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Found 6 papers, 4 papers with code
Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models
However, existing benchmarks like GSM8K or MATH are now being solved with high accuracy (e. g., OpenAI o1 achieves 94. 8% on MATH dataset), indicating their inadequacy for truly challenging these models.
ORLM: Training Large Language Models for Optimization Modeling
We apply the data from OR-Instruct to various open-source LLMs of 7b size (termed as ORLMs), resulting in a significantly improved capability for optimization modeling.
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.
