Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.01918 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912624883531776 |
|---|---|
| author | Singh, Jaskaranjeet Thakur, Rakesh |
| author_facet | Singh, Jaskaranjeet Thakur, Rakesh |
| contents | Despite rapid advances in large language models (LLMs), low-resource languages remain excluded from NLP, limiting digital access for millions. We present PunGPT2, the first fully open-source Punjabi generative model suite, trained on a 35GB corpus covering literature, religious texts, news, social discourse, etc. PunGPT2 captures Punjabi's syntactic and morphological richness through a tokenizer optimized for Gurmukhi and Shahmukhi scripts. We introduce Pun-RAG, a retrieval-augmented framework integrating PunGPT2 with a FAISS retriever over a curated Punjabi knowledge base, and Pun-Instruct, an instruction-tuned variant using QLoRA for robust zero-shot summarization, translation, and question answering. Our key innovation, Quantum-RAG, fuses sparse, dense, and quantum kernel embeddings for efficient, context-aware retrieval with low memory overhead, marking the first practical quantum-inspired retrieval in a low-resource LLM. Our models outperform multilingual baselines (mBERT, mT5, MuRIL, BLOOM) on FLORES-200, IndicGenBench, and a new PunjabiEval suite. Quantum-RAG yields +7.4 Recall@10 over FAISS and +3.5 BLEU over mT5 on PunjabiEval. We publicly release all training scripts, hyperparameters, evaluation pipelines, the 35GB Punjabi corpus, the PunjabiEval benchmark, and all model weights, establishing new state-of-the-art results for Punjabi language generation and retrieval. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01918 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-RAG and PunGPT2: Advancing Low-Resource Language Generation and Retrieval for the Punjabi Language Singh, Jaskaranjeet Thakur, Rakesh Computation and Language Artificial Intelligence Despite rapid advances in large language models (LLMs), low-resource languages remain excluded from NLP, limiting digital access for millions. We present PunGPT2, the first fully open-source Punjabi generative model suite, trained on a 35GB corpus covering literature, religious texts, news, social discourse, etc. PunGPT2 captures Punjabi's syntactic and morphological richness through a tokenizer optimized for Gurmukhi and Shahmukhi scripts. We introduce Pun-RAG, a retrieval-augmented framework integrating PunGPT2 with a FAISS retriever over a curated Punjabi knowledge base, and Pun-Instruct, an instruction-tuned variant using QLoRA for robust zero-shot summarization, translation, and question answering. Our key innovation, Quantum-RAG, fuses sparse, dense, and quantum kernel embeddings for efficient, context-aware retrieval with low memory overhead, marking the first practical quantum-inspired retrieval in a low-resource LLM. Our models outperform multilingual baselines (mBERT, mT5, MuRIL, BLOOM) on FLORES-200, IndicGenBench, and a new PunjabiEval suite. Quantum-RAG yields +7.4 Recall@10 over FAISS and +3.5 BLEU over mT5 on PunjabiEval. We publicly release all training scripts, hyperparameters, evaluation pipelines, the 35GB Punjabi corpus, the PunjabiEval benchmark, and all model weights, establishing new state-of-the-art results for Punjabi language generation and retrieval. |
| title | Quantum-RAG and PunGPT2: Advancing Low-Resource Language Generation and Retrieval for the Punjabi Language |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.01918 |