AI Research Lab · Project
LLM Evaluation Benchmarks
You can't improve what you can't measure.
Overview
We design rigorous, reproducible benchmarks — including Indian-language and real-world task suites — to evaluate LLM capability, safety, and efficiency. Good benchmarks are the yardsticks the whole field needs to make honest progress.
Objectives
- Capability coverageReasoning, coding, knowledge, and language tasks.
- Indian-language evalFirst-class evaluation for Indian languages.
- Safety & robustnessTest for harmful, biased, or brittle behavior.
- EfficiencyMeasure speed, memory, and cost — not just accuracy.
Our Approach
- Curated datasetsHigh-quality, well-documented task sets.
- Transparent metricsClear, reproducible scoring methodology.
- Contamination checksGuard against train/test leakage.
- LeaderboardTrack internal models over time.
Focus & Tech
DatasetsMetricsIndian-language EvalReproducibilityLeaderboard
Status: Defining task suites & scoring
Interested in this work?
We collaborate on open benchmarks and evaluation.