AbstentionBench - underspecified intent - KUQ/Ambiguous - F1: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified intent - KUQ/Ambiguous - 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
1GPT-4o76.72#357
2Gemini 1.5 Pro74.88#454
3O172.07#190
4Llama-3.1-Tulu-3-70B-DPO70.12#732
5Llama-3.1-Tulu-3-70B69.7#499
6Qwen 2.5 32B Instruct69.52#509
7Llama 3.3 70B Instruct69.47#541
8Llama-3.1-Tulu-3-8B-DPO68.74#992
9Llama-3.1-Tulu-3-8B68.67#848
10Mistral 7B Instruct (v0.3)66.55#1255
11Llama 3.1 70B Instruct65.3#567
12Llama-3.1-Tulu-3-8B-SFT64.71#977
13Llama 3.1 405B Instruct64.18#470
14Llama 3.1 8B Instruct61.3#1017
15OLMo-7B-0724-Instruct-hf60.45#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-intent-kuq-ambiguous-f1 · How It Works · Data refreshed daily, snapshot 2026-11-05.