AbstentionBench - underspecified context - BBQ - Precision: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - BBQ - 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
1DeepSeek R1 Distill Llama 70B98.59#614
2Llama 3.3 70B Instruct98#541
3O197.6#190
4Llama 3.1 70B Instruct96.58#567
5Qwen 2.5 32B Instruct96.52#509
6Llama 3.1 405B Instruct96.5#470
7GPT-4o94.07#357
8Llama-3.1-Tulu-3-70B-DPO94.04#732
9Llama-3.1-Tulu-3-70B93.77#499
10Llama 3.1 70B93.28#588
11Llama-3.1-Tulu-3-8B93.24#848
12Llama 3.1 8B91.95#1130
13Gemini 1.5 Pro91.89#454
14Llama-3.1-Tulu-3-8B-DPO91.41#992
15OLMo-7B-0724-Instruct-hf91.3#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-bbq-precision · How It Works · Data refreshed daily, snapshot 2026-11-05.