Summary of Rat: Retrieval Augmented Thoughts Elicit Context-aware Reasoning in Long-horizon Generation, by Zihao Wang and Anji Liu and Haowei Lin and Jiaqi Li and Xiaojian Ma and Yitao Liang
RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
by Zihao Wang, Anji Liu, Haowei Lin, Jiaqi Li, Xiaojian Ma, Yitao Liang
First submitted to arxiv on: 8 Mar 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 We explore a novel method, called retrieval-augmented thoughts (RAT), that significantly improves large language models’ reasoning and generation ability in long-horizon generation tasks. RAT iteratively revises each thought step with retrieved information relevant to the task query, current, and past thought steps. This approach is applied to GPT-3.5, GPT-4, and CodeLLaMA-7b, resulting in substantial performance improvements on various tasks, including code generation (+13.63%), mathematical reasoning (+16.96%), creative writing (+19.2%), and embodied task planning (+42.78%). The proposed method also mitigates hallucination. This research has implications for natural language processing and human-computer interaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if a computer could understand and generate text like humans do. Researchers have found a way to make large language models better at this by using information from the internet to help them think. They call it retrieval-augmented thoughts. This method helps the computers reason and generate text more accurately, reducing mistakes. The team tested this approach on different tasks and saw big improvements in areas like coding, math problems, creative writing, and even planning for robots. This discovery could lead to better interactions between humans and computers. |
Keywords
» Artificial intelligence » Gpt » Hallucination » Natural language processing
