Summary of Evaluating Language Model Context Windows: a “working Memory” Test and Inference-time Correction, by Amanda Dsouza et al.

Evaluating Language Model Context Windows: A “Working Memory” Test and Inference-time Correction

by Amanda Dsouza, Christopher Glaze, Changho Shin, Frederic Sala

First submitted to arxiv on: 4 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary This paper explores the performance of large language models in real-world applications, specifically when reasoning over large volumes of documents. The authors propose an evaluation framework called SWiM to benchmark the capabilities of these models, particularly those with extended context capabilities, which can accommodate up to 2 million tokens. They test eight long context models and find that even strong models like GPT-4 and Claude 3 Opus degrade in performance when information is present in the middle of the context window. To alleviate this issue, they propose a training-free approach called medoid voting, which achieves up to a 24% lift in accuracy on single document QA tasks.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper looks at how big language models work with lots of documents. The authors want to see if these models can handle really long texts and if they make mistakes when information is hidden in the middle. They test different models and find that even good ones like GPT-4 and Claude 3 Opus struggle. To fix this, they suggest a simple way to get better answers without training.