Summary of Grounding and Evaluation For Large Language Models: Practical Challenges and Lessons Learned (survey), by Krishnaram Kenthapadi and Mehrnoosh Sameki and Ankur Taly
Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)
by Krishnaram Kenthapadi, Mehrnoosh Sameki, Ankur Taly
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 machine learning paper explores the trustworthiness, safety, and observability of artificial intelligence-based systems in high-stakes domains. The authors emphasize the need for evaluating these systems not only for accuracy but also for robustness, bias, security, interpretability, and responsible AI dimensions. Focusing on large language models and generative AI, the paper highlights various harms associated with these systems, including hallucinations, manipulative content, and copyright infringement. It surveys state-of-the-art approaches to address these issues and outlines open challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is getting better at doing things for us. But it’s also important that we make sure this technology doesn’t cause harm. That’s why scientists are working on ways to keep AI safe, fair, and understandable. This paper looks at a type of AI called generative AI, which can create new content like text or images. The authors identify some big problems with these systems, such as making up fake information or spreading harmful messages. They also discuss the latest ideas for solving these issues. |
Keywords
» Artificial intelligence » Machine learning
