Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 16·5 min read
A Definition of Good Explanations and the Challenges Explaining LLM Outputs
TL;DR
Explaining the outputs of large language models (LLMs) is challenging due to their complexity and opacity. This work defines what constitutes a good explanation and identifies key challenges in providing clarity on LLM outputs.
✦ Why It Matters
Engineers can enhance user trust in LLMs by implementing better interpretability techniques based on this research.
Key Takeaways
- Define what makes an explanation effective for LLM outputs.
- Identify challenges in explaining complex AI systems.
- Develop frameworks to evaluate explanation quality.
- Focus on user needs to improve model transparency.
Full Summary
Large language models (LLMs) like GPT-3 generate text based on vast datasets, but their decision-making processes are often opaque, making it difficult for users to understand their outputs. This research defines good explanations as those that are accurate, relevant, and comprehensible, while also identifying challenges such as the inherent complexity of LLMs and the variability in user expectations.
The authors propose a framework for evaluating explanations based on these criteria. They conducted user studies to assess how different explanation methods impacted user trust and understanding.
Results indicated that users preferred explanations that were concise and directly related to the output, leading to a 30% increase in perceived trust when good explanations were provided. These findings suggest that enhancing explanation quality can significantly improve user interaction with LLMs, making them more accessible and reliable for various applications.