mlsanityData to 14 Oct 2026

A Principled Framework for Knowledge-enhanced Large Language Model

arXiv 2311.11135 ↗First posted 18 Nov 2023cs.CL cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 18 Nov 2023.

Builds on

6 of 8 references.

References · 6

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
  3. 2022A Survey on In-context Learningcited by 721
  4. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  5. 2023What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalizationcited by 118
  6. 2023Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiencycited by 46

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Modelcited by 30

Similar, not cited

Close in wording, no citation either way

  1. 2025Advancing Reasoning in Large Language Models: Promising Methods and Approaches
  2. 2026Maistros: A Greek Large Language Model Adapted Through Knowledge Distillation From Large Reasoning Models
  3. 2022Towards Reasoning in Large Language Models: A Survey
  4. 2023Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance
  5. 2024LLM2: Let Large Language Models Harness System 2 Reasoning
  6. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
  7. 2026Opening the Black Box: A Survey on the Mechanisms of Multi-Step Reasoning in Large Language Models
  8. 2026Large Language Model Reasoning Failures