Can ChatGPT Make Explanatory Inferences? Benchmarks for Abductive Reasoning
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Explanatory inference is the creation and evaluation of hypotheses that provide explanations, and is sometimes known as abduction or abductive inference. Generative AI is a new set of artificial intelligence models based on novel algorithms for generating text, images, and sounds. This paper proposes a set of benchmarks for assessing the ability of AI programs to perform explanatory inference, and uses them to determine the extent to which ChatGPT, a leading generative AI model, is capable of making explanatory inferences. Tests on the benchmarks reveal that ChatGPT performs creative and evaluative inferences in many domains, although it is limited to verbal and visual modalities. Claims that ChatGPT and similar models are incapable of explanation, understanding, causal reasoning, meaning, and creativity are rebutted.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 6 of its 41 references that are in the map. First posted on arXiv 29 Apr 2024.
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6 of 41 references.
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Other references · 5
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- 2023Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responsescited by 9
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Citing papers per year: 2024: 1; 2025: 4; 2026: 1.
6 papers.
- 2024Imagining and building wise machines: The centrality of AI metacognitioncited by 12
- 2025LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoningcited by 12
- 2025Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphscited by 11
- 2025Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Modelcited by 8
- 2025Reasoning about Affordances: Causal and Compositional Reasoning in LLMscited by 1
- 2026HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphscited by 0
- 2026HypoAgent: An Agentic Framework for Interactive Abductive Hypothesis Generation over Knowledge Graphs29 May 2026
- 2025Unifying Deductive and Abductive Reasoning in Knowledge Graphs with Masked Diffusion Model13 Oct 2025
- 2025Controllable Logical Hypothesis Generation for Abductive Reasoning in Knowledge Graphs27 May 2025
- 2025Reasoning about Affordances: Causal and Compositional Reasoning in LLMs23 Feb 2025
- 2025LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning16 Feb 2025
- 2024Imagining and building wise machines: The centrality of AI metacognition4 Nov 2024
Similar, not cited
Close in wording, no citation either way
- 2026Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs
- 2022Explanatory Paradigms in Neural Networks
- 2022AbductionRules: Training Transformers to Explain Unexpected Inputs
- 2023Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation
- 2024ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI
- 2025DixitWorld: Evaluating Multimodal Abductive Reasoning in Vision-Language Models with Multi-Agent Dixit Gameplay
- 2025GEAR: A General Evaluation Framework for Abductive Reasoning
- 2026Abductive Reasoning with Syllogistic Forms in Large Language Models