Papers by Max Ku
TheoremQA: A Theorem-driven Question Answering Dataset (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent LLMs like GPT-4 and PaLM-2 have made tremendous progress in solving fundamental math problems like GSM8K by achieving over 90% accuracy. |
| Approach: | They propose to use theorem-driven question-answering dataset to evaluate AI models' ability to apply theoretic concepts to solving challenging science problems. |
| Outcome: | TheoremQA is curated by domain experts and contains 800 high-quality questions covering 350 theoremics from Math, Physics, EE&CS, and Finance. |
TheoremExplainAgent: Towards Video-based Multimodal Explanations for LLM Theorem Understanding (2025.acl-long)
Copied to clipboard
| Challenge: | Understanding domain-specific theorems requires more than text-based reasoning . current evaluations of theoretical models are based on textual cues . |
| Approach: | They propose an agentic approach for generating long-form theorem explanation videos using Manim animations. |
| Outcome: | The proposed agent generates long-form theorem explanation videos using Manim animations . the agent achieves a success rate of 93.8% and an overall score of 0.77 . |
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation (2024.emnlp-main)
Copied to clipboard
Xuan He, Dongfu Jiang, Ge Zhang, Max Ku, Achint Soni, Sherman Siu, Haonan Chen, Abhranil Chandra, Ziyan Jiang, Aaran Arulraj, Kai Wang, Quy Do, Yuansheng Ni, Bohan Lyu, Yaswanth Narsupalli, Rongqi Fan, Zhiheng Lyu, Bill Yuchen Lin, Wenhu Chen
| Challenge: | Existing video metrics are lagging behind in providing reliable scores over generated videos due to lack of large-scale human-annotated dataset. |
| Approach: | They propose to use VideoFeedback to train a human-annotated multi-aspect score over 37.6K synthesized videos from 11 existing video generative models. |
| Outcome: | The proposed model outperforms the prior best metrics by 50 points in the test. |
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing metrics for conditional image generation are opaque and lack explainability . evaluators of these metrics have limited ability to evaluate image synthesis tasks . |
| Approach: | They propose a Visual Instruction-guided Explainable metric for evaluating conditional image models. |
| Outcome: | The proposed model achieves a high Spearman correlation with human evaluations, but is weaker than GPT-4o and GPT-v in evaluating synthetic images. |