COLLIE
Progress Over Time
Interactive timeline showing model performance evolution on COLLIE
COLLIE Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  OpenAI | — | — | — | ||
| 2 | OpenAI | — | — | — | ||
| 3 | OpenAI | — | — | — | ||
| 4 |  Mistral AI | 128B | 256K | $1.50 / $7.50 | ||
| 5 | OpenAI | — | — | — | ||
| 6 | OpenAI | — | 1.0M | $2.00 / $8.00 | ||
| 7 | Mistral AI | 119B | 256K | $0.15 / $0.60 | ||
| 8 | OpenAI | — | 128K | $2.50 / $10.00 | ||
| 9 | OpenAI | — | 1.0M | $0.40 / $1.60 | ||
| 10 | OpenAI | — | 1.0M | $0.10 / $0.40 |
What is COLLIE?
COLLIE is a grammar-based framework for systematic construction of constrained text generation tasks. It allows specification of rich, compositional constraints across diverse generation levels and modeling challenges including language understanding, logical reasoning, and semantic planning. The COLLIE-v1 dataset contains 2,080 instances across 13 constraint structures.
COLLIE is a text benchmark evaluating models on reasoning, language, and writing tasks. LLM Stats tracks 10 models on this benchmark, scored on a 0–1 scale. The current average is 0.8, with the leader at 1.0.
Compare leaders on the best AI for reasoning, best AI for language and best AI for writing leaderboards.
Current leaders
GPT-5 from OpenAI currently leads the COLLIE leaderboard with a score of 0.990 across 10 evaluated AI models.
Source paper
- Title
- COLLIE: Systematic Construction of Constrained Text Generation Tasks
- Authors
- Shunyu Yao, Howard Chen, Austin W. Hanjie, Runzhe Yang, and 1 others
- Published
- arXiv
- 2307.08689
Abstract
Text generation under constraints have seen increasing interests in natural language processing, especially with the rapidly improving capabilities of large language models. However, existing benchmarks for constrained generation usually focus on fixed constraint types (e.g.,generate a sentence containing certain words) that have proved to be easy for state-of-the-art models like GPT-4. We present COLLIE, a grammar-based framework that allows the specification of rich, compositional constraints with diverse generation levels (word, sentence, paragraph, passage) and modeling challenges (e.g.,language understanding, logical reasoning, counting, semantic planning). We also develop tools for automatic extraction of task instances given a constraint structure and a raw text corpus. Using COLLIE, we compile the COLLIE-v1 dataset with 2080 instances comprising 13 constraint structures. We perform systematic experiments across five state-of-the-art instruction-tuned language models and analyze their performances to reveal shortcomings. COLLIE is designed to be extensible and lightweight, and we hope the community finds it useful to develop more complex constraints and evaluations in the future.
FAQ
Common questions about the COLLIE benchmark and leaderboard.