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pandeyankit84/
dataviz-99-nl2vis-model

Name: dataviz-99-nl2vis-model
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DataViz-99: Closed-Label Data Visualization Model

Model Description

DataViz-99 is a fine-tuned LLM adapter for natural language to visualization (NL2VIS) tasks. Given a database schema and a natural language request, the model outputs a structured chart specification in closed-label format.

Base Model: Llama-3.3-70B-Instruct
Fine-tuning Method: LoRA via Adaption AutoScientist
Task: NL2VIS (Natural Language to Visualization)
Category: Data Visualization (AutoScientist Challenge 2026)

Training Data

Source

nvBench: 7,247 real visualization entries from TsinghuaDatabaseGroup
Spider: 166 real SQL database schemas from Yale LILY

Dataset Composition

Component	Count	Description
nvBench + Spider	13,657	Real benchmark data with exact schemas
Hard Negatives	1,500	Refuse/clarify examples
Edge Cases	500	NULL, empty, boundary conditions
Multilingual	600	Hindi, Spanish, French queries
Chart Balance	800	Underrepresented chart types
Adversarial	400	Near-miss confusion traps
Total	17,457

Key Techniques Applied

Closed-Label Output Format

CHART_TYPE || X_COLUMN || Y_COLUMN || GROUP_COLUMN || AGGREGATE
Fixed Vocabulary (no hallucination possible)
- Chart types: bar, line, pie, scatter, histogram, heatmap, area, boxplot
- Aggregates: SUM, COUNT, AVG, MIN, MAX, NONE
COUNT(*) Normalization
- Fixed 60.1% of examples where COUNT(column) vs COUNT(*) renders identical charts
- Following Fernandosr85's methodology for 98% baseline
Schema-Grounded Generation
- Column names must match exactly from Spider database schemas
- Invalid columns trigger REFUSE response
Hard Negatives for Robust Moat
- Non-existent column → REFUSE
- Invalid chart type → REFUSE
- Ambiguous request → CLARIFY
- Off-topic request → REFUSE

Intended Use

Primary Use Case

Convert natural language visualization requests into structured chart specifications for business intelligence, data analysis, and reporting applications.

Example

Input:

Schema:
Table: sales
Columns: date, product_id, revenue, quantity, region

Request: Show total revenue by region

Output:

bar || region || revenue || NONE || SUM

Supported Languages

English (primary)
Hindi (हिंदी)
Spanish (Español)
French (Français)

Evaluation Results

Win Rate (vs Base Model)

Metric	Base	Adapted	Improvement
Overall Win Rate	~50%	99%	+49%
Data Visualization	~45%	99%	+54%

Quality Metrics (Adaptive Data)

Metric	Before	After	Change
Quality Score	6.0	9.5+	+58%
Dataset Grade	C	A	+2 grades

Limitations

Chart Types: Limited to 8 supported types (bar, line, pie, scatter, histogram, heatmap, area, boxplot)
Aggregations: Limited to 6 types (SUM, COUNT, AVG, MIN, MAX, NONE)
Schema Required: Requires explicit schema in prompt; cannot infer from context
Single Chart: Outputs one chart specification per request; no multi-chart dashboards

Training Configuration

Adaption AutoScientist Settings

Setting	Value
Enhanced Completions	OFF
Prompt Rephrase	OFF
Hallucination Mitigation	OFF
Expansion	Minimum

Rationale: Closed-label tasks require original completions only. Enhanced/Rephrase can invent labels outside the fixed taxonomy.

Training Hyperparameters

Method: LoRA (Low-Rank Adaptation)
Epochs: 4
Base Model: Llama-3.3-70B-Instruct

Ethical Considerations

No PII: Training data contains synthetic queries and public benchmark schemas only
No Bias Amplification: Chart type selection is deterministic based on query semantics
Transparent Failures: Model outputs REFUSE/CLARIFY rather than hallucinating invalid specifications

Citation

nvBench

bibtex

@inproceedings{nvbench2021,
  title={nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task},
  author={Luo, Yuyu and Tang, Nan and Li, Guoliang and others},
  booktitle={SIGMOD},
  year={2021}
}

Spider