VAANI: Cultural Grounding in Vision–Language AI

• VAANI for Cultural Grounding is a dataset and evaluation paradigm that anchors multimodal models in authentic, culturally rich Indian contexts.
• It employs a rigorous pipeline including native sourcing, automated multi-choice question generation, and human verification to ensure cultural coherence.
• Cross-lingual experiments show VAANI minimizes accuracy gaps between Hindi/Telugu and English, highlighting its role in robust cultural discrimination.

VAANI (Voice of All India) for cultural grounding refers to a class of datasets, evaluation paradigms, and model adaptation strategies designed to probe and enhance the ability of vision–LLMs (VLMs) to recognize, reason about, and operationalize culture-specific content in both visual and conversational domains. VAANI's key contribution is anchoring the evaluation of multimodal AI in authentic, regionally sourced cultural artifacts, customs, and dialogues, with a specific focus on India’s linguistic diversity and cultural heterogeneity. VAANI has gained prominence as the “cultural grounding” split in multilingual vision–language benchmarks such as HinTel-AlignBench and is central to emerging paradigms in cultural reasoning, VQA, and dialogue generation (Chigrupaatii et al., 19 Nov 2025).

1. Dataset Design and Construction

The VAANI dataset originates as a large-scale image–caption corpus capturing region-specific scenes, festivals, handicrafts, and daily rituals spanning Indian culture (Chigrupaatii et al., 19 Nov 2025). In HinTel-AlignBench, two primary VAANI splits are introduced:

• VAANI-H: Images with Hindi transcriptions, 945 multi-choice QA pairs
• VAANI-T: Images with Telugu transcriptions, 1,020 multi-choice QA pairs

The dataset employs a semi-automated pipeline for multi-choice QA construction:

1. Native Sourcing: Only images natively captioned in Hindi or Telugu are selected.
2. Question Generation: GPT-4.1 is prompted to produce one visual-context-dependent multiple-choice question per caption, with four options.
3. Automated Filtering: Candidates solvable using the caption alone (i.e., lacking visual grounding) are filtered out via model prompts.
4. Human Verification: Native speakers review all items for (a) visual grounding (requiring inspection of the image), (b) fluency, and (c) cultural coherence.

No numeric similarity thresholds are reported; filtering is entirely model-assisted and manually verified. All questions are multiple-choice format with exactly one correct answer and three plausible, culturally registered distractors.

2. Task Formulation and Evaluation Protocols

The VAANI cultural grounding task is strictly defined as follows (Chigrupaatii et al., 19 Nov 2025):

• Input: An image $I$ and a multiple-choice question $Q$ (in Hindi or Telugu)
• Output: Predicted answer index $\hat{y} \in \{1,2,3,4\}$
• Objective: Correct prediction requires the model to identify region-specific visual cues, not just object category or caption paraphrase
• Metric:

$$\mathrm{Accuracy} = \frac{1}{N} \sum_{i=1}^N \mathbf{1}[\hat{y}_i = y_i]$$

No partial credit or F1 computation is used, as each item is single-label.

Failure modes, as identified in large-scale evaluation (GPT-4.1 on VAANI-T), cluster as follows:

• Lack of knowledge about region-specific cultural facts ($\sim 17\%$)
• Visual grounding error—failure to map question vocabulary to salient image regions ($\sim 49\%$)
• Visual perception failure ($\sim 19\%$)
• Misattribution of cultural meaning ($\sim 15\%$)

A crucial empirical result is that nearly half of errors stem from visual grounding rather than knowledge or comprehension (Chigrupaatii et al., 19 Nov 2025).

3. Comparative Performance and Cross-Lingual Trends

VAANI supports direct comparison between Hindi/Telugu and English-aligned QA performance:

• VAANI-H: Hindi 85.85% vs. English 83.76% (+2.09pt gain in Hindi)
• VAANI-T: Telugu 80.53% vs. English 80.98% (–0.45pt drop in Telugu)

These cross-lingual gaps are notably smaller than in other VQA domains (e.g., VQAv2), where 5–20 point accuracy drops are typical moving from English to Indian languages. This suggests that visually-grounded cultural discrimination is relatively robust to target language in VAANI, possibly due to shallow distractor generation and the use of natural, culturally fluent Hindi/Telugu phrasings (Chigrupaatii et al., 19 Nov 2025).

4. Connections to Benchmarking Frameworks and Model Requirements

Within broader vision–language benchmarking, VAANI fills a unique role as a human-verified, natively sourced, and regionally exhaustive cultural intelligence measure (Chigrupaatii et al., 19 Nov 2025):

• Contrasts with datasets reliant on automatic translation or non-native templates
• Pushes models to reason about nuanced traditions, attire, artifacts, and ritual contexts rather than generic visual entities

In parallel, the Seeing Culture Benchmark (SCB) introduces a two-stage task—(1) MCQ VQA and (2) spatial grounding via segmentation—across culturally diverse Southeast Asian artifacts. Integration of VAANI-style reasoning into models for SCB requires a dual-headed architecture, multi-task VQA and segmentation loss, and targeted data augmentation (Satar et al., 20 Sep 2025).

Dataset	Language(s)	Modality	#QA Pairs (per lang.)	Task Type
VAANI-H	Hindi	Vision/Text	945	Cultural MCQ VQA
VAANI-T	Telugu	Vision/Text	1,020	Cultural MCQ VQA
SCB	English	Vision/Text	3,178	2-stage VQA + Segmentation Grounding

5. Model Architecture and Adaptation for Cultural Grounding

Direct implementation of VAANI-style cultural grounding in VLMs necessitates modifications aligned with findings from both evaluation and the SCB integration recipe (Satar et al., 20 Sep 2025, Chigrupaatii et al., 19 Nov 2025):

• Visual Encoder: Pre-trained vision backbone (e.g., ViT, ConvNet)
• Language Encoder/Decoder: Transformer architecture, multilingual if possible (e.g., mT5, mBART for dialogue)
• Cultural Feature Fusion:
  • Option 1: Fuse categorical/country/region embedding into vision-language fusion layers (using prompt, adapter, or vector fusion of country–concept taxonomy)
  • Option 2: Linear projection of human- or survey-derived culture vectors (e.g., Hofstede embedding for dialogue agents (Cao et al., 18 Jan 2024))
• Dual-Headed Output:
  • MCQ Classifier: Cross-entropy head for VQA
  • Segmentation Decoder: Pixel- or polygon-level mask via IoU-based or Dice loss
• Loss Function:

  $$L_{\text{total}} = \alpha L_{\text{VQA}} + \beta L_{\text{IoU}}$$

  $\alpha,\beta$ weighting as per task priority

For multi-stage or sequential training, models may be pretrained for VQA, then fine-tuned on segmentation, or optimized jointly on all tuples where the VQA prediction is correct. Data augmentation strategies include oversampling long-tail (rare country-category) samples, artifact-preserving geometric transforms, and style transfer.

Editor's term: "Cultural Feature Fusion"—incorporating explicit metadata (country/culture embeddings) or continuous culture survey scores into the multimodal reasoning pipeline, crucial for disambiguating closely related cultural artifacts.

6. Extensions to Dialogue and Norm Discovery

VAANI’s paradigm extends beyond static VQA to dialogue and dynamic conversational norm induction:

• cuDialog incorporates per-conversation cultural label and quantitative vectors (Hofstede’s 6D values), using encoder–decoder architectures with cultural feature fusion at every decoding step. This leads to increased contextually appropriate, culturally distinctive dialogue responses, as measured by BLEU, ROUGE, BERTScore, and distinctiveness metrics (Cao et al., 18 Jan 2024).
• NormSAGE enables on-the-fly discovery and verification of socio-cultural norms from dialogue, integrating corrective and grounding self-verification mechanisms. These support adaptation to new linguistic/cultural domains and transparent reasoning about observed/violated norms (Fung et al., 2022).

These modular, prompting-based frameworks can be combined with VAANI as either dialogue agents (cuDialog+VAANI) or as norm retrieval/violation detection components (NormSAGE+VAANI), furthering real-time cultural alignment in both visual and conversational AI systems.

7. Challenges and Future Directions

Salient technical challenges identified by VAANI authors and related benchmarks include:

• Visual Grounding: Substantial proportion of errors in models such as GPT-4.1 on VAANI derive from the inability to correctly associate culturally salient question terms with specific image regions (Chigrupaatii et al., 19 Nov 2025).
• Distractor Design: The shallow quality of model-generated distractors may not penalize superficial statistical guessing, suggesting the need for more adversarial, human-curated alternatives.
• Underrepresented Classes: Long-tail artifacts and low-resource linguistic/cultural groupings remain weak points, indicating a requirement for balanced sampling and augmentation.
• Cultural Fusion Generality: While linear or prompt-based cultural feature fusion is effective, richer, structure-aware representations (country–concept taxonomy, fine-grained domain labels) and norm retrieval modules remain open directions.

Ongoing work aims to refine dataset difficulty, annotation specificity, distractor diversity, and multidomain metric coverage (MCQ, grounding, dialogue, and norm adherence). A plausible implication is that broadening VAANI toward multi-stage cultural grounding and norm-aware dialogue will yield more robust, interpretable, and culturally sensitive multimodal AI systems.

---

Key references:

• "HinTel-AlignBench: A Framework and Benchmark for Hindi-Telugu with English-Aligned Samples" (Chigrupaatii et al., 19 Nov 2025)
• "Seeing Culture: A Benchmark for Visual Reasoning and Grounding" (Satar et al., 20 Sep 2025)
• "Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys" (Cao et al., 18 Jan 2024)
• "NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly" (Fung et al., 2022)