Large language models (LLMs) with in-context learning have demonstrated
remarkable capability in the text-to-SQL task. Previous research has prompted
LLMs with various demonstration-retrieval strategies and intermediate reasoning
steps to enhance the performance of LLMs. However, those works often employ
varied strategies when constructing the prompt text for text-to-SQL inputs,
such as databases and demonstration examples. This leads to a lack of
comparability in both the prompt constructions and their primary contributions.
Furthermore, selecting an effective prompt construction has emerged as a
persistent problem for future research. To address this limitation, we
comprehensively investigate the impact of prompt constructions across various
settings and provide insights for future work.

研究大型语言模型在上下文学习中在文本到 SQL 任务中的表现，尤其是对于提示文本构建的影响和最有效的构建策略进行的全面调查。

如何引导 LLMs 进行文本到 SQL 转换：零 - shot、单域和跨域场景的研究

How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain,  and Cross-domain Settings

Mixed-initiative dialogue tasks involve repeated exchanges of information and
conversational control. Conversational agents gain control by generating
responses that follow particular dialogue intents or strategies, prescribed by
a policy planner. The standard approach has been fine-tuning pre-trained
language models to perform generation conditioned on these intents. However,
these supervised generation models are limited by the cost and quality of data
annotation. We instead prompt large language models as a drop-in replacement to
fine-tuning on conditional generation. We formalize prompt construction for
controllable mixed-initiative dialogue. Our findings show improvements over
fine-tuning and ground truth responses according to human evaluation and
automatic metrics for two tasks: PersuasionForGood and Emotional Support
Conversations.

研究采用大型语言模型替代 fine-tuning 来实现可控制的混合对话，并在 PersuasionForGood 和 Emotional Support Conversations 两个任务中通过人类评估和自动度量标准显示了改进。

通过提示实现可控的混合主动对话生成

Controllable Mixed-Initiative Dialogue Generation through Prompting

Large language models have demonstrated surprising ability to perform
in-context learning, i.e., these models can be directly applied to solve
numerous downstream tasks by conditioning on a prompt constructed by a few
input-output examples. However, prior research has shown that in-context
learning can suffer from high instability due to variations in training
examples, example order, and prompt formats. Therefore, the construction of an
appropriate prompt is essential for improving the performance of in-context
learning. In this paper, we revisit this problem from the view of predictive
bias. Specifically, we introduce a metric to evaluate the predictive bias of a
fixed prompt against labels or a given attributes. Then we empirically show
that prompts with higher bias always lead to unsatisfactory predictive quality.
Based on this observation, we propose a novel search strategy based on the
greedy search to identify the near-optimal prompt for improving the performance
of in-context learning. We perform comprehensive experiments with
state-of-the-art mainstream models such as GPT-3 on various downstream tasks.
Our results indicate that our method can enhance the model's in-context
learning performance in an effective and interpretable manner.

本文提出了一个度量标准，评估了一个固定提示对标签或给定属性的预测偏差，并提出了一种新的基于贪心搜索的搜索策略来确定最佳提示，以提高上下文学习的性能，并在多种下游任务中使用 GPT-3 等最先进的主流模型进行全面实验。结果表明，我们的方法可以有效提高模型的在上下文学习性能。