Fill the complete context length with many shot examples and evaluate the performance! Great new insights, although extreme scaling happens on open source LLMs with extended context length, not on latest models w/ 1 Mio token length, like Gemini 1.5 Pro (see my new, upcoming video in the next days).
A comprehensive analysis of in-context learning (ICL) when extended to long-context models, specifically examining the performance and characteristics. A key finding is that performance in ICL continues to improve with the inclusion of hundreds or even thousands of demonstrations, surpassing traditional fine-tuning methods in certain scenarios. This improvement is not merely additive but is significantly driven by the model's ability to attend to more relevant examples during inference.
For datasets with extensive label spaces, the gains from increasing context length are particularly pronounced. Additionally, the study highlights that while retrieval methods show diminishing returns with extended contexts, the use of a large, randomly selected set of demonstrations in long-context ICL remains surprisingly effective, suggesting that the sheer volume of context can sometimes compensate for the lack of finely-tuned example selection.
Another critical insight from the study is the reduced sensitivity of long-context ICL to the order of examples and the negative impact of grouping same-label examples, which suggests that optimal performance relies on a diverse set of in-context demonstrations rather than clustered or ordered ones. The research also identifies that the performance gains in long-context ICL are primarily attributed to the model's ability to reference relevant examples rather than refining task-specific decision boundaries through extensive encoding. This revelation is supported by experiments showing that performance saturates before reaching the maximum context length for many datasets, indicating that current models have not yet fully exploited the potential of long-context ICL.
Furthermore, long-context models exhibit robust performance across various datasets, maintaining efficiency and accuracy, and offering a promising alternative to traditional fine-tuning, especially when computational efficiency and rapid adaptability are paramount.
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In-Context Learning with Long-Context Models: An In-Depth Exploration
arxiv.org/pdf/2405.00200
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