3/20/2026 | USA | technology | ✓ Verified - arxiv.org

Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs

#progressive training #explainable AI #citation-grounded dialogue #hallucination reduction #English-Hindi LLMs

📌 Key Takeaways

Researchers developed a progressive training method to reduce hallucinations in English-Hindi LLMs.
The approach uses citation-grounded dialogue to enhance explainability and accuracy.
The method aims to achieve zero hallucination in multilingual conversational AI systems.
Training focuses on grounding responses in verifiable sources to improve reliability.

📖 Full Retelling

arXiv:2603.18911v1 Announce Type: cross Abstract: Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning on external knowledge sources. However, most existing approaches focus exclusively on English, lack explicit citation mechanisms for verifying factual claims, and offer limited transparency into model decision-making. We present XKD-Dial, a progressive four-stage training pipeline for explainable, knowledge-grounded dialogue generatio

🏷️ Themes

AI Training, Multilingual NLP

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Deep Analysis

Why It Matters

This research addresses a critical problem in multilingual AI systems - hallucination, where models generate false or unsupported information. It matters because reliable, verifiable AI responses are essential for applications in healthcare, legal advice, education, and customer service across diverse linguistic communities. The English-Hindi focus specifically benefits over 600 million Hindi speakers who need trustworthy AI tools in their native language, while the citation-grounded approach creates more transparent and accountable conversational AI.

Context & Background

Hallucination in large language models refers to the generation of plausible-sounding but factually incorrect information, which has been a persistent challenge since the emergence of GPT-style models
Multilingual LLMs have historically performed worse in non-English languages due to training data imbalances, with Hindi and other Indian languages receiving less attention than European languages
Citation-grounded dialogue systems require models to provide verifiable sources for their claims, representing an emerging approach to AI transparency and reliability
Previous attempts to reduce hallucination have included reinforcement learning from human feedback, retrieval-augmented generation, and fine-tuning techniques, but achieving zero hallucination has remained elusive
The Indian AI research community has been pushing for better indigenous language support as digital adoption grows across non-English speaking populations

What Happens Next

Researchers will likely publish detailed methodology and results in upcoming AI conferences (NeurIPS, ACL, or EMNLP 2024), followed by open-sourcing of training datasets and model checkpoints. Technology companies serving Indian markets may integrate these techniques into their Hindi-language AI products within 6-12 months. The progressive training approach could be adapted for other language pairs, potentially leading to similar research for Bengali, Tamil, and other widely spoken Indian languages.

Frequently Asked Questions

What does 'zero hallucination' mean in this context?

Zero hallucination means the model provides responses that are fully supported by cited sources without generating any unsupported factual claims. This doesn't mean perfect accuracy, but rather that every factual statement can be traced to a specific reference, allowing users to verify information.

Why focus specifically on English-Hindi language models?

Hindi is spoken by over 600 million people but has received less AI research attention than European languages. This work addresses both the hallucination problem and the language equity gap, creating more reliable AI tools for one of the world's largest linguistic communities.

How does progressive training differ from standard fine-tuning?

Progressive training gradually increases task complexity and citation requirements, allowing the model to build skills systematically rather than learning everything at once. This step-by-step approach helps the model develop more robust citation habits and reduces the tendency to generate unsupported information.

Will this make AI conversations slower or more cumbersome?

Citation-grounded responses may be slightly longer due to source references, but the research focuses on maintaining conversational flow. The trade-off is increased response time for significantly improved reliability, which is crucial for high-stakes applications.

Can this approach be applied to other languages beyond Hindi?

Yes, the progressive training methodology is language-agnostic and could be adapted for any language pair. The researchers likely chose English-Hindi as a case study that addresses both technical and equity considerations, with potential for expansion to other underserved languages.

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Original Source

              --> Computer Science > Computation and Language arXiv:2603.18911 [Submitted on 19 Mar 2026] Title: Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs Authors: Vedant Pandya View a PDF of the paper titled Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs, by Vedant Pandya View PDF HTML Abstract: Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning on external knowledge sources. However, most existing approaches focus exclusively on English, lack explicit citation mechanisms for verifying factual claims, and offer limited transparency into model decision-making. We present XKD-Dial, a progressive four-stage training pipeline for explainable, knowledge-grounded dialogue generation in a bilingual (English-Hindi) setting, comprising: (1) multilingual adaptation, (2) English dialogue SFT with citation grounding, (3) bilingual dialogue SFT, and (4) GRPO alignment with citation-aware rewards. We evaluate six models spanning encoder-decoder (250M-3B) and decoder-only (1B-7B) architectures at every pipeline stage. Our key contributions are: three post-hoc explainability analyses - cross-attention alignment, Integrated Gradients attribution, and occlusion-based causal grounding - applied systematically across the training trajectory to reveal how citation behaviour is learned, not only whether it is learned; citation-grounded SFT reduces hallucination to 0.0% for encoder-decoder models from Stage 2 onward; the progressive pipeline prevents catastrophic forgetting while improving Hindi capabilities; smaller models match larger models on English after SFT v) GRPO provides marginal improvement over well-designed SFT for structured citation tasks. We evaluate across six automatic metrics (BLEU, ROUGE, BERTScore, FactScore, Citation-F1, and hallucination rate). Comments: 30 pages, 15 figure...
            

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