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Fostering Human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and natural language processing 2

Fostering Human-AI interaction: development of a Clinical De...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Bergomi, Laura Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

Artificial Intelligence (AI) is increasingly integrated into Decision Support Systems (DSS). The explainability of AI-based systems becomes crucial in sensitive and critical domains, such as healthcare, where ethical considerations and reliability are paramount concerns. In the clinical setting, it is important to evaluate how humans and AI can collaborate on cognitive tasks. Collaboration protocols (HAI-CP) allow for the investigation of the usefulness of AI models and their impact on users (both positive and negative). Although research on the application of these methods is blooming, there is little understanding of the impact on clinical decision-making, especially for eXplainable AI (XAI) systems, due to the lack of user studies. Therefore, the goal of this proposal is to develop a clinical DSS enhanced by XAI and natural language processing (NLP): their synergy can add value to the interaction between users and AI, fostering a more linguistically natural, comprehensible, trustworthy, and supporting interfacing, that blends into the existing workflows. This proposal explores potential solutions to tailor natural language explanations and data visualizations to the end-user, improving the comprehensibility of the reasons behind a decision, and increasing the user’s confidence in the decision;investigates and tests possible strategies to "get the patient-in-the-loop";explores uncertainty quantification and counterfactual approaches, and finally assesses the impact on naturalistic (i.e., real-world) decision-making and long-term effects and biases. © 2024 Copyright for this paper by its authors.

关键词： Decision support systems

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Textgraphs 2022 Shared Task on natural language Premise Selection 16

TextGraphs 2022 Shared Task on Natural Language Premise Sele...

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16th workshop on graph-based methods for natural language processing, Textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Valentino, Marco Ferreira, Deborah Thayaparan, Mokanarangan Freitas, André Ustalov, Dmitry Idiap Research Institute Switzerland University of Manchester United Kingdom Toloka Serbia

The Shared Task on natural language Premise Selection (NLPS) asks participants to retrieve the set of premises that are most likely to be useful for proving a given mathematical statement from a supporting knowledge base. While previous editions of the Textgraphs shared tasks series targeted multi-hop inference for explanation regeneration in the context of science questions (Thayaparan et al., 2021;Jansen and Ustalov, 2020, 2019), NLPS aims to assess the ability of state-of-the-art approaches to operate on a mixture of natural and mathematical language and model complex multi-hop reasoning dependencies between statements. To this end, this edition of the shared task makes use of a large set of approximately 21k mathematical statements extracted from the PS-ProofWiki dataset (Ferreira and Freitas, 2020a). In this summary paper, we present the results of the 1st edition of the NLPS task, providing a description of the evaluation data, and the participating systems. Additionally, we perform a detailed analysis of the results, evaluating various aspects involved in mathematical language processing and multi-hop inference. The best-performing system achieved a MAP of 15.39, improving the performance of a TF-IDF baseline by approximately 3.0 MAP.1 © 2022 proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Large dataset

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Explaining Neural NLP Models for the Joint Analysis of Open- and Closed-Ended Survey Answers 2

Explaining Neural NLP Models for the Joint Analysis of Open-...

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2nd workshop on Trustworthy natural language processing, TrustNLP 2022

作者： Mosca, Edoardo Hermann, Katharina Eder, Tobias Groh, Georg TU Munich Department of Informatics Germany

ISBN: (纸本)9781955917780

Large-scale surveys are a widely used instrument to collect data from a target audience. Beyond the single individual, an appropriate analysis of the answers can reveal trends and patterns and thus generate new insights and knowledge for researchers. Current analysis practices employ shallow machine learning methods or rely on (biased) human judgment. This work investigates the usage of state-of-the-art NLP models such as BERT to automatically extract information from both open- and closed-ended questions. We also leverage explainability methods at different levels of granularity to further derive knowledge from the analysis model. Experiments on EMS-a survey-based study researching influencing factors affecting a student's career goals-show that the proposed approach can identify such factors both at the input- and higher concept-level. © 2022 Association for Computational Linguistics.

关键词： natural language processing systems

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Team ap-team at PAN: LLM Adapters for Various Datasets 25

Team ap-team at PAN: LLM Adapters for Various Datasets

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Boeva, Galina Gritsai, German Grabovoy, Andrey Advacheck OÜ Tallinn Estonia Grenoble France

The recent breakthrough in text generation ensures that the quality level of generation increases with each new model. On the other hand, the task associated with the use of generated text is relevant. Spreading false information, spamming, generating scientific articles and texts are all problems that have arisen from this outburst. Binary text classification methods have been proposed to control the situation. This research provides an approach based on aggregating QLoRA adapters which are trained for multiple distributions of generative model families. Our method LAVA (LLM Adapters for Various dAtasets) demonstrates comparable results with the primary baseline provided by the PAN organizers. The proposed method provides an efficient and fast detector with high performance of the target metrics, in view of the possibility of parallel training of adapters for the language models. It makes detecting process straightforward and flexible to tailor the adapter to appearing distributions and add it to an existing approach. Furthermore, each learns dependencies separately from the others, after which the outputs are aggregated. © 2024 Copyright for this paper by its authors.

关键词： natural language processing systems

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MG-BERT: Multi-graph Augmented BERT for Masked language Modeling 15

MG-BERT: Multi-Graph Augmented BERT for Masked Language Mode...

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15th workshop on graph-based methods for natural language processing, Textgraphs 2021

作者： Ghader, Parishad Behnam Zakerinia, Hossein Baghshah, Mahdieh Soleymani Sharif University of Technology Tehran Iran

ISBN: (纸本)9781954085381

Pre-trained models like Bidirectional Encoder Representations from Transformers (BERT), have recently made a big leap forward in natural language processing (NLP) tasks. However, there are still some shortcomings in the Masked language Modeling (MLM) task performed by these models. In this paper, we first introduce a multi-graph including different types of relations between words. Then, we propose Multi-graph augmented BERT (MG-BERT) model that is based on BERT. MG-BERT embeds tokens while taking advantage of a static multi-graph containing global word co-occurrences in the text corpus beside global real-world facts about words in knowledge graphs. The proposed model also employs a dynamic sentence graph to capture local context effectively. Experimental results demonstrate that our model can considerably enhance the performance in the MLM task. © 2021 Association for Computational Linguistics.

关键词： Knowledge graph

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Evaluating LLMs Capabilities at natural language to Logic Translation: A Preliminary Investigation 6

Evaluating LLMs Capabilities at Natural Language to Logic Tr...

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6th International workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis, OVERLAY 2024

作者： Brunello, Andrea Ferrarese, Riccardo Geatti, Luca Marzano, Enrico Montanari, Angelo Saccomanno, Nicola University of Udine Italy G.A.P. Srl Italy

Translating natural language (NL) into logical formalisms like First-Order Logic (FOL) has long been a challenge across multiple disciplines, including mathematics, computer science, and education. Traditional computational linguistics methods have struggled with this task due to the complexity and ambiguity of natural language. However, advancements in natural language processing (NLP), particularly the introduction of Large language Models (LLMs), have opened up new possibilities for tackling this challenge. Despite their potential, a systematic approach to evaluating the performance of LLMs in NL-to-FOL translation is still lacking. In this study, we take a first step towards filling in this gap. We examine a large dataset based on students’ efforts in formalizing natural language statements from the book "language, Proof, and Logic". based on this dataset, we propose a preliminary evaluation pipeline to assess LLM performance in NL-to-FOL translation tasks, considering both syntactic and semantic aspects. We then apply this pipeline to evaluate two recent LLMs, Meta’s Llama 3.1 (8B) and Google DeepMind’s Gemma 2 (9B). Our findings validate the proposed approach, revealing key similarities and differences between LLM-generated and student-produced formulas, and provide valuable insights into the current capabilities of LLMs in this domain. © 2024 Copyright for this paper by its authors.

关键词： Computer circuits

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AraLegal-BERT: A pretrained language model for Arabic Legal text 4

AraLegal-BERT: A pretrained language model for Arabic Legal ...

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4th natural Legal language processing workshop, NLLP 2022, co-located with the 2022 Conference on Empirical methods in natural language processing, EMNLP 2022

作者： AL-Qurishi, Muhammad AlQaseemi, Sarah Soussi, Riad Elm Company Research Department Riyadh12382 Saudi Arabia

ISBN: (纸本)9781959429180

The effectiveness of the bidirectional encoder representations from transformers (BERT) model for multiple linguistic tasks is well documented. However, its potential for a narrow and specific domain, such as legal, has not been fully explored. In this study, we examine the use of BERT in the Arabic legal domain and customize this language model for several downstream tasks using different domain-relevant training and test datasets to train BERT from scratch. We introduce AraLegal-BERT, a bidirectional encoder transformer-based model that has been thoroughly tested and carefully optimized with the goal of amplifying the impact of natural language processing-driven solutions on jurisprudence, legal documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it against three BERT variants for the Arabic language in three natural language understanding tasks. The results showed that the base version of AraLegal-BERT achieved better accuracy than the typical and original BERT model concerning legal texts. © 2022 Association for Computational Linguistics.

关键词： Signal encoding

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UNLPS at Textgraphs-16 natural language Premise Selection Task: Unsupervised natural language Premise Selection in Mathematical Text using Sentence-MPNet 16

UNLPS at TextGraphs-16 Natural Language Premise Selection Ta...

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16th workshop on graph-based methods for natural language processing, Textgraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Trust, Paul Minghim, Rosane Kadusabe, Provia Zahran, Ahmed Younis, Haseeb Millos, Evangelos University College Cork Ireland Worldquant University United States Dalhousie University Canada

This paper describes our system for the submission to the Textgraphs 2022 shared task at COLING 2022: natural language Premise Selection (NLPS) from mathematical texts. The task of NLPS regards selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae that are most likely to be used to achieve a particular mathematical proof. We formulated this solution as an unsupervised semantic similarity task by first obtaining contextualized embeddings of both the premises and mathematical proofs using sentence transformers. We then obtained the cosine similarity between these embeddings and then selected premises with the highest cosine scores as the most probable. Our system improves over the baseline system that uses bag of words models based on term frequency inverse document frequency in terms of mean average precision (MAP) by about 23.5% (0.1516 versus 0.1228). © 2022 proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Translating Ancient Chinese to Modern Chinese at Scale: A Large language Model-based Approach 1

Translating Ancient Chinese to Modern Chinese at Scale: A La...

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1st workshop on Ancient language Translation, ALT 2023, co-located with 19th Machine Translation Summit, MTS 2023

作者： Cao, Jiahuan Peng, Dezhi Shi, Yongxin Jiang, Zongyuan Jin, Lianwen School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

Recently, the emergence of large language models (LLMs) has provided powerful foundation models for a wide range of natural language processing (NLP) tasks. However, the vast majority of the pre-training corpus for most existing LLMs is in English, resulting in their Chinese proficiency falling far behind that of English. Furthermore, ancient Chinese has a much larger vocabulary and less available corpus than modern Chinese, which significantly challenges the generalization capacity of existing LLMs. In this paper, we investigate the Ancient-Chinese-to-Modern-Chinese (A2M) translation using LLMs including LLaMA and Ziya. Specifically, to improve the understanding of Chinese texts, we explore the vocabulary extension and incremental pre-training methods based on existing pre-trained LLMs. Subsequently, a large-scale A2M translation dataset with 4M pairs is utilized to fine-tune the LLMs. Experimental results demonstrate the effectiveness of the proposed method, especially with Ziya-13B, in translating ancient Chinese to modern Chinese. Moreover, we deeply analyze the performance of various LLMs with different strategies, which we believe can benefit further research on LLM-based A2M approaches. © 2023 The Authors.

关键词： Large datasets

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HIP 2023 - proceedings of the 2023 International workshop on Historical Document Imaging and processing

HIP 2023 - Proceedings of the 2023 International Workshop on...

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7th International workshop on Historical Document Imaging and processing, HIP 2023, held in conjunction with ICDAR 2023

ISBN: (纸本)9798400708411

The proceedings contain 18 papers. The topics discussed include: handwritten text recognition from crowdsourced annotations;an evaluation of handwritten text recognition methods for historical ciphered manuscripts;drawing the line: a dual evaluation approach for shaping ground truth in image retrieval using rich visual embeddings of historical images;gauging the limitations of natural language supervised text-image metrics learning by Iconclass visual concepts;two-step sequence transformer based method for cham to Latin script transliteration;homer restored: virtual reconstruction of Papyrus Bodmer 1;study of historical Byzantine seal images: the BHAI Project for computer-based sigillography;classifying the scripts of Aramaic incantation bowls;Laypa: a novel framework for applying segmentation networks to historical documents;and document layout analysis with deep learning and heuristics.

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