Summary of From Data to Commonsense Reasoning: the Use Of Large Language Models For Explainable Ai, by Stefanie Krause et al.
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI
by Stefanie Krause, Frieder Stolzenburg
First submitted to arxiv on: 4 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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 Commonsense reasoning is a challenging task for AI models, but crucial for enhancing explainability. This paper investigates the effectiveness of large language models (LLMs) on question answering (QA) tasks, focusing on their abilities in reasoning and explainability. Three LLMs are studied: GPT-3.5, Gemma, and Llama 3. The results demonstrate the ability of LLMs to reason with commonsense, outperforming humans on various datasets. For instance, Llama 3 achieved a mean accuracy of 90% on all eleven datasets, surpassing human performance by an average of 21%. Additionally, GPT-3.5 provides good explanations for its decisions, as revealed by our questionnaire, where 66% of participants rated the explanations as “good” or “excellent”. These findings shed light on current LLMs and pave the way for future investigations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can answer questions that need commonsense reasoning. The models, like GPT-3.5, Gemma, and Llama 3, are good at this task and even do better than humans on some datasets. This is important because it makes AI more explainable, which means we can understand why the model made a certain decision. In this study, the authors found that Llama 3 was especially good at answering questions correctly, with an average accuracy of 90%. They also showed that GPT-3.5’s explanations for its answers were helpful and easy to understand. |
Keywords
» Artificial intelligence » Gpt » Llama » Question answering
