Summary of Evaluating the Capabilities Of Llms For Supporting Anticipatory Impact Assessment, by Mowafak Allaham et al.
Evaluating the Capabilities of LLMs for Supporting Anticipatory Impact Assessment
by Mowafak Allaham, Nicholas Diakopoulos
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This paper explores the use of Large Language Models (LLMs) to generate insights into the negative impacts of emerging Artificial Intelligence (AI) technologies. The authors fine-tune two LLMs, GPT-3 and Mistral-7B, on a diverse sample of articles from news media and compare their outputs with those generated by instruction-based models like GPT-4 and Mistral-7B-Instruct. The results show that the fine-tuned models produce high-quality and diverse impacts, while instruction-based models have gaps in certain categories of impacts. This research highlights a potential bias in state-of-the-art LLMs and suggests aligning smaller LLMs on news media as a scalable alternative for generating high-quality and diverse impacts in support of anticipatory governance approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer programs called Large Language Models to help us think about the bad things that might happen when new Artificial Intelligence technologies come out. The authors tried training these models on news articles to see if they could generate good ideas and insights. They found that some models did a better job than others, and that bigger models aren’t always better. This research is important because it can help us make better decisions about how to use AI in the future. |
Keywords
» Artificial intelligence » Gpt
