Papers by Zhiyong Huang
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)
Copied to clipboard
| Challenge: | Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy. |
| Approach: | They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a . |
| Outcome: | The proposed framework systematically reveals the performance of different target mLLMs. |
Zero-shot Cross-lingual Alignment for Embedding Initialization (2024.findings-acl)
Copied to clipboard
| Challenge: | CrossInit initializes embeddings into similar geometrical structures across languages in unsupervised manner. |
| Approach: | They propose a method that initializes embeddings into similar geometrical structures across languages in an unsupervised manner. |
| Outcome: | The proposed method demostrates similar patterns in low-resource and dissimilar languages. |
CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for extractive and abstractive summarization use token-level or sentence-level training objectives. |
| Approach: | They propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo. |
| Outcome: | The proposed framework boosts extractive and abstractive results on CNN/DailyMail benchmarks while maintaining inference efficiency. |
MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Programming often involves translating detailed and complex specifications into code . current state-of-the-art models struggle to solve these problems, a new study shows . |
| Approach: | They propose a multi-modal coding dataset to evaluate algorithmic problem-solving skills in visually rich contexts. |
| Outcome: | The proposed model lacks powerful vision-code models due to the extreme demand for reasoning abilities. |