs) represent a critical area of research in natural language processing and artificial intelligence, with the goal of translating natural language descriptions into mathematical expressions and deriving solutions. Despite recent progress with graph-based and tree-based neural models, many approaches still struggle to capture the implicit mathematical knowledge embedded in these problems. This limitation impedes their ability to accurately represent the problem-solving process, especially for complex MWPs that require an understanding of implicit mathematical relationships. To tackle this challenge, we propose integrating mathematical knowledge directly into graph neural network models to enhance their effectiveness in solving intricate MWPs. Specifically, we introduce a formula graph where each formula is abstracted into a graph structure and integrated into an enhanced model, thereby facilitating efficient resolution of MWPs involving implicit knowledge. Our approach aims to improve model performance on complex MWPs, as demonstrated through empirical evaluation on the Ape210K dataset.

关键词： Knowledge engineering Accuracy Translation Large language models Mathematical models Graph neural networks natural language processing Libraries Problem-solving Physics

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Less Hallucination and More Factuality: Human Values Alignment in Text Summarization 39th

Less Hallucination and More Factuality: Human Values Alignme...

引用

39th CCF National conference on Computer Applications

作者： Qiu, Zihan Xu, Yumei Beijing Foreign Studies Univ Sch Informat Sci & Technol Beijing 100089 Peoples R China

ISBN: (纸本)9789819796700;9789819796717

Text summarization task aims to condense lengthy documents into shorter texts while preserving the essential information. However, related work mostly focused on preserving semantic information of documents and ignored the human values alignment between documents and summaries. What's more, summaries generated by LLMs are often found to contain hallucinations and lack factuality. In this paper, we introduce a two-stage extract-generate summarization framework to address the above issues. In the first stage, sentences containing both semantic and human values are extracted through a diffusion model. Then in the second stage, overall summaries are generated via LLMs. By extracting important sentences before summarization, we can shift LLMs' attention to preserve more human values information from the documents, hence achieving the goal of human values alignment. Specifically, we follow Schwartz's Theory of Human Values for human values definition due to its wide adoption as a value framework in the realm of NLP(natural language processing). To verify the performance of our framework, we designed several LLM-only methods including in-context methods as baselines to compare with. Experiments conducted on CNN/DM and Reddit summarization datasets demonstrate improvements on both human values alignment and factuality achieved by our framework, indicating the importance of an extractor before leveraging LLM for summarization.

关键词： Human Values Alignment Text Summarization Large language Model

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Topic and Sentiment Analysis of Reddit's Vaping-Cessation Community

Topic and Sentiment Analysis of Reddit's Vaping-Cessation Co...

引用

2023 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Choi, Joongeun Jang, Hyeju Wincheser High School Winchester United States Indiana University-Purdue University Indianapolis Department of Computer Science Indianapolis United States

ISBN: (纸本)9798350337488

To alleviate the teen vaping crisis, this study analyzed the 'QuitVaping' subreddit. We identified posts sharing experience and/or advice using DistilBERT and investigated distinctive features in the data with topic modeling (BERTopic) and sentiment analysis (VADER) based on upvotes. We extracted 23 distinct vape-related symptoms and 17 suggested withdrawal methods, along with insightful topics. Highly upvoted posts often contained hopeful advice with positive sentiments. Our results help understand individual experiences, giving valuable insights to reduce teen vaping. © 2023 IEEE.

关键词： natural language processing sentiment analysis social media topic analysis vaping

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Unelicitable Backdoors in language Models via Cryptographic Transformer Circuits 38

Unelicitable Backdoors in Language Models via Cryptographic ...

引用

38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Draguns, Andis Gritsevskiy, Andrew Motwani, Sumeet Ramesh de Witt, Christian Schroeder Contramont Research Switzerland IMCS UL Cavendish Labs United Kingdom University of California Berkeley United States University of Oxford United Kingdom

The rapid proliferation of open-source language models significantly increases the risks of downstream backdoor attacks. These backdoors can introduce dangerous behaviours during model deployment and can evade detection by conventional cybersecurity monitoring systems. In this paper, we introduce a novel class of backdoors in transformer models, that, in contrast to prior art, are unelicitable in nature. Unelicitability prevents the defender from triggering the backdoor, making it impossible to properly evaluate ahead of deployment even if given full white-box access and using automated techniques, such as red-teaming or certain formal verification methods. We show that our novel construction is not only unelicitable thanks to using cryptographic techniques, but also has favourable robustness properties. We confirm these properties in empirical investigations, and provide evidence that our backdoors can withstand state-of-the-art mitigation strategies. Additionally, we expand on previous work by showing that our universal backdoors, while not completely undetectable in white-box settings, can be harder to detect than some existing designs. By demonstrating the feasibility of seamlessly integrating backdoors into transformer models, this paper fundamentally questions the efficacy of pre-deployment detection strategies. This offers new insights into the offence-defence balance in AI safety and security. © 2024 Neural information processing systems foundation. All rights reserved.

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Diffusion Models are Zero-Shot Generative Text-Vision Retrievers

Diffusion Models are Zero-Shot Generative Text-Vision Retrie...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Bao Xie, Zeke Zhang, Xiaomei Zhu, Xiangyu Lei, Zhen MAIS Institute of Automation Chinese Academy of Sciences Beijing China School of Artifical Intelligence University of Chinese Academy of Sciences Beijing China Guangzhou China CAIR HKISI Chinese Academy of Sciences Hong Kong

ISBN: (纸本)9798350368741

Large-scale text-to-image diffusion models have demonstrated impressive capabilities for downstream tasks by leveraging strong vision-language alignment from generative pre-training. Recently, a number of works have explored how to use the power of text-to-image diffusion models for text-image matching tasks. While previous generative text-image matching methods have shown potential in retrieving the most challenging candidates, they still suffer from extremely slow retrieval speeds and lack the ability to handle temporal dimensions, making them impractical for text-video retrieval. In this paper, we propose ZSGenRet, a simple yet effective zero-shot generative text-vision retrieval framework for both text-image and text-video tasks, based on pre-trained text-to-image diffusion models. We further incorporate inversion saliency detection to identify key frames in videos and enhance the semantic representation of the vision encoder. Experimental results demonstrate that ZSGenRet significantly improves text-video retrieval performance and achieves competitive results on text-image retrieval while remarkably improving efficiency. To the best of our knowledge, the proposed ZSGenRet is the first to explore zero-shot text-video retrieval based on diffusion models. © 2025 IEEE.

关键词： diffusion models Text-vision retrieval vision-language models zero-shot learning

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An Analogy based Approach for Solving Target Sense Verification 6

An Analogy based Approach for Solving Target Sense Verificat...

引用

6th International conference on natural language processing and Information Retrieval, NLPIR 2022

作者： Zervakis, Georgios Vincent, Emmanuel Couceiro, Miguel Schoenauer, Marc Marquer, Esteban Université de Lorraine Cnrs Inria Loria Nancy France Inria Tau Lisn Cnrs Univ. Paris-Saclay France

ISBN: (纸本)9781450397629

Contextualized language models have emerged as a de facto standard in natural language processing due to the vast amount of knowledge they acquire during pretraining. Nonetheless, their ability to solve tasks that require reasoning over this knowledge is limited. Certain tasks can be improved by analogical reasoning over concepts, e.g., understanding the underlying relations in "Man is to Woman as King is to Queen". In this work, we propose a way to formulate target sense verification as an analogy detection task, by transforming the input data into quadruples. We present AB4TSV (Analogy and BERT for TSV), a model that uses BERT to represent the objects in these quadruples combined with a convolutional neural network to decide whether they constitute valid analogies. We test our system on the WiC-TSV evaluation benchmark, and show that it can outperform existing approaches. Our empirical study shows the importance of the input encoding for BERT. This dependence gets alleviated by integrating the axiomatic properties of analogies during training, while preserving performance and improving interpretability. © 2022 ACM.

关键词： natural language processing systems

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