Natural Context Drift Undermines the Natural Language Understanding of Large Language Models
This highlights a significant challenge for LLMs in handling natural text evolution, which is incremental as it builds on existing QA benchmarks.
The study investigated how natural evolution of context paragraphs affects question answering in large language models (LLMs), finding that LLM performance declines as passages diverge from pretraining versions, with average accuracy on BoolQ dropping by over 30% from highest to lowest similarity bins.
How does the natural evolution of context paragraphs affect question answering in generative Large Language Models (LLMs)? To investigate this, we propose a framework for curating naturally evolved, human-edited variants of reading passages from contemporary QA benchmarks and for analyzing LLM performance across a range of semantic similarity scores, which quantify how closely each variant aligns with content seen during pretraining. Using this framework, we evaluate six QA datasets and eight LLMs with publicly available training data. Our experiments reveal that LLM performance declines as reading passages naturally diverge from the versions encountered during pretraining-even when the question and all necessary information remains present at inference time. For instance, average model accuracy on BoolQ drops by over 30% from the highest to lowest similarity bins, with slopes exceeding 70 across several LLMs. These findings suggest that natural text evolution poses a significant challenge to the language understanding capabilities of LLMs.