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Found 4 papers, 2 papers with code
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
Inspired by the cognitive mechanism in human mathematical learning, it first extracts topics and knowledge points from seed math questions and then build a concept graph, which is subsequently used to generate new math questions.
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
Modular Retrieval for Generalization and Interpretation
Built upon the paradigm, we propose a retrieval model with modular prompt tuning named REMOP.
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
We believe this work facilitates the industry, as it saves enormous efforts and costs of deployment and increases the utility of computing resources.
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