Month in 4 Papers (February 2025)

Author(s): Ala Falaki, PhD

Originally published on Towards AI.

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Exploring how caching strategies, context length, uncertainty estimation, and conceptual representations are reshaping knowledge retrieval in language models.

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!

📝 Large Concept Models: Language Modeling in a Sentence Representation Space [paper] [code]

This paper introduces Large Concept Models (LCM) that process whole sentences at once (instead of tokens), like how humans naturally think in complete ideas rather than individual words. They used the encoder-decoder SONAR model as frozen components, with the LCM model in the middle. So, first, the LCM model receives the sentence embedding from the SONAR’s encoder. Then, LCM generates the new embedding, which will be passed to SONAR’s decoder for generation.

The selected architecture for LCM was named “Two-Tower,” which consists of two components: contextualizer and denoiser, that are implemented using transformer layers. They experimented with different architectures, but Two-Tower proved to be more effective. This approach provides strong performance across languages… Read the full blog for free on Medium.

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