Lost-in-Distance: Impact of Contextual Proximity on LLM Performance in Graph Tasks

TL;DR

This paper investigates how the proximity of relevant information within context affects Large Language Models' performance on complex graph tasks, revealing a phenomenon called "lost-in-distance" that impacts accuracy.

Contribution

It introduces the lost-in-distance concept, demonstrates its effect on LLM performance in graph reasoning, and evaluates multiple models and encoding techniques to analyze this impact.

Findings

Model accuracy drops up to 6x as distance increases

Performance depends on relative positioning of graph elements

Lost-in-distance occurs independently of encoding and model size

Abstract

Despite significant advancements, Large Language Models (LLMs) exhibit blind spots that impair their ability to retrieve and process relevant contextual data effectively. We demonstrate that LLM performance in graph tasks with complexities beyond the "needle-in-a-haystack" scenario-where solving the problem requires cross-referencing and reasoning across multiple subproblems jointly-is influenced by the proximity of relevant information within the context, a phenomenon we term "lost-in-distance". We examine two fundamental graph tasks: identifying common connections between two nodes and assessing similarity among three nodes, and show that the model's performance in these tasks significantly depends on the relative positioning of common edges. We evaluate three publicly available LLMs using various graph encoding techniques that represent graph structures for LLM input. We propose a formulation for the lost-in-distance phenomenon and demonstrate that lost-in-distance and lost-in-the middle phenomenas occur independently. Results indicate that model accuracy can decline by up to 6x as the distance between node connections increases, independent of graph encoding and model size.