Information Anxiety in Large Language Models

TL;DR

This paper investigates how large language models handle popular entities, revealing issues like information anxiety where models struggle with lexical variations and disentangling facts, highlighting the need for more holistic evaluation methods.

Contribution

The study provides a comprehensive analysis of internal reasoning, retrieval mechanisms, and the phenomenon of information anxiety in LLMs related to entity popularity and lexical variations.

Findings

Popular questions lead to early convergence of internal states.

Increased entity popularity causes dissimilarity in retrieved attributes.

LLMs struggle to disentangle facts from parametric memory for popular subjects.

Abstract

Large Language Models (LLMs) have demonstrated strong performance as knowledge repositories, enabling models to understand user queries and generate accurate and context-aware responses. Extensive evaluation setups have corroborated the positive correlation between the retrieval capability of LLMs and the frequency of entities in their pretraining corpus. We take the investigation further by conducting a comprehensive analysis of the internal reasoning and retrieval mechanisms of LLMs. Our work focuses on three critical dimensions - the impact of entity popularity, the models' sensitivity to lexical variations in query formulation, and the progression of hidden state representations across LLM layers. Our preliminary findings reveal that popular questions facilitate early convergence of internal states toward the correct answer. However, as the popularity of a query increases, retrieved attributes across lexical variations become increasingly dissimilar and less accurate. Interestingly, we find that LLMs struggle to disentangle facts, grounded in distinct relations, from their parametric memory when dealing with highly popular subjects. Through a case study, we explore these latent strains within LLMs when processing highly popular queries, a phenomenon we term information anxiety. The emergence of information anxiety in LLMs underscores the adversarial injection in the form of linguistic variations and calls for a more holistic evaluation of frequently occurring entities.