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Meaningless is better: Hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learning

Year: 2025
Type: article
Abstract: This paper introduces a novel method, referred to as "hashing", which involves masking potentially bias-inducing words in large language models (LLMs) with hash-like meaningless identifiers to reduce cognitive biases and reliance on external knowledge. The method was tested across four sets of experiments involving a total of 640 prompts. Statistical analysis using chi-square tests showed significant improvements in all tested scenarios, which covered the Llama, ChatGPT, Copilot, Gemini and Mixtral models. In the first experiment, hashing decreased the conjunction fallacy rate in a modified version of the “Linda” problem aimed at evaluating susceptibility to cognitive biases. In the second experiment, it improved LLM results on the frequent itemset extraction task. In the third experiment, we found hashing is also effective when the Linda problem is presented in a tabular format rather than text, indicating that the technique works across various input representations. In the fourth experiment, consistent with psychological literature showing that step-by-step reasoning suppresses biases, we compared hashing's effectiveness with Chain of Thought (CoT) LLM models, finding that CoT also suppressed biases in LLMs. Overall, the proposed hashing method was shown to improve bias reduction and reduce the unwanted incorporation of external knowledge. Despite bias reduction, reduction in hallucination rates was inconsistent across LLM model types. These findings suggest that masking bias-inducing terms can improve LLM performance, although its effectiveness is model- and task-dependent.
DOI: 10.1016/j.ipm.2025.104493
Language: English
Open Access status: hybrid
Estimated APC paid: $3,670
OpenAlex ID: W4416909724
FWCI: 0.416
Cites: 21
Cited by: 1
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