Month in 4 Papers (December 2024)

Author(s): Ala Falaki, PhD

Originally published on Towards AI.

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This series of posts is designed to bring you the newest findings and developments in the NLP field. I’ll delve into four significant research papers each month, offering a comprehensive summary. Be sure to visit my blog regularly or subscribe to my newsletter for monthly updates. Let’s dive in!

Also, here’s to an amazing 2025! (Just a bit early!)

📝 Large Language Monkeys: Scaling Inference Compute with Repeated Sampling [paper]

This paper proposes scaling the inference time as another dimension for scaling NLP models. They suggest that using the LLM to generate only one sample as the response is ineffective. The success of repeated sampling depends on two key factors: coverage and precision. Coverage refers to how many problems we can solve as we take more samples, while precision focuses on whether we can accurately choose the correct answer from the set of generated samples.

One of the open challenges is to have an automatic evaluation to measure the success rate. A method like a unit test is useful for finding a correct sample from a pool of candidates, but there are tasks like math problems that we don’t have a proper way… Read the full blog for free on Medium.

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