Summary of Can Llms Replace Neil Degrasse Tyson? Evaluating the Reliability Of Llms As Science Communicators, by Prasoon Bajpai et al.
Can LLMs replace Neil deGrasse Tyson? Evaluating the Reliability of LLMs as Science Communicators
by Prasoon Bajpai, Niladri Chatterjee, Subhabrata Dutta, Tanmoy Chakraborty
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 evaluates the reliability of Large Language Models (LLMs) as science communicators, focusing on their performance in scientific question-answering tasks. The authors introduce a novel dataset, SCiPS-QA, containing 742 Yes/No queries embedded in complex scientific concepts, and develop a benchmarking suite to assess LLMs for correctness and consistency across various criteria. Three proprietary GPT models from OpenAI are benchmarked against 13 open-access LLMs from Meta’s Llama-2, Llama-3, and Mistral families. While most open-access models underperform compared to GPT-4 Turbo, the Llama-3-70B model shows strong performance, often surpassing GPT-4 Turbo in evaluation aspects. The study also reveals that even top-performing GPT models struggle with verifying their own responses accurately, and human evaluators are frequently deceived by incorrect responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well big language models do when they’re trying to communicate science to people. These models are getting really popular, but it’s not clear if they can be trusted to give us accurate answers. The researchers created a special test to see how well the models do with scientific questions that require understanding complex concepts. They tested several different models and found that some of them did better than others. Unfortunately, even the best models struggled to accurately verify their own answers, and human evaluators were often tricked into thinking incorrect answers were correct. |
Keywords
» Artificial intelligence » Gpt » Llama » Question answering
