Search Results for author:
Found 9 papers, 8 papers with code
MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning
Natural language image-caption datasets, widely used for training Large Multimodal Models, mainly focus on natural scenarios and overlook the intricate details of mathematical figures that are critical for problem-solving, hindering the advancement of current LMMs in multimodal mathematical reasoning.
WebGen-Bench: Evaluating LLMs on Generating Interactive and Functional Websites from Scratch
To assess the quality of the generated websites, we use GPT-4o to generate test cases targeting each functionality described in the instructions, and then manually filter, adjust, and organize them to ensure accuracy, resulting in 647 test cases.
MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code
Training several popular base models with this corpus significantly improves their mathematical abilities, leading to the creation of the MathCoder2 family of models.
Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset
Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista.
Ranked #1 on
Multimodal Reasoning
on MATH-V
(using extra training data)
MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
In this paper, we present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations and, consequently, enhancing their mathematical reasoning abilities.
Ranked #6 on
Math Word Problem Solving
on SVAMP
(using extra training data)
Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
We found that its success can be largely attributed to its powerful skills in generating and executing code, evaluating the output of code execution, and rectifying its solution when receiving unreasonable outputs.
Ranked #6 on
Math Word Problem Solving
on MATH
DiffSwap: High-Fidelity and Controllable Face Swapping via 3D-Aware Masked Diffusion
Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e. g., identity and landmarks).
