Wu & Deng (2025)

Transitive Inference in Large Language Models and Prompting Intervention

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1-5

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DOI

Reference

Wu, W., & Deng, W. (2025). Transitive Inference in Large Language Models and Prompting Intervention. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1-5. https://doi.org/10.1109/icassp49660.2025.10889585

What Rests on This

1 mechanism cites this source. Each finding below is quoted as it appears on that mechanism's own page.

Integrative Inference Memory Processes

Measured in AI

Transitive inference has been tested by name in large language models given the premises in the conversation (in the model's context window, meaning what it can see at the moment): GPT-3.5-Turbo, GPT-4, Llama3-8B and Qwen showed strong inference on 10-item hierarchies with human-like symbolic distance and terminal-item effects, but the abstract reports no human group and no accuracy figures
Measured in AI

The systems differed in how they responded to help: step-by-step (chain-of-thought) and confidence prompts improved GPT-4 on transitive inference but lowered accuracy in the other models tested, and terminal-item biases persisted or grew