Many-Shot In-Context Learning (MS-ICL) Task

A Many-Shot In-Context Learning (MS-ICL) Task is a ICL method that enhances in-context learning by employing significantly more examples for task specifications.

• Context:
  • It can typically extend the capabilities of Large Language Models (LLMs) by providing clearer task instructions and reducing ambiguity, which results in substantial performance gains across a variety of tasks.
  • It can often leverage structured examples to guide Large Language Models (LLMs) towards more accurate and reliable outputs without the need for explicit fine-tuning or specialized training regimes.
  • It can range from using simple repetitive tasks to complex reasoning and high-dimensional function approximations.
  • It can demonstrate a dependency on the quality of input examples, indicating a critical need for high-quality, well-curated example sets.
  • It can show variations in performance based on the order and context of the provided examples, highlighting the influence of prompt design on model output consistency.
  • ...
• Example(s):
  • a Reinforced ICL framework where model-generated rationales replace those created by humans, enabling autonomous learning from dynamically adjusted feedback loops.
  • an Unsupervised ICL setting that excludes rationales altogether, focusing entirely on domain-specific prompts to foster pure inductive reasoning without explicit guidance.
  • ...
• Counter-Example(s):
  • Few-Shot Learning, which involves using a smaller number of examples and typically faces limitations in clarity and performance consistency.
  • LLM Fine Tuning, ... .
  • ...
• See: In-Context Learning, Large Language Models, Reinforced ICL, Unsupervised ICL

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References

2024

• (Agarwal, Singh et al., 2024) ⇒ Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal Behbahani, Aleksandra Faust, and Hugo Larochelle. (2024). “Many-Shot In-Context Learning.” In: [1](https://doi.org/10.48550/arXiv.2404.11018).
  • NOTES:
    • It introduces Many-Shot ICL as an extension of in-context learning with LLMs, employing a larger number of examples to enhance task specification clarity, reduce command ambiguity, and improve performance across varied tasks.
    • It develops new frameworks for ICL, specifically Reinforced ICL and Unsupervised ICL, aimed at improving performance in complex reasoning tasks by altering the dependence on human-generated rationales.
    • It provides empirical evidence showing that many-shot ICL outperforms few-shot learning by addressing pre-training biases and functioning effectively in complex reasoning and high-dimensional tasks.
    • It notes the influence of example order on many-shot ICL performance, highlighting challenges in training consistency and the need for careful prompt design.

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