Multilingual Large Model Hallucination Evaluation Framework: A Systematic Study Focusing on Indian Languages

This study constructs a multilingual large model hallucination evaluation framework for Indian languages, integrating three technical routes: cross-language adaptation of TruthfulQA, integrated application of NLLB-200, and mechanistic interpretability analysis. It fills the gap in hallucination research for low-resource languages and provides reliable evaluation tools and insights for academic and industrial applications.

The 'hallucination' problem of large language models restricts their reliable application. Existing research focuses on high-resource languages like English, with insufficient attention to the 22+ official languages of India and the low-resource languages used by hundreds of millions of non-English users. Multilingual evaluation faces unique challenges: large differences in grammar, culture, and knowledge distribution; translation-based testing methods struggle to capture language-specific hallucination patterns; and there is a lack of high-quality benchmark datasets and tools.

Evaluation Methodology

1. Cross-language Adaptation of TruthfulQA: Solve translation quality control (semantic equivalence + cultural context), answer standard localization (culture-specific truth criteria), and difficulty calibration (adjusting indicators for language differences).
2. Integration of NLLB-200: Undertake roles in data augmentation (expanding training and testing data), cross-language transfer (extending English benchmarks to target languages), and hallucination detection assistance (comparing semantic consistency).
3. Mechanistic Interpretability: Analyze attention patterns, track neuron activation related to hallucinations, and conduct causal intervention experiments (ablation tests to verify component impacts).

Indian Language Coverage Strategy

Select representative language families (Indo-European/Dravidian), handle multiple writing systems (Devanagari/Tamil, etc.), and address code-mixing phenomena (e.g., Hindi-English code-mixing).

Dataset Construction

1. Translate benchmarks like TruthfulQA and verify with native speakers; 2. Collect Indian local knowledge questions to fill gaps; 3. Generate adversarial samples to improve evaluation discrimination.

Evaluation Metrics

Accuracy (factual correctness), Consistency (answer stability under different expressions), Confidence Calibration (matching degree between model confidence and accuracy), Cross-language Transferability (ability to transfer knowledge across languages).

Interpretability Tools

Activation visualization (attention heatmaps/neuron distribution), Probe classifiers (identify internal representations related to hallucinations), Intervention interface (manual intervention on layers/observe output changes from neurons).

Key Findings

• Impact of language resource differences: There are systematic differences in hallucination performance between high and low-resource languages (knowledge distribution bias, reasoning ability differences, higher hallucination rates for non-Western cultural questions).
• Hallucination types: Translation-induced, knowledge transfer failure, language confusion, fictional citation.

Mitigation Recommendations

Multilingual pre-training optimization (increase high-quality low-resource data), culture-aware fine-tuning (local expert annotated data), retrieval-augmented generation (build Indian language knowledge bases), uncertainty quantification (models actively express uncertainty).

Academic Contributions

Provide standardized evaluation tools, Indian language hallucination test benchmark datasets, and application examples of mechanistic interpretability in low-resource languages.

Industrial Guidance

Help enterprises select models (compare hallucination performance), identify scenario risk points, and clarify optimization paths.

Social Equity

Promote AI inclusion (low-resource language users get reliable services), respect culture (avoid marginalization of non-Western knowledge), and promote participatory development (local evaluation drives demand-matching technology).

Current Limitations

Incomplete language coverage (cannot cover all Indian languages/dialects), results easily outdated due to dynamic model updates, subjective factors in fact judgment.

Future Work

Develop real-time hallucination monitoring systems post-deployment, design user-participatory dynamic evaluation mechanisms, expand to multimodal scenarios (text and images), deepen research on causal attribution of hallucinations.