Beyond Early-Token Bias: Model-Specific and Language-Specific Position Effects in Multilingual LLMs

A recent study on Large Language Models (LLMs) reveals that position bias, which affects how information is weighted based on its context location, varies significantly across different languages and model architectures. The research analyzed five languages—English, Russian, German, Hindi, and Vietnamese—using models like Qwen2.5-7B-Instruct and Mistral 7B, finding that late positions are favored in certain models contrary to the common early-token preference assumption.
This development is crucial as it challenges existing notions of how LLMs process information and highlights the need for tailored prompting strategies. The findings suggest that understanding model-specific and language-specific biases can enhance the accuracy and reliability of outputs generated by LLMs, which are increasingly used in diverse applications.
The implications of this research extend to broader discussions about the reliability and fairness of LLMs in various contexts, including survey simulations and linguistic analysis. Issues such as overconfidence in model predictions and the potential for social bias in outputs are critical considerations, emphasizing the importance of ongoing evaluation and refinement of LLMs to ensure they meet the needs of diverse user demographics.

Beyond Early-Token Bias: Model-Specific and Language-Specific Position Effects in Multilingual LLMs