AbstentionBench - underspecified context - SQuAD 2.0 - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - SQuAD 2.0 - 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
1Qwen 2.5 32B Instruct74.99#509
2Llama 3.1 405B Instruct73.35#470
3Llama 3.3 70B Instruct69.68#541
4O169.17#190
5Llama 3.1 70B Instruct63.47#567
6Gemini 1.5 Pro62.9#454
7Mistral 7B Instruct (v0.3)61.26#1255
8Llama-3.1-Tulu-3-70B-DPO59.46#732
9Llama 3.1 8B Instruct58.05#1017
10GPT-4o57.76#357
11Llama-3.1-Tulu-3-70B57.54#499
12Llama 3.1 70B55.11#588
13Llama 3.1 8B52.17#1130
14DeepSeek R1 Distill Llama 70B48.33#614
15OLMo-7B-0724-Instruct-hf42.35#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-squad-2-0-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.