AbstentionBench - underspecified context - Musique - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - Musique - 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. 20 models tracked.

Top models

#ModelScoreOverall rank
1Qwen 2.5 32B Instruct87.08#509
2Mistral 7B Instruct (v0.3)86.4#1255
3Gemini 1.5 Pro83.51#454
4Llama 3.1 8B Instruct80.85#1017
5Llama 3.3 70B Instruct78.34#541
6Llama 3.1 405B Instruct76.98#470
7Llama 3.1 70B Instruct73.4#567
8GPT-4o62.77#357
9Llama 3.1 8B54.71#1130
10Llama-3.1-Tulu-3-70B-DPO54.18#732
11Llama 3.1 70B51.52#588
12O148.56#190
13Llama-3.1-Tulu-3-70B47.19#499
14OLMo-7B-0724-Instruct-hf42.17#1441
15DeepSeek R1 Distill Llama 70B36.25#614

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-musique-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.