Search Results for author:
Found 6 papers, 4 papers with code
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
This RM is then applied to the iterative evolution of data in supervised fine-tuning (SFT).
Ranked #1 on
Math Word Problem Solving
on MATH
(using extra training data)
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #1 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
DotaMath: Decomposition of Thought with Code Assistance and Self-correction for Mathematical Reasoning
In this paper, we introduce a series of LLMs that employs the Decomposition of thought with code assistance and self-correction for mathematical reasoning, dubbed as DotaMath.
OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models
Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora.
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies.
Tailor: A Prompt-Based Approach to Attribute-Based Controlled Text Generation
We experimentally find that these prompts can be simply concatenated as a whole to multi-attribute CTG without any re-training, yet raises problems of fluency decrease and position sensitivity.
