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Chenghao Ma

3 accepted papers

2025

FinMMR: Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging

ICCV 2025poster

We present FinMMR, a novel bilingual multimodal benchmark tailored to evaluate the reasoning capabilities of multimodal large language models (MLLMs) in financial numerical reasoning tasks. Compared to existing benchmarks, our work introduces three significant advancements. (1) Multimodality: We met…

Cited by 0SourcePDFScholar
2025

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark for Large Language Models

ICLR 2025poster

Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. 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…

Cited by 50SourcePDFScholar
2024

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

ACL 2024findings

Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge retrieval and semantic parsing. However, three core challenges remain: inefficient knowledge retrieval, mistakes of ret…