Sources
Rohan Paul
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
- Rohan Paul
"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
Graham NeubigWith 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
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