Reliable Natural Language Understanding with Large Language Models and Answer Set Programming

• URL: http://arxiv.org/abs/2302.03780v3
• Date: Wed, 30 Aug 2023 08:56:37 GMT
• Title: Reliable Natural Language Understanding with Large Language Models and Answer Set Programming
• Authors: Abhiramon Rajasekharan (The University of Texas at Dallas), Yankai Zeng (The University of Texas at Dallas), Parth Padalkar (The University of Texas at Dallas), Gopal Gupta (The University of Texas at Dallas)
• Abstract summary: Large language models (LLMs) are able to leverage patterns in the text to solve a variety of NLP tasks, but fall short in problems that require reasoning. We propose STAR, a framework that combines LLMs with Answer Set Programming (ASP) Goal-directed ASP is then employed to reliably reason over this knowledge.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans understand language by extracting information (meaning) from sentences, combining it with existing commonsense knowledge, and then performing reasoning to draw conclusions. While large language models (LLMs) such as GPT-3 and ChatGPT are able to leverage patterns in the text to solve a variety of NLP tasks, they fall short in problems that require reasoning. They also cannot reliably explain the answers generated for a given question. In order to emulate humans better, we propose STAR, a framework that combines LLMs with Answer Set Programming (ASP). We show how LLMs can be used to effectively extract knowledge -- represented as predicates -- from language. Goal-directed ASP is then employed to reliably reason over this knowledge. We apply the STAR framework to three different NLU tasks requiring reasoning: qualitative reasoning, mathematical reasoning, and goal-directed conversation. Our experiments reveal that STAR is able to bridge the gap of reasoning in NLU tasks, leading to significant performance improvements, especially for smaller LLMs, i.e., LLMs with a smaller number of parameters. NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.

Related papers

• Reasoning with Large Language Models, a Survey [2.831296564800826]
  This paper reviews the rapidly expanding field of prompt-based reasoning with LLMs. Our taxonomy identifies different ways to generate, evaluate, and control multi-step reasoning. We find that self-improvement, self-reflection, and some meta abilities of the reasoning processes are possible through the judicious use of prompts.
  arXiv  Detail & Related papers  (2024-07-16T08:49:35Z)
• 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)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models [59.84769254832941]
  We propose a FaLlacy Understanding Benchmark (FLUB) containing cunning texts that are easy for humans to understand but difficult for models to grasp. Specifically, the cunning texts that FLUB focuses on mainly consist of the tricky, humorous, and misleading texts collected from the real internet environment. Based on FLUB, we investigate the performance of multiple representative and advanced LLMs.
  arXiv  Detail & Related papers  (2024-02-16T22:12:53Z)
• How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering [52.86931192259096]
  Knowledge Base Question Answering (KBQA) aims to answer natural language questions based on facts in knowledge bases. Recent works leverage the capabilities of large language models (LLMs) for logical form generation to improve performance.
  arXiv  Detail & Related papers  (2024-01-11T09:27:50Z)
• CLadder: Assessing Causal Reasoning in Language Models [82.8719238178569]
  We investigate whether large language models (LLMs) can coherently reason about causality. We propose a new NLP task, causal inference in natural language, inspired by the "causal inference engine" postulated by Judea Pearl et al.
  arXiv  Detail & Related papers  (2023-12-07T15:12:12Z)
• Zero-Shot Question Answering over Financial Documents using Large Language Models [0.18749305679160366]
  We introduce a large language model (LLM) based approach to answer complex questions requiring multi-hop numerical reasoning over financial reports. We use novel zero-shot prompts that guide the LLM to encode the required reasoning into a Python program or a domain specific language.
  arXiv  Detail & Related papers  (2023-11-19T16:23:34Z)
• Leveraging Large Language Models to Generate Answer Set Programs [5.532477732693001]
  Large language models (LLMs) have demonstrated exceptional performance in various natural language processing tasks. This paper proposes a neuro-symbolic method that combines the strengths of large language models and answer set programming.
  arXiv  Detail & Related papers  (2023-07-15T03:40:55Z)
• Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text [5.532477732693001]
  We show that a large language model can serve as a highly effective few-shot semantically. It can convert natural language sentences into a logical form that serves as input for answer set programs. We demonstrate that this method achieves state-of-the-art performance on several benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN.
  arXiv  Detail & Related papers  (2023-07-15T03:29:59Z)
• PAL: Program-aided Language Models [112.94785609781503]
  We present Program-Aided Language models (PaL) to understand natural language problems. PaL offloads the solution step to a programmatic runtime such as a Python interpreter. We set new state-of-the-art results in all 12 benchmarks.
  arXiv  Detail & Related papers  (2022-11-18T18:56:13Z)

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.