Summary

• The paper presents LUQ, an innovative sampling-based approach for UQ in LLMs, addressing the challenges of generating long text with confidence assessments.
• LUQ outperforms existing UQ methods by effectively correlating with model factuality scores, making it a powerful tool in reducing nonfactual outputs from LLMs.
• The study examines the confidence patterns of popular LLMs when generating long responses and their relationship with factual correctness of the generated text.
• LUQ-Ensemble is proposed as an extension of LUQ, ensembling responses from multiple models to select the least uncertain and most factually correct response.

Opinion

• LUQ significantly advances the field of UQ by catering to the unique demands of long text generation, a crucial need for practical AI applications.
• The LUQ-Ensemble methodology could be a breakthrough in improving the overall quality and reliability of LLM outputs, especially in scenarios requiring detailed and accurate information.

Read the full paper here.