Summary of Prompting Large Language Models with Rationale Heuristics For Knowledge-based Visual Question Answering, by Zhongjian Hu et al.
Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering
by Zhongjian Hu, Peng Yang, Bing Li, Fengyuan Liu
First submitted to arxiv on: 22 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 In this paper, researchers develop a new framework for Visual Question Answering (VQA) using Large Language Models (LLMs). The proposed PLRH (Prompts LLMs with Rationale Heuristics) approach prompts LLMs to generate intermediate thought processes, or rationale heuristics, before predicting answers. This novel method outperforms existing baselines by 2.2 and 2.1 on OK-VQA and A-OKVQA datasets, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve knowledge-based VQA by activating the full potential of LLMs. The PLRH framework prompts Chain of Thought (CoT) to generate rationale heuristics, which then inspire LLMs to predict answers. The result is a significant improvement in VQA performance compared to previous methods. |
Keywords
» Artificial intelligence » Question answering
