AbstentionBench - underspecified context - MMLU History - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - MMLU History - Recall as Abstention Recall (%); values converted from 0-1 to percent. Precision and recall must be read together: always abstaining is not necessarily useful. Scenario/dataset pairs and SFT/DPO/RL checkpoints remain separate; no synthetic overall average. Source: facebookresearch/AbstentionBench analysis/abstention_performance.csv.

Metric: Abstention Recall (%). Source: github.com. Saturation forecast: Estimated already saturated. 21 models tracked.

Top models

#ModelScoreOverall rank
1Gemini 1.5 Pro58.97#454
2GPT-4o51.28#357
3Qwen 2.5 32B Instruct48.72#509
4O143.59#190
5Llama-3.1-Tulu-3-70B-DPO41.03#732
6Llama 3.1 8B Instruct38.46#1017
7Llama 3.1 70B Instruct35.9#567
8Llama-3.1-Tulu-3-70B35.9#499
9O1 (High)35.9#190 (O1)
10Llama 3.1 405B Instruct33.33#470
11TinyLlama-1.1B-Chat-v1.023.08#1547
12Llama 3.3 70B Instruct20.51#541
13OLMo-7B-0724-Instruct-hf17.95#1441
14Llama-3.1-Tulu-3-8B15.38#848
15Llama-3.1-Tulu-3-8B-DPO15.38#992

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=abstentionbench-underspecified-context-mmlu-history-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.