MiniMaxAI/MR-NIAH
Multi-Round Needles-In-A-Haystack (MR-NIAH) Evaluation Overview Multi-Round Needles-In-A-Haystack (MR-NIAH) is an evaluation framework designed to assess long-context retrieval performance in large language models (LLMs). It serves as a crucial benchmark for retrieval tasks in long multi-turn dialogue contexts, revealing fundamental capabilities necessary for building lifelong companion AI assistants. MR-NIAH extends the vanilla k-M NIAH (Kamradt, 2023) by… See the full description on the dataset page: https://huggingface.co/datasets/MiniMaxAI/MR-NIAH.
| Author / organization | MiniMaxAI |
|---|---|
| License (as declared) | mit |
| Downloads | 175 (as reported by Hugging Face) |
| Likes | 9 |
| Created | 6 March 2025 |
| Last updated at source | 8 March 2025 |
Recent changes
- 16 Oct 2026 Description: Multi-Round Needles-In-A-Haystack (MR-NIAH) Evaluation Overview Multi-Round Needles-In-A-Haystack (MR-NIAH) is an evaluation framework designed to assess long-context retrieval performance in large language models (LLMs). It serves as a cru → Multi-Round Needles-In-A-Haystack (MR-NIAH) Evaluation Overview Multi-Round Needles-In-A-Haystack (MR-NIAH) is an evaluation framework designed to assess long-context retrieval performance in large language models (LLMs). It serves as a cru
Changes detected by Silytics between refreshes of the source.
More from MiniMaxAI
- MiniMaxAI/TTS-Multilingual-Test-SetDataset
- MiniMaxAI/SynLogicDataset
- MiniMaxAI/role-play-benchDataset
- MiniMaxAI/OctoCodingBenchDataset
- MiniMaxAI/VIBEDataset
- MiniMaxAI/OctoBenchDataset
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 16 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.