AbstentionBench - false premise - CoCoNot/False presumptions - Recall: leaderboard
AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports false premise - CoCoNot/False presumptions - 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
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | O1 | 87.95 | #190 |
| 2 | Llama-3.1-Tulu-3-70B | 80.72 | #499 |
| 3 | Llama 3.1 405B Instruct | 79.52 | #470 |
| 4 | Llama-3.1-Tulu-3-70B-DPO | 79.52 | #732 |
| 5 | Llama-3.1-Tulu-3-8B-DPO | 75.9 | #992 |
| 6 | GPT-4o | 74.7 | #357 |
| 7 | Qwen 2.5 32B Instruct | 74.7 | #509 |
| 8 | Llama 3.3 70B Instruct | 73.49 | #541 |
| 9 | Llama-3.1-Tulu-3-8B | 72.29 | #848 |
| 10 | Mistral 7B Instruct (v0.3) | 71.08 | #1255 |
| 11 | Llama 3.1 70B Instruct | 69.88 | #567 |
| 12 | Gemini 1.5 Pro | 69.88 | #454 |
| 13 | Llama 3.1 8B Instruct | 68.67 | #1017 |
| 14 | DeepSeek R1 Distill Llama 70B | 66.27 | #614 |
| 15 | OLMo-7B-0724-Instruct-hf | 66.27 | #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-false-premise-coconot-false-presumptions-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.