Generalization in LLM Problem Solving: The Case of the Shortest Path

LLMs often struggle with systematic generalization, even on basic reasoning like shortest path. Use controlled synthetic environments to rigorously evaluate their capabilities and identify limitations before deployment.

beginner15 min5 steps

The play

Understand LLM Generalization Gaps
Recognize that Large Language Models (LLMs) may not automatically generalize well to new or out-of-distribution scenarios, even if they perform well on training data.
Prioritize Rigorous Evaluation
For critical applications, implement comprehensive testing in controlled environments to assess an LLM's true reasoning capabilities beyond surface-level performance.
Construct Synthetic Test Environments
Design simplified, isolated test cases (e.g., small graphs, specific rule sets) to pinpoint systematic generalization failures in LLMs.
Utilize Core Reasoning Tasks
Employ tasks like the 'shortest path' problem as a benchmark to specifically probe an LLM's ability to apply logical reasoning consistently.
Augment LLMs for Robustness
For applications demanding high-stakes logical reasoning, consider integrating LLMs with symbolic reasoning systems or other deterministic methods to enhance reliability.

Starter code

Given the following graph where nodes are connected by edges:
A-B (cost 1)
B-C (cost 2)
A-C (cost 4)
C-D (cost 1)

What is the shortest path from A to D and its total cost?

Source

Paperarxiv.org