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Hacker News: SepLLM: Accelerate LLMs by Compressing One Segment into One Separator

Mar 6, 2025

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Source URL: https://sepllm.github.io/ Source: Hacker News Title: SepLLM: Accelerate LLMs by Compressing One Segment into One Separator Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a novel framework called SepLLM designed to enhance the performance of Large Language Models (LLMs) by improving inference speed and computational efficiency. It identifies an innovative…

Hacker News: Large Language Models as Markov Chains

Dec 1, 2024

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Source URL: https://arxiv.org/abs/2410.02724 Source: Hacker News Title: Large Language Models as Markov Chains Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a theoretical analysis of large language models (LLMs) by framing them as equivalent to Markov chains. This approach may unveil new insights into LLM performance, pre-training, and generalization, which are…

Hacker News: CleaR: Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Labels

Nov 29, 2024

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Source URL: https://arxiv.org/abs/2411.00873 Source: Hacker News Title: CleaR: Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Labels Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a novel approach to Parameter-Efficient Fine-Tuning (PEFT) designed to enhance model performance when working with noisy labeled data. This research is particularly relevant for professionals in AI,…

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Hacker News: SepLLM: Accelerate LLMs by Compressing One Segment into One Separator

Hacker News: Large Language Models as Markov Chains

Hacker News: CleaR: Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Labels