AbstentionBench - underspecified context - UMWP - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - UMWP - 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. 19 models tracked.

Top models

#ModelScoreOverall rank
1O176.11#190
2Llama 3.1 70B Instruct70.63#567
3Gemini 1.5 Pro68.23#454
4Qwen 2.5 32B Instruct66.23#509
5GPT-4o66.17#357
6Llama 3.1 8B Instruct61.66#1017
7Mistral 7B Instruct (v0.3)60.63#1255
8DeepSeek R1 Distill Llama 70B56.69#614
9Llama 3.1 70B54.15#588
10Llama 3.3 70B Instruct53.37#541
11Llama 3.1 8B50.6#1130
12Llama-3.1-Tulu-3-70B49.83#499
13Llama-3.1-Tulu-3-70B-DPO49.14#732
14Llama-3.1-Tulu-3-8B-DPO31.77#992
15Llama-3.1-Tulu-3-8B31.6#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-umwp-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.