Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
- URL: http://arxiv.org/abs/2404.01677v2
- Date: Wed, 3 Apr 2024 09:28:31 GMT
- Title: Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
- Authors: Zhouhao Sun, Xiao Ding, Li Du, Bibo Cai, Jinglong Gao, Ting Liu, Qin Bing,
- Abstract summary: We propose a novel framework, named Generalizable and Faithful Reasoner (GFaiR), which introduces the paradigm of resolution refutation.
Resolution refutation has the capability to solve all first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction.
Our system outperforms previous works by achieving state-of-the-art performances in complex scenarios while maintaining performances in simple scenarios.
- Score: 24.584926992534346
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks. However, they still struggle with performing first-order logic reasoning over formal logical theories expressed in natural language. This is because the previous LLMs-based reasoning systems have the theoretical incompleteness issue. As a result, it can only address a limited set of simple reasoning problems, which significantly decreases their generalization ability. To address this issue, we propose a novel framework, named Generalizable and Faithful Reasoner (GFaiR), which introduces the paradigm of resolution refutation. Resolution refutation has the capability to solve all first-order logic reasoning problems by extending reasoning rules and employing the principle of proof by contradiction, so our system's completeness can be improved by introducing resolution refutation. Experimental results demonstrate that our system outperforms previous works by achieving state-of-the-art performances in complex scenarios while maintaining performances in simple scenarios. Besides, we observe that GFaiR is faithful to its reasoning process.
Related papers
- Make LLMs better zero-shot reasoners: Structure-orientated autonomous reasoning [52.83539473110143]
We introduce a novel structure-oriented analysis method to help Large Language Models (LLMs) better understand a question.
To further improve the reliability in complex question-answering tasks, we propose a multi-agent reasoning system, Structure-oriented Autonomous Reasoning Agents (SARA)
Extensive experiments verify the effectiveness of the proposed reasoning system. Surprisingly, in some cases, the system even surpasses few-shot methods.
arXiv Detail & Related papers (2024-10-18T05:30:33Z) - Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning.
This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs.
Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
arXiv Detail & Related papers (2024-09-25T18:35:45Z) - Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation [19.799266797193344]
Argumentation-based systems often lack explainability while supporting decision-making processes.
Counterfactual and semifactual explanations are interpretability techniques.
We show that counterfactual and semifactual queries can be encoded in weak-constrained Argumentation Framework.
arXiv Detail & Related papers (2024-05-07T07:27:27Z) - LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models [52.03659714625452]
Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks.
But, can they really "reason" over the natural language?
This question has been receiving significant research attention and many reasoning skills such as commonsense, numerical, and qualitative have been studied.
arXiv Detail & Related papers (2024-04-23T21:08:49Z) - Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs [87.34281749422756]
Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks.
However, their mastery of underlying inferential rules still falls short of human capabilities.
We propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic.
arXiv Detail & Related papers (2024-02-18T03:38:51Z) - Language Models can be Logical Solvers [99.40649402395725]
We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers.
LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
arXiv Detail & Related papers (2023-11-10T16:23:50Z) - Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic [19.476840373850653]
Large language models show hallucinations as their reasoning procedures are unconstrained by logical principles.
We propose LoT (Logical Thoughts), a self-improvement prompting framework that leverages principles rooted in symbolic logic.
Experimental evaluations conducted on language tasks in diverse domains, including arithmetic, commonsense, symbolic, causal inference, and social problems, demonstrate the efficacy of enhanced reasoning by logic.
arXiv Detail & Related papers (2023-09-23T11:21:12Z) - MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios.
Based on the multi-hop chain of reasoning, the explanation form includes three main components.
We evaluate the current best models' performance on this new explanation form.
arXiv Detail & Related papers (2022-10-22T16:01:13Z) - Faithful Reasoning Using Large Language Models [12.132449274592668]
We show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem.
Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs.
We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy.
arXiv Detail & Related papers (2022-08-30T13:44:41Z) - Logical Neural Networks [51.46602187496816]
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning)
Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly intepretable disentangled representation.
Inference is omni rather than focused on predefined target variables, and corresponds to logical reasoning.
arXiv Detail & Related papers (2020-06-23T16:55:45Z)
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.