Year: 2025
Type: conference-paper
Abstract: Large Language Models (LLMs) have shown remarkable capabilities in zero-shot learning applications, generating responses to queries using only pre-training information without the need for additional fine-tuning.This represents a significant departure from traditional machine learning approaches.Previous research has indicated that LLMs may exhibit serial position effects, such as primacy and recency biases, which are well-documented cognitive biases in human psychology.Our extensive testing across various tasks and models confirms the widespread occurrence of these effects, although their intensity varies.We also discovered that while carefully designed prompts can somewhat mitigate these biases, their effectiveness is inconsistent.These findings underscore the significance of serial position effects during the inference process, particularly in scenarios where there are no ground truth labels, highlighting the need for greater focus on addressing these effects in LLM applications.
Authors: Xiaobo Guo, Soroush Vosoughi
Institution: Dartmouth College
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888929