Lost in the Middle: How Language Models Use Long Contexts
Authors: name: Nelson F. Liu, name: Kevin Lin, name: John Hewitt, name: Ashwin Paranjape, name: Michele Bevilacqua, name: Fabio Petroni, name: Percy Liang

While recent language models have the ability to take long contexts as input,
relatively little is known about how well the language models use longer
context. We analyze language model performance on two tasks that require
identifying relevant information within their input contexts: multi-document
question answering and key-value retrieval. We find that performance is often
highest when relevant information occurs at the beginning or end of the input
context, and significantly degrades when models must access relevant
information in the middle of long contexts. Furthermore, performance
substantially decreases as the input context grows longer, even for explicitly
long-context models. Our analysis provides a better understanding of how
language models use their input context and provides new evaluation protocols
for future long-context models.