FACTS Grounding
Progress Over Time
Interactive timeline showing model performance evolution on FACTS Grounding
FACTS Grounding Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | — | — | — | |||
| 2 |  Google | — | — | — | ||
| 3 | Google | — | — | — | ||
| 4 | Google | — | — | — | ||
| 4 | Google | — | — | — | ||
| 6 | Google | 12B | — | — | ||
| 7 | Google | 27B | — | — | ||
| 8 | Google | — | — | — | ||
| 9 | Google | 4B | — | — | ||
| 10 | Google | — | 1.0M | $0.50 / $3.00 | ||
| 11 |  Zhipu AI | — | — | — | ||
| 12 | Google | — | 1.0M | $0.25 / $1.50 | ||
| 13 | Google | 1B | — | — |
What is FACTS Grounding?
A benchmark evaluating language models' ability to generate factually accurate and well-grounded responses based on long-form input context, comprising 1,719 examples with documents up to 32k tokens requiring detailed responses that are fully grounded in provided documents
FACTS Grounding is a text benchmark evaluating models on reasoning, factuality, and grounding tasks. LLM Stats tracks 13 models on this benchmark, scored on a 0–1 scale. The current average is 0.7, with the leader at 0.9.
Compare leaders on the best AI for reasoning, best AI for factuality and best AI for grounding leaderboards.
Current leaders
Gemini 2.5 Pro Preview 06-05 from Google currently leads the FACTS Grounding leaderboard with a score of 0.878 across 13 evaluated AI models.
Source paper
- Title
- The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input
- Authors
- Alon Jacovi, Andrew Wang, Chris Alberti, Connie Tao, and 22 others
- Published
- arXiv
- 2501.03200
Abstract
We introduce FACTS Grounding, an online leaderboard and associated benchmark that evaluates language models' ability to generate text that is factually accurate with respect to given context in the user prompt. In our benchmark, each prompt includes a user request and a full document, with a maximum length of 32k tokens, requiring long-form responses. The long-form responses are required to be fully grounded in the provided context document while fulfilling the user request. Models are evaluated using automated judge models in two phases: (1) responses are disqualified if they do not fulfill the user request; (2) they are judged as accurate if the response is fully grounded in the provided document. The automated judge models were comprehensively evaluated against a held-out test-set to pick the best prompt template, and the final factuality score is an aggregate of multiple judge models to mitigate evaluation bias. The FACTS Grounding leaderboard will be actively maintained over time, and contains both public and private splits to allow for external participation while guarding the integrity of the leaderboard. It can be found at https://www.kaggle.com/facts-leaderboard.
FAQ
Common questions about the FACTS Grounding benchmark and leaderboard.