HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification (2504.07069v1)

Abstract: This paper introduces a comprehensive system for detecting hallucinations in LLM outputs in enterprise settings. We present a novel taxonomy of LLM responses specific to hallucination in enterprise applications, categorizing them into context-based, common knowledge, enterprise-specific, and innocuous statements. Our hallucination detection model HDM-2 validates LLM responses with respect to both context and generally known facts (common knowledge). It provides both hallucination scores and word-level annotations, enabling precise identification of problematic content. To evaluate it on context-based and common-knowledge hallucinations, we introduce a new dataset HDMBench. Experimental results demonstrate that HDM-2 out-performs existing approaches across RagTruth, TruthfulQA, and HDMBench datasets. This work addresses the specific challenges of enterprise deployment, including computational efficiency, domain specialization, and fine-grained error identification. Our evaluation dataset, model weights, and inference code are publicly available.

HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification

The paper "HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification" provides an in-depth exploration of detecting hallucinations in the outputs of LLMs, specifically within enterprise environments. These hallucinations, defined as plausible-sounding yet factually incorrect responses, pose significant risks in high-stakes settings such as legal compliance and customer support, where accuracy is paramount. This research presents a novel taxonomy and a model, HDM-2, that identifies and tackles these hallucinations with greater precision than existing methods.

Taxonomy and Methodology

The authors introduce a refined taxonomy classifying hallucinations into four categories: context-based, common knowledge, enterprise-specific, and innocuous statements. This classification enables a more nuanced detection approach, offering a route to address varied factual error types that previous models overlooked by treating hallucinations monolithically. By distinguishing these types, the model can provide precise mitigation strategies and more accurate enterprise applications.

HDM-2, the proposed hallucination detection model, uses a modular approach focusing on context and common knowledge. It integrates context verification and common knowledge validation to generate hallucination scores and annotations at the word level. This framework supports a more detailed and scalable solution for deploying LLMs in enterprise environments. The model's evaluation demonstrates superior performance in the new HDMBench dataset, as well as the established RagTruth and TruthfulQA datasets, surpassing existing state-of-the-art methods.

Key Experimental Insights

HDM-2 shows significant advancements in detecting both contextual and common knowledge hallucinations. The score-driven analysis offers profoundly granular insights, achieving improved performance metrics compared to other methodologies. Moreover, the provided HDMBench dataset ensures comprehensive testing for different hallucination types, enhancing the validity and applicability of the model. Explicitly, HDM-2’s framework supports enterprise-specific adaptations through continued pre-training, offering a tailored validation route against proprietary enterprise knowledge.

Implications and Future Developments

The implications of this research are far-reaching. Practically, HDM-2’s approach enhances accuracy in real-world deployments of LLMs within enterprises, supporting tasks that demand high reliability, such as regulatory and compliance-related activities. Theoretically, it pushes the boundary of hallucination detection frameworks, suggesting new pathways for improving LLM real-world effectiveness.

Future developments in AI could explore deploying HDM-2’s architecture in multilingual settings or expanding its application in highly specialized domains requiring niche knowledge integration. Additionally, while the framework offers adaptational flexibility, further refinement could explore reducing computational demands, which would streamline enterprise-wide deployment.

Overall, this paper contributes significantly to the field of hallucination detection in LLMs, providing a detailed, effective framework for enterprise applications, addressing both previously unmet practical challenges and proposing theoretical advancements in the field of natural language processing.