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Researchers from UC Berkeley, along with ICSI and LBNL, have developed LLM2LLM, a novel iterative data augmentation technique aimed at enhancing the performance of large language models (LLMs) in low-data scenarios in 2024. This approach, detailed in a paper by N Lee, T Wattanawong, S Kim, K Mangalam, and others, uses a 'teacher' LLM to expand a small seed dataset, potentially overcoming one of the significant challenges in the field of natural language processing (NLP). The development underscores the ongoing efforts to refine LLMs, which are at the forefront of solving a wide array of NLP tasks but often require fine-tuning to achieve satisfactory performance levels in real-world applications.
InternLM2 The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, https://t.co/LqWpUoC1su
Struggling to fine-tune your large language model (LLM)? 🤯 There are two techniques to make your LLM more effective: RLHF and DPO. 🧠 Checkout this blog for a detailed comparison: https://t.co/hLDVhNHkSM #FineTuning #LLMs #MachineLearning https://t.co/VsxWEWQWqm
LLM2LLM: UC Berkeley, ICSI and LBNL Researchers’ Innovative Approach to Boosting Large Language Model Performance in Low-Data Regimes with Synthetic Data Quick read: https://t.co/xWQ3CQ770c LLM2LLM is proposed by a research team at UC Berkeley, ICSI, and LBNL as a…
