Conversational, multi-turn, text-to-SQL (CoSQL) tasks map natural language
utterances in a dialogue to SQL queries. State-of-the-art (SOTA) systems use
large, pre-trained and finetuned language models, such as the T5-family, in
conjunction with constrained decoding. With multi-tasking (MT) over coherent
tasks with discrete prompts during training, we improve over specialized
text-to-SQL T5-family models. Based on Oracle analyses over n-best hypotheses,
we apply a query plan model and a schema linking algorithm as rerankers.
Combining MT and reranking, our results using T5-3B show absolute accuracy
improvements of 1.0% in exact match and 3.4% in execution match over a SOTA
baseline on CoSQL. While these gains consistently manifest at turn level,
context dependent turns are considerably harder. We conduct studies to tease
apart errors attributable to domain and compositional generalization, with the
latter remaining a challenge for multi-turn conversations, especially in
generating SQL with unseen parse trees.

通过多任务联合训练和模式重排应用于 text-to-SQL 任务中，可以显著提高模型的表现，进一步结合查询计划模型和模式链接算法后，T5-3B 的精确匹配和执行匹配的准确度均超过 SOTA 基线。

面向会话的文本到 SQL 转换：现状和未来挑战的探索

Conversational Text-to-SQL: An Odyssey into State-of-the-Art and Challenges Ahead

LawGPT 1.0 is a virtual legal assistant built on the state-of-the-art
language model GPT-3, fine-tuned for the legal domain. The system is designed
to provide legal assistance to users in a conversational manner, helping them
with tasks such as answering legal questions, generating legal documents, and
providing legal advice. In this paper, we provide a brief overview of LawGPT
1.0, its architecture, and its performance on a set of legal benchmark tasks.
Please note that the detailed information about the model is protected by a
non-disclosure agreement (NDA) and cannot be disclosed in this report.

本研究介绍了基于 GPT-3 的虚拟法律助理 LawGPT 1.0，可进行智能答疑、法律咨询和文书生成等工作，并获得了良好的在法律基准测试中的表现。