Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)

• URL: http://arxiv.org/abs/2407.12858v1
• Date: Wed, 10 Jul 2024 01:23:10 GMT
• Title: Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)
• Authors: Krishnaram Kenthapadi, Mehrnoosh Sameki, Ankur Taly,
• Abstract summary: It is essential to evaluate and monitor AI systems for robustness, bias, security, interpretability, and other responsible AI dimensions. We focus on large language models (LLMs) and other generative AI models, which present additional challenges such as hallucinations, harmful and manipulative content, and copyright infringement.
• Score: 16.39412083123155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes domains, ensuring the trustworthiness, safety, and observability of these systems has become crucial. It is essential to evaluate and monitor AI systems not only for accuracy and quality-related metrics but also for robustness, bias, security, interpretability, and other responsible AI dimensions. We focus on large language models (LLMs) and other generative AI models, which present additional challenges such as hallucinations, harmful and manipulative content, and copyright infringement. In this survey article accompanying our KDD 2024 tutorial, we highlight a wide range of harms associated with generative AI systems, and survey state of the art approaches (along with open challenges) to address these harms.

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