DearICL framework enhances LLM in-context learning with novel sample selection

Researchers have developed DearICL, a novel framework for selecting optimal demonstration examples for in-context learning (ICL) in large language models (LLMs). Unlike static, offline selection methods, DearICL treats sample selection as a subset ranking problem, employing a non-linear surrogate and a gap-index bandit algorithm. This approach allows for fine-grained separation of effective and borderline examples, leading to instance-level subset ranking. Experiments on open-source LLMs show DearICL achieves significant accuracy gains, ranging from 8.08% to 15.9%, over existing linear bandit baselines with low sample complexity. AI

IMPACT Improves LLM adaptability to new tasks by optimizing demonstration example selection, potentially leading to more efficient and accurate few-shot learning.

RANK_REASON This is a research paper detailing a new method for in-context learning in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• DearICL
• In-context learning
• gap-index bandit algorithm
• Gotit.pub
• Hugging Face
• IArxiv
• large language models
• LLMs
• ScienceCast

• paper
• model release

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