AbstentionBench - subjective - CoCoNot/Humanizing - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports subjective - CoCoNot/Humanizing - 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-3.1-Tulu-3-8B-SFT97.56#977
2O196.34#190
3Llama-3.1-Tulu-3-8B-DPO93.9#992
4Llama-3.1-Tulu-3-70B-DPO93.9#732
5Llama 3.1 8B Instruct92.68#1017
6Llama 3.3 70B Instruct92.68#541
7Llama 3.1 70B Instruct91.46#567
8Llama-3.1-Tulu-3-70B91.46#499
9Llama-3.1-Tulu-3-8B90.24#848
10GPT-4o89.02#357
11Gemini 1.5 Pro89.02#454
12Llama 3.1 405B Instruct89.02#470
13Mistral 7B Instruct (v0.3)85.37#1255
14Qwen 2.5 32B Instruct82.93#509
15DeepSeek R1 Distill Llama 70B63.41#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-subjective-coconot-humanizing-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.