What is the accuracy-efficiency trade-off of LogiPart's embedding-aware local LLM assignment versus GraphMETRO

Abstract

Abstract: Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generatio

Research Question

What is the accuracy-efficiency trade-off of LogiPart's embedding-aware local LLM assignment versus GraphMETRO's node-based alignment on SuperGLUE benchmarks when scaling to 100k+ documents?

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