Reasoning with Natural Language Explanations

• URL: http://arxiv.org/abs/2410.04148v1
• Date: Sat, 5 Oct 2024 13:15:24 GMT
• Title: Reasoning with Natural Language Explanations
• Authors: Marco Valentino, André Freitas,
• Abstract summary: Explanation constitutes an archetypal feature of human rationality, underpinning learning and generalisation. An increasing amount of research in Natural Language Inference (NLI) has started reconsidering the role that explanations play in learning and inference.
• Score: 15.281385727331473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explanation constitutes an archetypal feature of human rationality, underpinning learning and generalisation, and representing one of the media supporting scientific discovery and communication. Due to the importance of explanations in human reasoning, an increasing amount of research in Natural Language Inference (NLI) has started reconsidering the role that explanations play in learning and inference, attempting to build explanation-based NLI models that can effectively encode and use natural language explanations on downstream tasks. Research in explanation-based NLI, however, presents specific challenges and opportunities, as explanatory reasoning reflects aspects of both material and formal inference, making it a particularly rich setting to model and deliver complex reasoning. In this tutorial, we provide a comprehensive introduction to the field of explanation-based NLI, grounding this discussion on the epistemological-linguistic foundations of explanations, systematically describing the main architectural trends and evaluation methodologies that can be used to build systems capable of explanatory reasoning.

Related papers

• Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving [13.485604499678262]
  This paper investigates the verification and refinement of natural language explanations through the integration of Large Language Models (LLMs) and Theorem Provers (TPs) We present a neuro-symbolic framework, named Explanation-Refiner, that integrates TPs with LLMs to generate and formalise explanatory sentences. In turn, the TP is employed to provide formal guarantees on the logical validity of the explanations and to generate feedback for subsequent improvements.
  arXiv  Detail & Related papers  (2024-05-02T15:20:01Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning [59.16962123636579]
  This paper proposes a new take on Prolog-based inference engines. We replace handcrafted rules with a combination of neural language modeling, guided generation, and semi dense retrieval. Our implementation, NELLIE, is the first system to demonstrate fully interpretable, end-to-end grounded QA.
  arXiv  Detail & Related papers  (2022-09-16T00:54:44Z)
• Scientific Explanation and Natural Language: A Unified Epistemological-Linguistic Perspective for Explainable AI [2.7920304852537536]
  This paper focuses on the scientific domain, aiming to bridge the gap between theory and practice on the notion of a scientific explanation. Through a mixture of quantitative and qualitative methodologies, the presented study allows deriving the following main conclusions.
  arXiv  Detail & Related papers  (2022-05-03T22:31:42Z)
• Rethinking Explainability as a Dialogue: A Practitioner's Perspective [57.87089539718344]
  We ask doctors, healthcare professionals, and policymakers about their needs and desires for explanations. Our study indicates that decision-makers would strongly prefer interactive explanations in the form of natural language dialogues. Considering these needs, we outline a set of five principles researchers should follow when designing interactive explanations.
  arXiv  Detail & Related papers  (2022-02-03T22:17:21Z)
• Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards [0.0]
  An emerging line of research in Explainable NLP is the creation of datasets enriched with human-annotated explanations and rationales. While human-annotated explanations are used as ground-truth for the inference, there is a lack of systematic assessment of their consistency and rigour. We propose a systematic annotation methodology, named Explanation Entailment Verification (EEV), to quantify the logical validity of human-annotated explanations.
  arXiv  Detail & Related papers  (2021-05-05T10:59:26Z)
• Abduction and Argumentation for Explainable Machine Learning: A Position Survey [2.28438857884398]
  This paper presents Abduction and Argumentation as two principled forms for reasoning. It fleshes out the fundamental role that they can play within Machine Learning.
  arXiv  Detail & Related papers  (2020-10-24T13:23:44Z)
• Towards Interpretable Natural Language Understanding with Explanations as Latent Variables [146.83882632854485]
  We develop a framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model.
  arXiv  Detail & Related papers  (2020-10-24T02:05:56Z)
• Towards Interpretable Reasoning over Paragraph Effects in Situation [126.65672196760345]
  We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect. We propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules. In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model.
  arXiv  Detail & Related papers  (2020-10-03T04:03:52Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.