Research map / Large language models / Retrieval and knowledge
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Share encoders across voluntary and involuntary inference dimensions (ablation) | ATOMIC inference generation · unseen test events | Precision at 10 (%): 45.32 -> 47.93 | Helped |
| Share encoders across dimensions grouped by causes versus effects (ablation) | ATOMIC inference generation · unseen test events | Precision at 10 (%): 45.32 -> 46.76 | Helped |
| Share encoders across dimensions grouped by agent versus theme (ablation) | ATOMIC inference generation · unseen test events | Precision at 10 (%): 45.32 -> 46.41 | Helped |
| Share model parameters across related inference dimensions instead of training separate models (ablation) | ATOMIC inference generation · Test | BLEU | Mixed |
| Group dimensions by voluntary versus involuntary inference type (ablation) | ATOMIC inference generation · Test | BLEU | Mixed |
Closest papers by meaning
- RULEBREAKERS: Challenging LLMs at the Crossroads between Formal Logic and Human-like Reasoning (ICML 2025 · similarity 0.87)
- Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement (ICLR 2024 · similarity 0.87)
- Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens (ICLR 2026 · similarity 0.87)
- LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning (NeurIPS 2026 · similarity 0.87)
- ActivationReasoning: Logical Reasoning in Latent Activation Spaces (ICLR 2026 · similarity 0.87)
- State of Thought Enables Endogenous Reasoning (NeurIPS 2026 · similarity 0.86)
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