Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly
Peyman Hosseini, Ignacio Castro, Iacopo Ghinassi, Matthew Purver
Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in comprehending and analyzing lengthy sequential inputs, owing to their extensive context windows that allow processing millions of tokens in a single forward pass. However, this paper uncovers a surprising limitation: LLMs fall short when handling long input sequences. We investigate this issue using three datasets and two tasks (sentiment analysis and news categorization) across various LLMs, including Claude 3, Gemini Pro, GPT 3.5 Turbo, Llama 3 Instruct, and Mistral Instruct models. To address this limitation, we propose and evaluate ad-hoc solutions that substantially enhance LLMs’ performance on long input sequences by up to 50%, while reducing API cost and latency by up to 93% and 50%, respectively.
BibTeX
@inproceedings{hosseini-etal-2025-efficient,
title = "Efficient Solutions For An Intriguing Failure of {LLM}s: Long Context Window Does Not Mean {LLM}s Can Analyze Long Sequences Flawlessly",
author = "Hosseini, Peyman and
Castro, Ignacio and
Ghinassi, Iacopo and
Purver, Matthew",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.128/",
pages = "1880--1891"
}