The paper introduces the L-Tuning technique, which fine-tunes both the prompt and the prefix embeddings of a large language model in a synchronized manner. The prompt is a short text sequence that is prepended to the input, while the prefix embeddings are the initial representations of the input text.
Apr 15, 2024
Dec 21, 2023 · This paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language Inference (NLI) framework.
To address these issues, this paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language ...
Apr 13, 2024 · Empirical evidence suggests that L-Tuning significantly outperforms conventional prompt and prefix tuning in LLMs, both in terms of reducing ...
Feb 15, 2024 · For a standard prefix tuning, one inserts a pseudo-embedding (or embeddings) in the beginning of the sequence and these are updated together ...
Feb 7, 2024 · [2402.01643] L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs · Comments Section · Community Info Section · More posts you may ...
L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs. M Kowsher, MSI Sobuj, A Mahmud, NJ Prottasha, P Bhat. arXiv preprint arXiv:2402.01643, 2023.
To address these issues, this paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language ...
L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs ... Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a ...
Jan 3, 2024 · Exciting news! Introducing our latest research: "L-TUNING: Synchronized Label Tuning for Prompt and Prefix in Large Language Models.