Papers by Chaoli Zhang

2 papers
The Illusion of Randomness: How LLMs Fail to Emulate Stochastic Decision-Making in Rock-Paper-Scissors Games? (2025.findings-emnlp)

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Challenge: Prior research indicates that large language models articulate the theoretical probability distributions associated with optimal strategic choices, but their actual decision-making diverges from these prescriptions.
Approach: a systematic evaluation of 20 state-of-the-art LLMs reveals a cognitive bias gap . intrinsic biases inherited from pre-training corpora alone are insufficient to explain deviations . a semantic-free paradigm strips away intrinsic bias to isolate pure positional bias .
Outcome: a systematic evaluation of 20 state-of-the-art LLMs shows that intrinsic biases are insufficient to explain deviations.
LogiCoT: Logical Chain-of-Thought Instruction Tuning (2023.findings-emnlp)

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Challenge: Recent work on self-instruction tuning has focused on enhancing the general proficiency of models.
Approach: They propose a new instruction-tuning dataset for Logical Chain-of-Thought reasoning with GPT-4 that harvests instructions for prompting GPT to generate chain-of thought rationales.
Outcome: The proposed dataset enables the model to generate chain-of-thought rationales with GPT-4.

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