Paper: https://arxiv.org/pdf/2411.02830)
This research introduces Mixtures of In-Context Learners (MOICL), a novel approach to improve in-context learning (ICL) in large language models (LLMs). MOICL addresses ICL's limitations by partitioning demonstrations into expert subsets and learning a weighting function to combine their predictions. Experiments demonstrate MOICL's superior performance across various classification datasets, enhanced efficiency, and robustness to noisy or imbalanced data. The method dynamically identifies helpful and unhelpful demonstration subsets, improving accuracy and reducing computational costs. A key advantage is MOICL's ability to handle more demonstrations than standard ICL by mitigating the quadratic complexity of attention mechanisms.
ai , artificial intelligence , arxiv , research , paper , publication , llm, genai, generative ai , large visual models, large language models, large multi modal models, nlp, text, machine learning, ml, nividia, openai, anthropic, microsoft, google, technology, cutting-edge, meta, llama, chatgpt, gpt, elon musk, sam altman, deployment, engineering, scholar, science
