Pre-detection of LLM Hallucination Risks: A Study on Pre-Inference Classifier Based on DeBERTa-v3

This study proposes an innovative pre-inference hallucination detection system that predicts hallucination risks before LLM generation through multi-model consensus annotation and DeBERTa-v3 fine-tuning. It addresses issues like resource waste and poor user experience in traditional post-hoc detection, providing proactive prevention ideas for the safe application of LLMs.

LLM hallucination refers to the generation of seemingly reasonable but incorrect/fictional content, which is a core obstacle to large-scale applications and has severe consequences in critical scenarios like healthcare and law. Traditional post-hoc detection has three major limitations: resource waste (detection only after incorrect content is generated), poor user experience (users first see wrong content), and high costs (massive computing resources consumed during generation). The Harshbhatt1008 project proposes an innovative idea of pre-inference risk prediction.

1. Synthetic dataset generation: Generate seed queries based on templates and knowledge bases, label risk levels combined with query features, and construct adversarial samples;
2. Multi-model consensus annotation: Multiple LLMs answer the same query independently, low consistency is marked as high risk, and manual verification is combined to improve reliability;
3. DeBERTa-v3 fine-tuning: Select its advantages such as enhanced decoding, moderate scale, and efficient inference, and adopt strategies like layered learning rate and early stopping mechanism;
4. Probability evaluation framework: Quantify prediction uncertainty via Shannon entropy, and introduce significance tests to ensure prediction reliability.

Workflow: Receive query → Extract semantic features and risk indicators → DeBERTa-v3 classification to evaluate risk → Decision branch (low risk: direct generation; medium risk: enable RAG; high risk: reject/transfer to human) → Return result. Experimental evaluation: Classification performance (high accuracy, recall, precision on test set); Cost-effectiveness (reduce invalid generation, optimize resource allocation, improve user experience); Interpretability (attention visualization helps understand decision basis).

Application scenarios: Enterprise-level deployment (security gateway filters compliance risks, routing processing pipeline, generates audit logs); Customer service system (real-time evaluation of answerability, high risk transferred to human); Content platform (pre-generation risk assessment, fact-checking for sensitive topics). Limitations: Complex risk definition (difficult to cover multiple dimensions), domain specificity (need to adapt to different domain tolerances), adversarial attacks (need to continuously update defense strategies).

Comparison with post-hoc detection: Pre-detection has earlier timing, lower cost, and proactive prevention; Comparison with uncertainty quantification: Cross-model universal, lower computational overhead. Future directions: Multimodal expansion (cross-modal alignment risk pre-detection), real-time online learning (continuous improvement from deployment feedback), deep integration with LLM architecture (fine-grained generation control).

The pre-inference hallucination risk classifier realizes the transformation from 'post-hoc remedy' to 'pre-hoc prevention', intelligently allocates resources, and ensures output quality and efficiency. This open-source project provides valuable tools and ideas for LLM security research, is of great significance for LLM applications in critical scenarios, and is worth in-depth exploration by developers and researchers.