Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·25d ago·5 min read
Two Axes of LLM Abstention: Answer Correctness and Question Answerability
TL;DR
Large Language Models (LLMs) face challenges in providing correct answers and determining if a question is answerable. This study introduces a framework to evaluate LLM abstention based on answer correctness and question answerability.
✦ Why It Matters
Engineers can implement abstention strategies in LLMs to enhance user trust and satisfaction in AI applications.
Key Takeaways
- Evaluate LLMs using the two axes of answer correctness and question answerability.
- Implement abstention strategies to improve user trust in AI responses.
- Analyze LLM performance on diverse question sets for better insights.
- Encourage models to admit uncertainty rather than provide incorrect answers.
Full Summary
Large Language Models (LLMs) are increasingly used for various applications, but their reliability in providing accurate answers is often questioned. This research develops a framework to assess LLM abstention, focusing on two axes: answer correctness (the accuracy of the response) and question answerability (the model's ability to determine if a question can be answered).
By analyzing responses from multiple LLMs, the study finds that models tend to abstain from answering when they lack confidence, which can enhance user trust. The methodology involved testing LLMs on a diverse set of questions and measuring their performance in terms of both correctness and their decision to abstain.
Results indicate that improved abstention strategies can lead to better user experiences, as users prefer models that admit uncertainty rather than providing incorrect answers. These insights can guide future LLM development and deployment strategies.