AbstentionBench - underspecified context - MediQ - F1: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - MediQ - F1 as Abstention F1 (%); 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 F1 (%). Source: github.com. Saturation forecast: Estimated already saturated. 20 models tracked.

Top models

#ModelScoreOverall rank
1Mistral 7B Instruct (v0.3)57.82#1255
2Qwen 2.5 32B Instruct43.25#509
3Gemini 1.5 Pro40.32#454
4GPT-4o37.63#357
5Llama 3.1 405B Instruct35.83#470
6Llama 3.3 70B Instruct32.27#541
7Llama 3.1 70B Instruct30.51#567
8Llama 3.1 8B Instruct23.47#1017
9Llama 3.1 8B22.96#1130
10OLMo-7B-0724-Instruct-hf18.3#1441
11O117.35#190
12Llama-3.1-Tulu-3-70B-DPO17.22#732
13Llama 3.1 70B14.35#588
14Llama-3.1-Tulu-3-70B14.11#499
15DeepSeek R1 Distill Llama 70B6.98#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-underspecified-context-mediq-f1 · How It Works · Data refreshed daily, snapshot 2026-11-05.