A Principled Framework for Knowledge-enhanced Large Language Model

• URL: http://arxiv.org/abs/2311.11135v1
• Date: Sat, 18 Nov 2023 18:10:02 GMT
• Title: A Principled Framework for Knowledge-enhanced Large Language Model
• Authors: Saizhuo Wang, Zhihan Liu, Zhaoran Wang, Jian Guo
• Abstract summary: Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning. This paper introduces a rigorously designed framework for creating LLMs that effectively anchor knowledge and employ a closed-loop reasoning process.
• Score: 58.1536118111993
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are versatile, yet they often falter in tasks requiring deep and reliable reasoning due to issues like hallucinations, limiting their applicability in critical scenarios. This paper introduces a rigorously designed framework for creating LLMs that effectively anchor knowledge and employ a closed-loop reasoning process, enhancing their capability for in-depth analysis. We dissect the framework to illustrate the contribution of each component to the LLMs' performance, offering a theoretical assurance of improved reasoning under well-defined assumptions.

Related papers

• Language Agents Meet Causality -- Bridging LLMs and Causal World Models [50.79984529172807]
  We propose a framework that integrates causal representation learning with large language models. This framework learns a causal world model, with causal variables linked to natural language expressions. We evaluate the framework on causal inference and planning tasks across temporal scales and environmental complexities.
  arXiv  Detail & Related papers  (2024-10-25T18:36:37Z)
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
  Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning. We propose Reversal of Thought (RoT), a novel framework aimed at enhancing the logical reasoning abilities of LLMs. RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
  arXiv  Detail & Related papers  (2024-10-16T07:44:28Z)
• StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization [94.31508613367296]
  Retrieval-augmented generation (RAG) is a key means to effectively enhance large language models (LLMs) We propose StructRAG, which can identify the optimal structure type for the task at hand, reconstruct original documents into this structured format, and infer answers based on the resulting structure. Experiments show that StructRAG achieves state-of-the-art performance, particularly excelling in challenging scenarios.
  arXiv  Detail & Related papers  (2024-10-11T13:52:44Z)
• 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)
• CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge [44.59258397967782]
  Large language models (LLMs) have demonstrated impressive capabilities across various natural language processing tasks. We present a systematic evaluation of state-of-the-art LLMs' complex logical reasoning abilities. We find that LLMs excel at reasoning over general world knowledge but face significant challenges with specialized domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-07-30T05:40:32Z)
• Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought [31.964412924094656]
  Large language models (LLMs) have shown exceptional performance as general-purpose assistants. We introduce a novel learning framework, THOUGHT-LIKE-PRO, to facilitate learning and generalization across diverse reasoning tasks. Our empirical findings indicate that our proposed approach substantially enhances the reasoning abilities of LLMs.
  arXiv  Detail & Related papers  (2024-07-18T18:52:10Z)
• On the Hardness of Faithful Chain-of-Thought Reasoning in Large Language Models [25.029579061612456]
  Large Language Models (LLMs) are increasingly being employed in real-world applications in critical domains such as healthcare. It is important to ensure that the Chain-of-Thought (CoT) reasoning generated by these models faithfully captures their underlying behavior.
  arXiv  Detail & Related papers  (2024-06-15T13:16:44Z)
• 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)
• 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)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)

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.