Divide, Reweight, and Conquer: A Logit Arithmetic Approach for In-Context Learning
LARA improves in-context learning by dividing long demonstrations into shorter parallel groups and reweighting their logits via non-gradient optimization, boosting accuracy and memory efficiency over baselines on BBH and MMLU.
Published Oct 14, 20241 citation▲ 1 on Hugging FaceCode ★ 8arXiv ↗
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LARA delivers measurable accuracy and memory gains through logit ensembling and non-gradient search on BBH and MMLU, though it lacks variance reporting, noisy-demo stress tests, and long-context wall-clock benchmarks beyond baseline hygiene.
Abstract
In-Context Learning (ICL) emerges as a key feature for Large Language Models (LLMs), allowing them to adapt to new tasks by leveraging task-specific examples without updating model parameters. However, ICL faces challenges with increasing numbers of examples due to performance degradation and quadratic computational costs. In this paper, we propose Logit Arithmetic Reweighting Approach (LARA), a novel framework that enhances ICL by using logit-based ensembling of multiple demonstrations. Our approach divides long input demonstrations into parallelizable shorter inputs to significantly reduce memory requirements, and then effectively aggregate the information by reweighting logits of each group via a non-gradient optimization approach. We further introduce Binary LARA (B-LARA), a variant that constrains weights to binary values to simplify the search space and reduces memory usage by filtering out less informative demonstration groups. Experiments on BBH and MMLU demonstrate that LARA and B-LARA outperform all baseline methods in both accuracy and memory efficiency. We also conduct extensive analysis to show that LARA generalizes well to scenarios of varying numbers of examples from limited to many-shot demonstrations.