Hindi Tourism QA System

• Hindi Tourism QA System is an automated framework that uses culturally sensitive, domain-adapted language models and curated datasets to answer tourism queries in Hindi.
• It integrates synthetic data generation, parameter-efficient LoRA tuning, and multilingual capabilities to deliver robust extractive and generative answers.
• System evaluations demonstrate high performance through staged training and quality control, ensuring practical application in low-resource, culturally nuanced tourism contexts.

A Hindi Tourism QA (Question Answering) System is an automated framework that addresses tourism-related queries in Hindi, leveraging domain-adapted LLMs and curated datasets to provide accurate, contextually grounded answers. These systems integrate advances in multilingual comprehension, synthetic data generation, parameter-efficient fine-tuning, and culturally sensitive model selection to meet the requirements of low-resource domains such as Indian tourism. Systems extensively evaluated for Varanasi and broader tourism contexts demonstrate robust extractive and generative QA capabilities in both monolingual and cross-lingual (English-Hindi) settings (Gupta et al., 2020, Majhi et al., 29 Oct 2025, Gatla et al., 28 Nov 2025).

1. Dataset Construction and Augmentation

System efficacy critically depends on the availability of high-quality, domain-representative QA corpora. Multiple research efforts (Gatla et al., 28 Nov 2025, Majhi et al., 29 Oct 2025, Gupta et al., 2020) report two principal strategies:

• Manual Annotation: Expert annotators create Hindi QA pairs by reading tourism contexts and devising 2–3 factoid or descriptive questions per passage, typically focusing on relevant subdomains (e.g., Ganga Aarti, temples, logistics). For the VATIKA dataset, 13,092 QA pairs were derived from 5,244 contexts, while Varanasi-specific efforts yielded 7,715 pairs across ten subdomains.
• Synthetic Generation: Large LLMs (LLaMA-70B, Phi-14B) generate additional QA pairs via few-shot prompting. Typical scale: Phi-14B produced 33,000 pairs, LLaMA-70B generated 4,000 pairs, augmenting original datasets up to 50,000 QA instances (Majhi et al., 29 Oct 2025).
• Quality Control: Filtering criteria include ≥60% answer–context token overlap, maximum cosine-similarity <0.8 between questions for diversity, and deduplication. Manual spot-checking maintains >90% fluency and relevance (Gatla et al., 28 Nov 2025).
• Format: SQuAD v1.1 style JSON with explicit context, contiguous answer span, and subdomain tags is standard for extractive QA (Gupta et al., 2020).

Table 1. Sub-domain-wise Manual and Augmented QA Counts (Gatla et al., 28 Nov 2025)

Sub-domain	Manual Pairs	Llama-Augmented
Temples	2,686	11,691
Kunds	470	2,398
Ashrams	1,555	5,284
Museums	484	1,039
Travel Agencies	2,413	6,828

This distribution ensures comprehensive coverage of region-specific tourism topics.

2. Model Architectures and Domain Adaptation

Hindi tourism QA systems employ encoder-only Transformer architectures optimized for low-resource adaptation:

• Foundation Models: mBERT (bert-base-multilingual-cased), IndicBERT, Hindi-BERT (l3cube-pune/hindi-bert-v2), and Hindi-RoBERTa (l3cube-pune/hindi-roberta) are primary choices, with Hindi-specific models consistently outperforming multilingual baselines (Gatla et al., 28 Nov 2025).
• Input Representation: Question $Q$ and passage context $C$ are concatenated: $X=[\textsc{CLS};Q;\textsc{SEP};C;\textsc{SEP}]$, then tokenized and processed by the model (Gatla et al., 28 Nov 2025, Gupta et al., 2020).
• Output Layer: For extractive QA, two linear heads predict answer start and end positions: $P_s=\mathrm{softmax}(H\,W_s)$, $P_e=\mathrm{softmax}(H\,W_e)$, with $H$ as hidden embeddings (Gatla et al., 28 Nov 2025, Gupta et al., 2020).
• Parameter-Efficient Tuning: LoRA (Low-Rank Adaptation) injects low-rank updates $\Delta W=B\,A^\top$ into each Transformer weight, allowing only 1–2% of parameters to be updated while achieving near-parity with full supervised fine-tuning (SFT); for BERT, LoRA (rank $r=4$) raises F1 from 79.8% (LoRA) to 82.3% (SFT) (Gatla et al., 28 Nov 2025).
• Cross-Lingual Extension: mBERT supports monolingual (HI→HI), cross-lingual (EN→HI, HI→EN), and zero-shot paradigms, enabling broad language coverage (Gupta et al., 2020).

3. Training Procedures and Optimization Strategies

Standardized training protocols allow reproducibility and transferability across domains:

• Curriculum Learning (mBERT):
  • Stage 1: Zero-shot fine-tuning on SQuAD v1.1 (EN, 150k QAs).
  • Stage 2: Fine-tune on HI→HI tourism QA.
  • Stage 3: Fine-tune on EN→HI tourism QA.
  • Hyperparameters: learning rate $5\times10^{-5}$, AdamW optimizer $$(\beta_1=0.9, \beta_2=0.999, \epsilon=1\text{e}{-6})$$, max sequence length 384, batch size 12 (train), epochs 3, gradient clipping norm ≤1.0 (Gupta et al., 2020).
• Small LM Fine-Tuning (LLaMA-3.1-8B):
  • Baseline (M1): 4 epochs on original QA.
  • Continued (M2): 2 epochs original, 2 epochs synthetic pairs.
  • Multi-Source (M3): 4 epochs on combined original and synthetic.
  • Shared hyperparameters: sequence length 4096, per-device batch size 2, gradient accumulation 4, learning rate $1\times10^{-5}$, token-level cross-entropy loss (Majhi et al., 29 Oct 2025).
• LoRA Adaptation: Adapter rank $r\in\{2,4,8,16,32\}$, dropout 0.1, no bias adaptation, enabling ∼98% reduction in trainable parameters (Gatla et al., 28 Nov 2025).

4. Evaluation Metrics and Comparative Results

Evaluation combines span-level accuracy and natural language generation metrics:

• Token-level F1: $$F_1 = \frac{2 \cdot |A\cap\hat{A}|}{|A| + |\hat{A}|}$$
• Exact Match (EM): Fraction of answers matching gold spans exactly.
• BLEU-N: $$\mathrm{BLEU} = \exp\left(\min(0,1-\tfrac{r}{c})\right)\prod_{n=1}^N p_n^{w_n}$$
• ROUGE-L: Based on longest common subsequence, $$\mathrm{ROUGE\text{-}L}=\frac{(1+\beta^2)R_LP_L}{R_L+\beta^2P_L}$$
• BERTScore and QA-F1: Used for abstractive, long-form answers (Majhi et al., 29 Oct 2025, Gatla et al., 28 Nov 2025, Gupta et al., 2020).

Table 2. Hindi Tourism QA: Model × Tuning Comparative Performance (Gatla et al., 28 Nov 2025)

Model	Tuning	F1 (%)	BLEU	ROUGE-L
mBERT	SFT	82.3	45.2	31.7
mBERT	LoRA (r=4)	79.8	42.9	30.1
Hindi-BERT	SFT	87.1	52.6	36.4
Hindi-RoBERTa	SFT	89.7	58.3	39.8
Hindi-RoBERTa	LoRA (r=4)	85.3	54.1	35.2

RoBERTa SFT exhibits the highest overall performance, with LoRA enabling resource-efficient deployment with minimal performance degradation.

5. Domain Adaptation and Data Augmentation

Domain-adaptive techniques counteract linguistic scarcity and ensure model grounding:

• Continued MLM Pre-training: MLM on 100k–1M tourism-specific sentences, masking 15% tokens for 100k steps, aligns representation spaces to domain vocabulary (e.g., “heritage”, “Darjeeling”, “चौमुखा”) (Gupta et al., 2020).
• Entity Handling: For OOV named entities, expand the tokenizer vocabulary and initialize embeddings from similar word averages; auxiliary “in-gazetteer” binary features enhance entity recognition (Gupta et al., 2020).
• Synthetic Augmentation: Zero-shot Llama prompting and back-translation broaden linguistic variety, paraphrasing, and cultural coverage (Gatla et al., 28 Nov 2025).
• Sampling: Stratified sampling ensures balanced coverage across subdomains.

6. Error Analysis, Generalization, and Best Practices

Empirical findings underscore the following:

• Synthetic Data Risks: Contradictory synthetic answers degrade BLEU-2 on held-out sets, causing hallucination in absence of explicit context (Majhi et al., 29 Oct 2025).
• Staged Training: Introducing synthetic QA examples after stabilizing on original data (M2) achieves the best robustness; direct multi-source (M3) improves in-domain but hurts generalization (Majhi et al., 29 Oct 2025).
• Cultural Nuance: Hindi-specific pretrained models outperform mBERT for culturally loaded terms (“Aarti”, “Kund”), establishing the importance of domain and language adaptation (Gatla et al., 28 Nov 2025).
• Efficiency: LoRA-based adaptation enables fine-tuning on commodity hardware, reducing parameter updates by >98% with only minor trade-offs in F1 (Gatla et al., 28 Nov 2025).
• Future Improvements: Retrieval-Augmented Generation (RAG), dataset expansion to multilingual settings, and user feedback loops are proposed directions (Gatla et al., 28 Nov 2025).

7. System Deployment and Maintenance

Deployment considerations are informed by empirical insights:

• Resource Requirements: LLaMA-8B models with 4k-token context are deployable on 48GB GPUs for sub-second inference (Majhi et al., 29 Oct 2025).
• Maintenance: Periodic regeneration of synthetic QA corpora and lightweight re-finetuning with updated LLMs ensure continual coverage of emerging tourism FAQs (Majhi et al., 29 Oct 2025).
• Pipeline Replicability: The workflow of synthetic QA generation followed by staged small-LM adaptation generalizes to other low-resource domains (e.g., healthcare, agriculture) (Majhi et al., 29 Oct 2025).

In summary, Hindi Tourism QA systems realize scalable, domain-sensitive, and computationally efficient automated answering for tourism in culturally nuanced settings, relying on an overview of curated and synthetic datasets, language-specific foundation models, and modern adaptation protocols (Gupta et al., 2020, Majhi et al., 29 Oct 2025, Gatla et al., 28 Nov 2025).