用适配领域的基础模型，构建了首个印地语旅游问答系统。
Tourism Question Answer System in Indian Language using Domain-Adapted Foundation Models
- 用Llama生成数据并微调BERT、RoBERTa模型解决低资源问题。
- LoRA微调仅用2%参数就达到85.3% F1，兼顾效率与精度。
- 针对瓦拉纳西文化特色优化，适合文旅NLP研究者参考。
本文首次系统构建面向印地语旅游领域的抽取式问答（QA）基准系统，聚焦文化精神重镇瓦拉纳西的虔诚氛围（Bhakti-Bhaav）。针对十类子领域（恒河夜祭、游船、美食广场、公共厕所、水池、博物馆、通用、修行院、神庙、旅行），构建包含7,715组印地语问答对的数据集，并通过Llama零样本提示扩充至27,455组。提出基于基础模型（BERT、RoBERTa）的框架，采用监督微调（SFT）和低秩适配（LoRA）实现高效参数优化。评估显示，LoRA微调在仅保留2%可训练参数的情况下仍取得85.3% F1，优于传统SFT。对比分析表明，使用SFT的RoBERTa在捕捉文化嵌入术语（如Aarti、Kund）方面表现更佳，验证了其上下文理解优势。该工作为印地语旅游问答建立了基础基准，强调了在低资源场景下结合文化语境的NLP框架重要性。
原文摘要 · Abstract (English)
This article presents the first comprehensive study on designing a baseline extractive question-answering (QA) system for the Hindi tourism domain, with a specialized focus on the Varanasi-a cultural and spiritual hub renowned for its Bhakti-Bhaav (devotional ethos). Targeting ten tourism-centric subdomains-Ganga Aarti, Cruise, Food Court, Public Toilet, Kund, Museum, General, Ashram, Temple and Travel, the work addresses the absence of language-specific QA resources in Hindi for culturally nuanced applications. In this paper, a dataset comprising 7,715 Hindi QA pairs pertaining to Varanasi tourism was constructed and subsequently augmented with 27,455 pairs generated via Llama zero-shot prompting. We propose a framework leveraging foundation models-BERT and RoBERTa, fine-tuned using Supervised Fine-Tuning (SFT) and Low-Rank Adaptation (LoRA), to optimize parameter efficiency and task performance. Multiple variants of BERT, including pre-trained languages (e.g., Hindi-BERT), are evaluated to assess their suitability for low-resource domain-specific QA. Evaluation metrics - F1, BLEU, and ROUGE-L - highlight trade-offs between answer precision and linguistic fluency. Experiments demonstrate that LoRA-based fine-tuning achieves competitive performance (85.3\% F1) while reducing trainable parameters by 98\% compared to SFT, striking a balance between efficiency and accuracy. Comparative analysis across models reveals that RoBERTa with SFT outperforms BERT variants in capturing contextual nuances, particularly for culturally embedded terms (e.g., Aarti, Kund). This work establishes a foundational baseline for Hindi tourism QA systems, emphasizing the role of LORA in low-resource settings and underscoring the need for culturally contextualized NLP frameworks in the tourism domain.
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