Enhancing Ethical Explanations of Large Language Models through
Iterative Symbolic Refinement
- URL: http://arxiv.org/abs/2402.00745v1
- Date: Thu, 1 Feb 2024 16:39:51 GMT
- Title: Enhancing Ethical Explanations of Large Language Models through
Iterative Symbolic Refinement
- Authors: Xin Quan, Marco Valentino, Louise A. Dennis, Andr\'e Freitas
- Abstract summary: This paper investigates how hybrid neuro-symbolic techniques can enhance the logical validity and alignment of ethical explanations.
We present an abductive-deductive framework named Logic-Explainer, which integrates Large Language Models with an external backward-chaining solver.
An empirical analysis demonstrates that Logic-Explainer can improve explanations generated via in-context learning methods and Chain-of-Thought.
- Score: 5.108863224378874
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: An increasing amount of research in Natural Language Inference (NLI) focuses
on the application and evaluation of Large Language Models (LLMs) and their
reasoning capabilities. Despite their success, however, LLMs are still prone to
factual errors and inconsistencies in their explanations, offering limited
control and interpretability for inference in complex domains. In this paper,
we focus on ethical NLI, investigating how hybrid neuro-symbolic techniques can
enhance the logical validity and alignment of ethical explanations produced by
LLMs. Specifically, we present an abductive-deductive framework named
Logic-Explainer, which integrates LLMs with an external backward-chaining
solver to refine step-wise natural language explanations and jointly verify
their correctness, reduce incompleteness and minimise redundancy. An extensive
empirical analysis demonstrates that Logic-Explainer can improve explanations
generated via in-context learning methods and Chain-of-Thought (CoT) on
challenging ethical NLI tasks, while, at the same time, producing formal proofs
describing and supporting models' reasoning. As ethical NLI requires
commonsense reasoning to identify underlying moral violations, our results
suggest the effectiveness of neuro-symbolic methods for multi-step NLI more
broadly, opening new opportunities to enhance the logical consistency,
reliability, and alignment of LLMs.
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 augments a TP 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) - FaithLM: Towards Faithful Explanations for Large Language Models [67.29893340289779]
Large Language Models (LLMs) have become proficient in addressing complex tasks by leveraging their internal knowledge and reasoning capabilities.
The black-box nature of these models complicates the task of explaining their decision-making processes.
We introduce FaithLM to explain the decision of LLMs with natural language (NL) explanations.
arXiv Detail & Related papers (2024-02-07T09:09:14Z) - From Understanding to Utilization: A Survey on Explainability for Large
Language Models [27.295767173801426]
This survey underscores the imperative for increased explainability in Large Language Models (LLMs)
Our focus is primarily on pre-trained Transformer-based LLMs, which pose distinctive interpretability challenges due to their scale and complexity.
When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement.
arXiv Detail & Related papers (2024-01-23T16:09:53Z) - LLMs for Relational Reasoning: How Far are We? [8.840750655261251]
Large language models (LLMs) have revolutionized many areas by achieving state-of-the-art performance on downstream tasks.
Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems.
arXiv Detail & Related papers (2024-01-17T08:22:52Z) - A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning [73.77088902676306]
We take a closer look at the self-verification abilities of large language models (LLMs) in the context of logical reasoning.
Our main findings suggest that existing LLMs could struggle to identify fallacious reasoning steps accurately and may fall short of guaranteeing the validity of self-verification methods.
arXiv Detail & Related papers (2023-11-14T07:13:10Z) - Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement [92.61557711360652]
Language models (LMs) often fall short on inductive reasoning, despite achieving impressive success on research benchmarks.
We conduct a systematic study of the inductive reasoning capabilities of LMs through iterative hypothesis refinement.
We reveal several discrepancies between the inductive reasoning processes of LMs and humans, shedding light on both the potentials and limitations of using LMs in inductive reasoning tasks.
arXiv Detail & Related papers (2023-10-12T17:51:10Z) - Concise and Organized Perception Facilitates Reasoning in Large Language Models [32.71672086718057]
We show that large language models (LLMs) exhibit failure patterns akin to human-like cognitive biases when dealing with disordered and irrelevant content in reasoning tasks.
We propose a novel reasoning approach named Concise and Organized Perception (COP)
COP carefully analyzes the given statements to identify the most pertinent information while eliminating redundancy efficiently.
arXiv Detail & Related papers (2023-10-05T04:47:49Z) - Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems.
LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning.
We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
arXiv Detail & Related papers (2023-10-02T01:00:50Z) - Large Language Models are In-Context Semantic Reasoners rather than
Symbolic Reasoners [75.85554779782048]
Large Language Models (LLMs) have excited the natural language and machine learning community over recent years.
Despite of numerous successful applications, the underlying mechanism of such in-context capabilities still remains unclear.
In this work, we hypothesize that the learned textitsemantics of language tokens do the most heavy lifting during the reasoning process.
arXiv Detail & Related papers (2023-05-24T07:33:34Z) - ChatABL: Abductive Learning via Natural Language Interaction with
ChatGPT [72.83383437501577]
Large language models (LLMs) have recently demonstrated significant potential in mathematical abilities.
LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities.
This paper presents a novel method for integrating LLMs into the abductive learning framework.
arXiv Detail & Related papers (2023-04-21T16:23:47Z)
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.