Search Results for author:
Found 11 papers, 4 papers with code
Rendering Graphs for Graph Reasoning in Multimodal Large Language Models
In this paper, we take the first step in incorporating visual information into graph reasoning tasks and propose a new benchmark GITQA, where each sample is a tuple (graph, image, textual description).
VLLaVO: Mitigating Visual Gap through LLMs
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts.
BYOM: Building Your Own Multi-Task Model For Free
Recently, various merging methods have been proposed to build a multi-task model from task-specific finetuned models without retraining.
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation
However, the performance of existing UDA methods is constrained by the large domain shift and limited target domain data.
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Our MetaMath-7B model achieves 66. 4% on GSM8K and 19. 4% on MATH, exceeding the state-of-the-art models of the same size by 11. 5% and 8. 7%.
Ranked #53 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Dual-Balancing for Multi-Task Learning
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields.
Forward-Backward Reasoning in Large Language Models for Mathematical Verification
Instead of using forward or backward reasoning alone, we propose FOBAR to combine FOrward and BAckward Reasoning for verification.
Effective Structured Prompting by Meta-Learning and Representative Verbalizer
Combining meta-learning the prompt pool and RepVerb, we propose MetaPrompter for effective structured prompting.
An Adaptive Policy to Employ Sharpness-Aware Minimization
Sharpness-aware minimization (SAM), which searches for flat minima by min-max optimization, has been shown to be useful in improving model generalization.
Effective Meta-Regularization by Kernelized Proximal Regularization
We study the problem of meta-learning, which has proved to be advantageous to accelerate learning new tasks with a few samples.
Multi-Subspace Structured Meta-Learning
We propose a MUlti-Subspace structured Meta-Learning (MUSML) algorithm to learn the subspace bases.
