RULER 1000K
RULER 1000K evaluates the official 13-task RULER v1 suite at a 1048576-token (1M) context budget.
MiniCPM-SALA from OpenBMB currently leads the RULER 1000K leaderboard with a score of 0.863 across 1 evaluated AI models.
What RULER 1000K measures
RULER 1000K is a text benchmark that evaluates large language models on long context and reasoning tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.9, with the leader reaching 0.9.
Compare leaders on the best AI for long context and best AI for reasoning leaderboards.
Publication
- Paper
- RULER: What's the Real Context Size of Your Long-Context Language Models?
- Authors
- Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, and 4 others
- Published
- arXiv
- 2404.06654
Abstract
The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the "needle") from long distractor texts (the "haystack"), has been widely adopted to evaluate long-context language models (LMs). However, this simple retrieval-based test is indicative of only a superficial form of long-context understanding. To provide a more comprehensive evaluation of long-context LMs, we create a new synthetic benchmark RULER with flexible configurations for customized sequence length and task complexity. RULER expands upon the vanilla NIAH test to encompass variations with diverse types and quantities of needles. Moreover, RULER introduces new task categories multi-hop tracing and aggregation to test behaviors beyond searching from context. We evaluate 17 long-context LMs with 13 representative tasks in RULER. Despite achieving nearly perfect accuracy in the vanilla NIAH test, almost all models exhibit large performance drops as the context length increases. While these models all claim context sizes of 32K tokens or greater, only half of them can maintain satisfactory performance at the length of 32K. Our analysis of Yi-34B, which supports context length of 200K, reveals large room for improvement as we increase input length and task complexity. We open source RULER to spur comprehensive evaluation of long-context LMs.
MiniCPM-SALA leads with 86.3%.
Progress Over Time
Interactive timeline showing model performance evolution on RULER 1000K
RULER 1000K Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  OpenBMB | 9B | — | — |
FAQ
Common questions about RULER 1000K.
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