What question did this study set out to answer?

The aim is to develop a framework to detect and mitigate reasoning biases in large language models.

March 13, 20260 citationsOpen Access

CSLV: Cross-Source Logical Validation for Reducing Source-Induced Reasoning Bias in Large Language Models

Key Points

The aim is to develop a framework to detect and mitigate reasoning biases in large language models.
Introduced the CSLV framework for cross-source logical validation
Evaluated logical consistency between queries, examples, and model outputs
Investigated ANIMA, integrating memory structures and validation layers
Conducted experimental validation and conceptual analysis
CSLV effectively reduced hallucination in model outputs
Minimized example-dependency in reasoning
Enhanced reliability of reasoning in large language models

Abstract

This work introduces CSLV (Cross-Source Logical Validation), a framework designed to detect and mitigate source-induced reasoning bias in large language models. The approach evaluates the logical consistency between the original query, provided examples, and the model's generated reasoning. The report also evaluates ANIMA, a cognitive architecture concept that integrates identity-based memory structures and validation layers to enhance reasoning reliability in AI systems. Experimental validation and conceptual analysis are provided to demonstrate how cross-source validation can reduce hallucination and example-dependency in LLM outputs.

Demander à l'IA

Bookmark

View Full Paper