AbstentionBench - underspecified context - BB/Disambiguate - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - BB/Disambiguate - 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
1Llama 3.3 70B Instruct97.33#541
2Llama 3.1 70B Instruct96#567
3Llama 3.1 70B92#588
4GPT-4o89.33#357
5Qwen 2.5 32B Instruct89.33#509
6Llama 3.1 405B Instruct88#470
7O184#190
8Gemini 1.5 Pro82.67#454
9Llama 3.1 8B Instruct78.67#1017
10Llama 3.1 8B40#1130
11Llama-3.1-Tulu-3-70B-DPO34.67#732
12OLMo-7B-0724-Instruct-hf29.33#1441
13DeepSeek R1 Distill Llama 70B26.67#614
14Llama-3.1-Tulu-3-70B21.33#499
15Llama-3.1-Tulu-3-8B20#848

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