Papers › Lost in the Middle: How Language Models Use Long Contexts

Lost in the Middle: How Language Models Use Long Contexts

6 Jul 2023arXiv:2307.03172archive 2025-07-28

Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang

While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe 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, 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 language models.

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get_closedbook_qa_prompt nelson-liu/lost-in-the-middle/src/lost_in_the_middle/prompting.py official repository unverified MIT (permissive) · 91506e645c7a9e50 · report
get_kv_retrieval_prompt nelson-liu/lost-in-the-middle/src/lost_in_the_middle/prompting.py official repository unverified MIT (permissive) · aca69348624f921b · report
generate_prompt_landmark jzhang38/longmamba/pass_key.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 870987e39d8888fd · report
passkey_retrieval_test jzhang38/longmamba/pass_key.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · cff545416361104b · report

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Language ModellingQuestion AnsweringRetrieval

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