Mouseion · Collection II · Source file held locally

Lost in the Middle: How Language Models Use Long Contexts

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

Provides a concrete evaluation method for checking whether retrieved information is actually used across long contexts.

A question to carry into the reading

Does adding more context improve an answer when the relevant evidence moves to the middle?

Figure 1 and sections 2–3; record the models, tasks and position controls before discussing generality.

Further reading: MA-OPEN-028 · MA-OPEN-029

These are curatorial comparisons, not evidence supporting a scientific claim.

Reading and source

Results concern the tested historical models and task settings. They neither measure Mouseion nor establish the performance of current models; no benchmark was run.

The selected historical edition or paper is preserved in its original PDF. Consult the edition and transmission notes below.

Open the PDF

Download the unchanged source file

Read at the original source

Edition, source and reuse

Edition: Transactions of the Association for Computational Linguistics 12 (2024), 157–173; DOI 10.1162/tacl_a_00638

Rights: CC BY 4.0 stated in the actual PDF; unchanged complete paper with author credit and licence link.

Rights/source reference 1 · Rights/source reference 2

Lost in the Middle: How Language Models Use Long Contexts — Nelson F. Liu; Kevin Lin; John Hewitt; Ashwin Paranjape; Michele Bevilacqua; Fabio Petroni; Percy Liang. Transactions of the Association for Computational Linguistics 12 (2024), 157–173; DOI 10.1162/tacl_a_00638. Source: https://aclanthology.org/2024.tacl-1.9/. Selected source bytes unchanged; HCR description and reading question are separate.

Review scope: Publisher metadata, 17-page parse, rendered first page/licence, abstract and methods framing inspected.