Large language models have shown unprecedented abilities in generating
linguistically coherent and syntactically correct natural language output.
However, they often return incorrect and inconsistent answers to input
questions. Due to the complexity and uninterpretability of the internally
learned representations, it is challenging to modify language models such that
they provide correct and consistent results. The data management community has
developed various methods and tools for providing consistent answers over
inconsistent datasets. In these methods, users specify the desired properties
of data in a domain in the form of high-level declarative constraints. This
approach has provided usable and scalable methods to delivering consistent
information from inconsistent datasets. We aim to build upon this success and
leverage these methods to modify language models such that they deliver
consistent and accurate results. We investigate the challenges of using these
ideas to obtain consistent and relevant answers from language models and report
some preliminary empirical studies.

研究介绍如何利用数据管理领域的方法和工具解决大型语言模型返回不一致答案的问题，并提供一些初步的实证研究。

构建一致性语言模型的声明式约束

Towards Consistent Language Models Using Declarative Constraints

Large language models (LLMs) that do not give consistent answers across
contexts are problematic when used for tasks with expectations of consistency,
e.g., question-answering, explanations, etc. Our work presents an evaluation
benchmark for self-consistency in cases of under-specification where two or
more answers can be correct. We conduct a series of behavioral experiments on
the OpenAI model suite using an ambiguous integer sequence completion task. We
find that average consistency ranges from 67\% to 82\%, far higher than would
be predicted if a model's consistency was random, and increases as model
capability improves. Furthermore, we show that models tend to maintain
self-consistency across a series of robustness checks, including prompting
speaker changes and sequence length changes. These results suggest that
self-consistency arises as an emergent capability without specifically training
for it. Despite this, we find that models are uncalibrated when judging their
own consistency, with models displaying both over- and under-confidence. We
also propose a nonparametric test for determining from token output
distribution whether a model assigns non-trivial probability to alternative
answers. Using this test, we find that despite increases in self-consistency,
models usually place significant weight on alternative, inconsistent answers.
This distribution of probability mass provides evidence that even highly
self-consistent models internally compute multiple possible responses.

在含糊的整数序列填充任务中，我们在 OpenAI 模型套件上进行了一系列行为实验，发现平均一致性介于 67％和 82％之间，远高于模型一致性随机的预测，并随着模型能力的提高而增加。此外，我们发现模型在自我一致性上具有不良标定性，并且通常倾向于在潜在的答案中分配显著的权重。