Summary of Agentps: Agentic Process Supervision For Multi-modal Content Quality Assurance Through Multi-round Qa, by Gorden Liu et al.
AgentPS: Agentic Process Supervision for Multi-modal Content Quality Assurance through Multi-round QA
by Gorden Liu, Yu Sun, Ruixiao Sun, Xin Dong, Hongyu Xiong
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces AgentPS, a novel framework that integrates Agentic Process Supervision into Multimodal Large Language Models (MLLMs) via multi-round question answering during fine-tuning. This approach demonstrates significant performance improvements over baseline MLLMs on proprietary TikTok datasets, leveraging process supervision and structured sequential reasoning. The results position AgentPS as a highly effective and efficient architecture for multimodal classification tasks, with potential applications in industrial settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AgentPS is a new way to make large language models better at understanding complex relationships between images and text. Right now, these models are great at simple tasks like identifying objects in pictures. But when it comes to more complicated problems that require thinking about many things at once, they struggle. The AgentPS system helps the model by giving it small quizzes during training, which makes it much better at understanding complex ideas. This is important because it could be used in real-world applications, like analyzing lots of data or helping with decision-making tasks. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Question answering
