New Framework Proposed for Detecting and Mitigating Hallucinations in Large Language Models

Global: New Framework Proposed for Detecting and Mitigating Hallucinations in Large Language Models

Researchers Ahmad Pesaranghader and Erin Li submitted a paper to arXiv on 14 Jan 2026 that proposes an operational framework aimed at detecting and mitigating hallucinations in large language models (LLMs) and large reasoning models (LRMs). The work addresses the reliability risk posed by factually incorrect or unsupported outputs, particularly in high‑stakes sectors such as finance and law.

Framework Overview

The authors describe a continuous‑improvement cycle that emphasizes root‑cause awareness. By structuring the process around ongoing feedback, the framework seeks to enhance model reliability over time rather than relying on isolated fixes.

Sources of Hallucinations

Hallucination origins are categorized into three groups: model‑related factors, data‑related factors, and context‑related factors. This taxonomy enables targeted interventions that address the specific cause of an error.

Detection Techniques

Multiple detection methods are integrated, including uncertainty estimation and reasoning‑consistency checks. These approaches aim to flag outputs that deviate from expected confidence levels or logical coherence.

Mitigation Strategies

Mitigation actions range from knowledge grounding—linking model responses to verified external sources—to confidence calibration, which adjusts the model’s self‑assessment of answer certainty.

Case Study in Finance

The paper demonstrates the framework through a tiered architecture applied to a financial data extraction task. Model, context, and data tiers interact in a closed feedback loop, allowing progressive improvements in extraction accuracy.

Implications for Regulation

By providing a systematic, scalable methodology, the framework offers a potential pathway for deploying generative AI systems in regulated environments where trustworthiness is paramount.

Future Directions

The authors suggest that extending the feedback loop and incorporating domain‑specific knowledge bases could further reduce hallucination rates across diverse applications.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.