Reported Confidence in LLMs Tracks Commitment More Than Correctness

1. 1Build a two-stage abstention paradigm to test LLM confidence reports
2. 2Run experiments to analyze the relationship between confidence and correctness
3. 3Configure models to report confidence and commit to answers
4. 4Test the effectiveness of confidence reports in real-world applications
5. 5Apply findings to improve the reliability of LLMs

AI engineers and data scientists benefit from understanding the nuances of LLM confidence reports, as it informs the development of more reliable AI models

💡 Reported confidence in LLMs tracks commitment more than correctness, highlighting the need for more nuanced uncertainty measures

🤖 LLM confidence reports may not always reflect correctness, but rather commitment #AI #LLMs