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Recent advancements in in-context learning (ICL) for large language models (LLMs) demonstrate significant improvements in model performance. Researchers from Carnegie Mellon University and Tel Aviv University, including A Bertsch, M Ivgi, U Alon, J Berant, M R. Gormley, and G Neubig, have explored the impact of increasing the number of demonstrations in-context to large values. Their findings indicate that long-context ICL, where thousands of examples are used, is more effective than traditional few-shot prompting and is competitive with fine-tuning methods. Additionally, research from Google and Microsoft highlights the effectiveness of many-shot in-context learning and addresses challenges like the 'lost-in-the-middle' problem in LLMs, where information in long contexts is not fully utilized. Notably, the extension of Llama-3's context was achieved with only 3.5K synthetic training samples generated by GPT-4.
This paper from Google explores into the potential of "many-shot" in-context learning with LLMs, exploring its effectiveness and limitations across various tasks, as well as ways to mitigate the need for extensive human-generated data. And finds significant performance boosts… https://t.co/FczW3vKtRI
"Make Your LLM Fully Utilize the Context" 📌 This research from Microsoft partially solves "lost-in-the-middle" problems in LLMs, where LLMs struggle to fully utilize information located within long contexts, particularly in the middle sections. 📌 The paper hypothesizes that… https://t.co/ccto8RtW7m
With long-context LMs, we can now fit *thousands* of training examples in context! We perform an in-depth exploration of many-shot in-context learning, finding it surprisingly effective, providing huge increases over few-shot prompting, and competitive with fine-tuning! https://t.co/uZlt8BwEZr