Summary of Leveraging Large Language Models with Chain-of-thought and Prompt Engineering For Traffic Crash Severity Analysis and Inference, by Hao Zhen et al.
Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference
by Hao Zhen, Yucheng Shi, Yongcan Huang, Jidong J. Yang, Ninghao Liu
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A study leverages three Large Language Models (LLMs), GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity inference, framing it as a classification task. The researchers generate textual narratives from original traffic crash tabular data using a pre-built template infused with domain knowledge. Additionally, they incorporate Chain-of-Thought (CoT) reasoning to guide the LLMs in analyzing crash causes and inferring severity. The study examines the impact of prompt engineering specifically designed for crash severity inference. The LLMs are tasked with crash severity inference to evaluate their capabilities, assess the effectiveness of CoT and domain-informed prompt engineering, and examine reasoning abilities with the CoT framework. Results show that LLaMA3-70B consistently outperforms other models, particularly in zero-shot settings. CoT and Prompt Engineering techniques significantly enhance performance, improving logical reasoning and addressing alignment issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study uses special kinds of AI called Large Language Models (LLMs) to help predict how severe a car crash is going to be. The researchers took original data about car crashes and turned it into text that the LLMs could understand. They also used something called Chain-of-Thought reasoning to help the LLMs figure out what made the crash severe or not severe. The study looked at three different kinds of LLMs and how well they did at predicting crash severity. It found that one type of LLM, called LLaMA3-70B, was particularly good at this task, especially when it didn’t have any extra training data. The researchers also found that using Chain-of-Thought reasoning and special prompts helped the LLMs do an even better job. |
Keywords
» Artificial intelligence » Alignment » Classification » Gpt » Inference » Prompt » Zero shot
