To enhance Large Language Models' (LLMs) reliability, calibration is
essential -- the model's assessed confidence scores should align with the
actual likelihood of its responses being correct. However, current confidence
elicitation methods and calibration metrics typically rely on a binary
true/false assessment of response correctness. This approach does not apply to
long-form generation, where an answer can be partially correct. Addressing this
gap, we introduce a unified calibration framework, in which both the
correctness of the LLMs' responses and their associated confidence levels are
treated as distributions across a range of scores. Within this framework, we
develop three metrics to precisely evaluate LLM calibration and further propose
two confidence elicitation methods based on self-consistency and
self-evaluation. Our experiments, which include long-form QA and summarization
tasks, demonstrate that larger models don't necessarily guarantee better
calibration, that calibration performance is found to be metric-dependent, and
that self-consistency methods excel in factoid datasets. We also find that
calibration can be enhanced through techniques such as fine-tuning, integrating
relevant source documents, scaling the temperature, and combining
self-consistency with self-evaluation. Lastly, we showcase a practical
application of our system: selecting and cascading open-source models and
ChatGPT to optimize correctness given a limited API budget. This research not
only challenges existing notions of LLM calibration but also offers practical
methodologies for improving trustworthiness in long-form generation.

通过引入统一的校准框架，以及发展三种度量和两种置信度引导方法，提高大规模语言模型的校准能力，并进行了实验验证，进一步展示了大型模型不一定保证更好的校准，校准性能依赖于度量，自一致性方法在基准数据集上表现优异，通过微调、整合相关源文件、缩放温度、将自一致性与自我评估相结合等技术可以提高校准性，此研究不仅挑战了现有大规模语言模型校准的概念，还提供了改善长文生成可信度的实用方法。

从大型语言模型中校正长文生成

Calibrating Long-form Generations from Large Language Models

The task of empowering large language models (LLMs) to accurately express
their confidence, referred to as confidence elicitation, is essential in
ensuring reliable and trustworthy decision-making processes. Previous methods,
which primarily rely on model logits, have become less suitable for LLMs and
even infeasible with the rise of closed-source LLMs (e.g., commercialized LLM
APIs). This leads to a growing need to explore the untapped area of
\emph{non-logit-based} approaches to estimate the uncertainty of LLMs. Hence,
in this study, we investigate approaches for confidence elicitation that do not
require model fine-tuning or access to proprietary information. We introduce
three categories of methods: verbalize-based, consistency-based, and their
hybrid methods for benchmarking, and evaluate their performance across five
types of datasets and four widely-used LLMs. Our analysis of these methods
uncovers several key insights: 1) LLMs often exhibit a high degree of
overconfidence when verbalizing their confidence; 2) Prompting strategies such
as CoT, Top-K and Multi-step confidences improve calibration of verbalized
confidence; 3) Consistency-based methods outperform the verbalized confidences
in most cases, with particularly notable improvements on the arithmetic
reasoning task; 4) Hybrid methods consistently deliver the best performance
over their baselines, thereby emerging as a promising state-of-the-art
approach; 5) Despite these advancements, all investigated methods continue to
struggle with challenging tasks, such as those requiring professional
knowledge, leaving significant scope for improvement of confidence elicitation.

本研究探讨了不需要模型微调或专有信息的自信度引出方法，通过基于词汇、一致性和混合等三种类别的方法的基准评估和评估，揭示了语言模型的自信度过高以及一些方法的优点。