AbstentionBench - underspecified context - ALCUNA - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - ALCUNA - 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 Instruct84.06#509
2Llama 3.1 8B Instruct78.23#1017
3Llama-3.1-Tulu-3-70B-DPO77.02#732
4Llama-3.1-Tulu-3-70B76.96#499
5Gemini 1.5 Pro74.6#454
6GPT-4o74.54#357
7Llama 3.1 70B71.88#588
8OLMo-7B-0724-Instruct-hf70.73#1441
9Llama 3.3 70B Instruct70.5#541
10Llama 3.1 405B Instruct70.44#470
11Llama 3.1 70B Instruct70.27#567
12Mistral 7B Instruct (v0.3)67.84#1255
13DeepSeek R1 Distill Llama 70B62.93#614
14Llama 3.1 8B53.94#1130
15O153.06#190

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-alcuna-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.