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检索条件"任意字段=1st Learning Language in Logic workshop, LLL 1999"

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EXPLIMED 2024 - Proceedings of the 1st workshop on Explainable Artificial Intelligence for the Medical Domain, co-located with 27th European Conference on Artificial Intelligence, ECAI 2024

EXPLIMED 2024 - Proceedings of the 1st Workshop on Explainab...

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1st workshop on Explainable Artificial Intelligence for the Medical Domain, EXPLIMED 2024

The proceedings contain 19 papers. The topics discussed include: integrating graph neural networks and fuzzy logic to enhance deep learning interpretability;ProtoAL: interpretable deep active learning with prototypes for medical imaging;latent diffusion models for privacy-preserving medical case-based explanations;explaining Bayesian networks in natural language using factor arguments. evaluation in the medical domain;vise: validated and invalidated symbolic explanations for knowledge graph integrity;prediction of continuous targets by explainable imbalanced regression from omics data in childhood obesity;explainable skin lesion classification with multitask learning;an explainable convolutional neural network for the detection of drug abuse;and towards explainable federated learning in healthcare: a focus on heart arrhythmia detection.

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Word Sense Disambiguation as a Game of Neurosymbolic Darts 1

Word Sense Disambiguation as a Game of Neurosymbolic Darts

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1st workshop on Bridging Neurons and Symbols for Natural language Processing and Knowledge Graphs Reasoning, NeusymBridge 2024

作者： Dong, Tiansi Sifa, Rafet Media Engineering Fraunhofer IAIS Schloss Birlinghoven 1 Sankt Augustin53757 Germany

ISBN: (纸本)9782493814128

Word Sense Disambiguation (WSD) is one of the hardest tasks in natural language understanding and knowledge engineering. The glass ceiling of the 80% F1 score is recently achieved through supervised learning, enriched by knowledge graphs. Here, we propose a novel neurosymbolic methodology that may push the F1 score above 90%. The core of our methodology is a neurosymbolic sense embedding, in terms of a configuration of nested n-dimensional balls. The central point of a ball well preserves pre-trained word embeddings learned from data, which partially fixes the locations of balls. Inclusion relations among balls precisely encode symbolic hypernym relations among senses, and enable simple logic deduction among sense embeddings. We trained a Transformer to learn the mapping from a contextualized word embedding to its sense ball embedding, just like playing the game of darts (a game of shooting darts into a dartboard). A series of experiments are carried out using pretraining n ball embeddings, which cover around 70% training data and 75% testing data in the benchmark WSD corpus. Euclidean distance and cosine similarity functions are used as objective functions, separately, and each reaches > 95.0% F1 score in the ALL-nball dataset. This substantially breaks the glass ceiling of deep learning methods. Future work is discussed to develop a full-fledged neurosymbolic WSD system that substantially outperforms deep learning approaches. © 2024 ELRA language Resource Association.

关键词： Knowledge graph

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LLMs for Relational Reasoning: How Far areWe? 1

LLMs for Relational Reasoning: How Far areWe?

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1st International workshop on Large language Models for Code (LLM4Code)

作者： Li, Zhiming Cao, Yushi Xu, Xiufeng Jiang, Junzhe Liu, Xu Teo, Yon Shin Lin, Shang-Wei Liu, Yang Nanyang Technol Univ Continental NTU Corp Lab Singapore Singapore Nanyang Technol Univ Singapore Singapore Hong Kong Polytech Univ Hong Kong Peoples R China Natl Univ Singapore Singapore Singapore Continental Automot Singapore Pte Ltd Singapore Singapore

ISBN: (纸本)9798400705793

Large language models (LLMs) have revolutionized many areas ( e.g. natural language processing, software engineering, etc.) by achieving state-of-the-art performance on extensive downstream tasks. Aiming to achieve robust and general artificial intelligence, there has been a surge of interest in investigating the reasoning ability of the LLMs. Whereas the textual and numerical reasoning benchmarks adopted by previous works are rather shallow and simple, it is hard to conclude that the LLMs possess strong reasoning ability by merely achieving positive results on these benchmarks. Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems that require common-sense planning by evaluating their performance on the reinforcement learning benchmarks. In this work, we conduct an in-depth assessment of several state-of-the-art LLMs' reasoning ability based on the inductive logic programming (ILP) benchmark, which is broadly recognized as a representative and challenging measurement for evaluating logic program induction/synthesis systems as it requires inducing strict cause-effect logic to achieve robust deduction on independent and identically distributed (IID) and out-of-distribution (OOD) test samples. Our evaluations illustrate that compared with the neural program induction systems which are much smaller in model size, the state-of-the-art LLMs are much poorer in terms of reasoning ability by achieving much lower performance and generalization using either natural language prompting or truth-value matrix prompting1.

关键词： Large language Models Relational Reasoning Program Induction

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An Iteratively-refined Dataset for High-Level Synthesis Functional Verification through LLM-Aided Bug Injection

An Iteratively-refined Dataset for High-Level Synthesis Func...

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1st IEEE International workshop on LLM-Aided Design (LAD)

作者： Wan, Lily Jiaxin Ye, Hanchen Wang, Jinghua Jha, Manvi Chen, Deming Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9798350376098;9798350376081

This paper explores the application of Large language Models (LLMs) in the domain of High-Level Synthesis (HLS) for hardware design and verification, f ocusing o n f unctional v erification ch allenges. The scarcity of open-source HLS codebases, especially those containing bugs, poses a significant c hallenge, a s L LMs r e quire e x tensive d a tasets f or efficient fine-tuning and evaluation. To tackle this, we introduce an innovative bug injection methodology working with a new dataset that we curated from a wide range of open-source HLS benchmark suites. This dataset features over 1,500 designs, with both the version injected with bugs and the corresponding bug-free version. Our bug injection method synergizes In-Context learning (ICL) with Retrieval Augmented Generation (RAG), and Chain of Thought (CoT). This approach significantly b o osts the dataset's overall validity rate for single-bug injections. We demonstrate our solution quality using GPT-4 Turbo for injecting either logic bugs or non-ideal pragmas (compiler directives) into HLS designs. For logic bugs, we achieve an 84.8% ratio for valid injection attempts. Furthermore, our approach maintains an 88.0% dataset validity rate (the valid bug injection rate). In addition, we also evaluate the quality of HLS pragma injections (focusing on non-ideal pragmas), and achieve a 74.0% attempt and an 87.9% valid injection ratio. Compared to brute-force prompting, our strategy yields a 20.4% and a 54.0% validity improvement for the bug and non-ideal pragma injection, respectively. The Chrysalis dataset is accessible at https://***/UIUC-ChenLab/Chrysalis-HLS.

关键词： Large language Models High-Level Synthesis functional verification dataset

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Connect++: A New Automated Theorem Prover Based on the Connection Calculus 1

Connect++: A New Automated Theorem Prover Based on the Conne...

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1st International workshop on Automated Reasoning with Connection Calculi, AReCCa 2023

作者： Holden, Sean B. University of Cambridge Department of Computer Science and Technology The Computer Laboratory William Gates Building 15 JJ Thomson Avenue CambridgeCB3 0FD United Kingdom

Connect++ is an automated theorem prover for first-order logic with equality, based on the clausal connection calculus and designed with three primary goals. The first was to produce a carefully coded system in a compiled language (C++). The second was to allow the system to support my own research agenda involving the addition of machine learning to connection provers. The third, somewhat inspired by the success of the MiniSAT solver for Boolean satisfiability, was to provide an implementation sufficiently modifiable as to provide a common basis for experiments by others. In addition to these aims I wanted to exploit the opportunities inherent in the connection calculus to explore the production of readable and certified proofs. This paper describes the system as it stands;development is ongoing and some plans for the future are also outlined. © 2023 CEUR-WS. All rights reserved.

关键词： Machine learning

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Extending Real logic with Aggregate Functions 15

Extending Real Logic with Aggregate Functions

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15th International workshop on Neural-Symbolic learning and Reasoning (NeSy) as part of the 1st International Joint Conference on learning and Reasoning (IJCLR)

作者： Badreddine, Samy Spranger, Michael Sony AI Inc Minato Ku 1-7-1 Konan Tokyo 1080075 Japan Sony Comp Sci Labs Inc 3-14-13 Higashi Gotanda Tokyo 1410022 Japan

Real logic is a recently introduced first-order language where formulas have fuzzy truth values in the interval [0,1] and semantics are defined concretely with real domains. The logic Tensor Networks (LTN) framework has applied Real logic to many important AI tasks through querying, learning, and reasoning. Motivated by real-life relational database applications, we study adding aggregate functions, such as averaging elements of a relation table, to Real logic. The key contribution of this paper is the formalization of such functions within Real logic. This extension is straightforward and fits coherently in the end-to-end differentiable language that Real logic is. We illustrate it on FooDB, a food chemistry database, and query foods and their nutrients. The resulting framework combines strengths of descriptive statistics modeled by fuzzy predicates, FOL to write complex queries and formulas, and SQL-like expressiveness to aggregate insights from data tables.

关键词： Many-Valued logic Relational Database Querying Aggregation Neurosymbolic AI

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Justification in Case-Based Reasoning 1

Justification in Case-Based Reasoning

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1st International workshop on Argumentation for eXplainable AI, ArgXAI 2022

作者： van Woerkom, Wijnand Grossi, Davide Prakken, Henry Verheij, Bart Department of Information and Computing Sciences Utrecht University Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands Amsterdam Center for Law and Economics University of Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Netherlands Faculty of Law University of Groningen Netherlands

The explanation and justification of decisions is an important subject in contemporary data-driven automated methods. Case-based argumentation has been proposed as the formal background for the explanation of data-driven automated decision making. In particular, a method was developed in recent work based on the theory of precedential constraint which reasons from a case base, given by the training data of the machine learning system, to produce a justification for the outcome of a focus case. An important role is played in this method by the notions of citability and compensation, and in the present work we develop these in more detail. Special attention is paid to the notion of compensation;we formally specify the notion and identify several of its desirable properties. These considerations reveal a refined formal perspective on the explanation method as an extension of the theory of precedential constraint with a formal notion of justification. © 2022 Copyright for this paper by its authors.

关键词： Case based reasoning

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Big generalizations with small data: Exploring the role of training samples in learning adjectives of size 1

Big generalizations with small data: Exploring the role of t...

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1st workshop on Beyond Vision and language: inTEgrating Real-World kNowledge, LANTERN@EMNLP-IJCNLP 2019

作者： Pezzelle, Sandro Fernández, Raquel Institute for Logic Language and Computation University of Amsterdam Netherlands

ISBN: (纸本)9781950737758

In this paper, we experiment with a recently proposed visual reasoning task dealing with quantities - modeling the multimodal, contextually-dependent meaning of size adjectives ('big', 'small') - and explore the impact of varying the training data on the learning behavior of a state-of-art system. In previous work, models have been shown to fail in generalizing to unseen adjective-noun combinations. Here, we investigate whether, and to what extent, seeing some of these cases during training helps a model understand the rule subtending the task, i.e., that being big implies being not small, and vice versa. We show that relatively few examples are enough to understand this relationship, and that developing a specific, mutually exclusive representation of size adjectives is beneficial to the task. © 2019 Association for Computational Linguistics

关键词： Computational linguistics

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These are not the stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution 1

These are not the Stereotypes You are Looking For: Bias and ...

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1st ACL workshop on Ethics in Natural language Processing, EthNLP 2017

作者： Koolen, Corina van Cranenburgh, Andreas Institute for Logic Language and Computation University of Amsterdam Netherlands Institut für Sprache und Information Heinrich Heine University Düsseldorf Germany

ISBN: (纸本)9781945626470

stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results. © 2017 Association for Computational Linguistics.

关键词： Text processing

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Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive lstms 1

Quantifying the vanishing gradient and long distance depende...

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1st workshop on Representation learning for NLP, Rep4NLP 2016 at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Le, Phong Zuidema, Willem Institute for Logic Language and Computation University of Amsterdam Netherlands

ISBN: (纸本)9781945626043

Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLstM) are models that compute representations for sentences, by recursively combining word embeddings according to an externally provided parse tree. Both models thus, unlike recurrent networks, explicitly make use of the hierarchical structure of a sentence. In this paper, we demonstrate that RNNs nevertheless suffer from the vanishing gradient and long distance dependency problem, and that RLstMs greatly improve over RNN's on these problems. We present an artificial learning task that allows us to quantify the severity of these problems for both models. We further show that a ratio of gradients (at the root node and a focal leaf node) is highly indicative of the success of backpropagation at optimizing the relevant weights low in the tree. This paper thus provides an explanation for existing, superior results of RLstMs on tasks such as sentiment analysis, and suggests that the benefits of including hierarchical structure and of including LstM-style gating are complementary. © 2016 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All Rights Reserved.

关键词： Sentiment analysis

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