AbstentionBench - underspecified context - Musique - Precision: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - Musique - Precision as Abstention Precision (%); 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 Precision (%). Source: github.com. Saturation forecast: Estimated already saturated. 20 models tracked.

Top models

#ModelScoreOverall rank
1Llama 3.1 70B99.41#588
2Llama 3.1 8B99.31#1130
3Llama-3.1-Tulu-3-8B98.73#848
4Llama-3.1-Tulu-3-8B-SFT97.06#977
5Llama-3.1-Tulu-3-8B-DPO95.31#992
6GPT-4o91.68#357
7O191.29#190
8Llama 3.1 70B Instruct90.11#567
9Llama 3.1 405B Instruct89.41#470
10Llama-3.1-Tulu-3-70B-DPO89.24#732
11Llama-3.1-Tulu-3-70B88.97#499
12DeepSeek R1 Distill Llama 70B88.66#614
13Llama 3.3 70B Instruct88.42#541
14Llama 3.1 8B Instruct83.98#1017
15OLMo-7B-0724-Instruct-hf81.98#1441

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