Large Language Models (LLMs), like LLaMA, have exhibited remarkable
performances across various tasks. Nevertheless, when deployed to specific
domains such as law or medicine, the models still confront the challenge of a
deficiency in domain-specific knowledge and an inadequate capability to
leverage that knowledge to resolve domain-related problems. In this paper, we
focus on the legal domain and explore how to inject domain knowledge during the
continual training stage and how to design proper supervised finetune tasks to
help the model tackle practical issues. Moreover, to alleviate the
hallucination problem during model's generation, we add a retrieval module and
extract relevant articles before the model answers any queries. Augmenting with
the extracted evidence, our model could generate more reliable responses. We
release our data and model at this https URL

本文研究了如何在持续训练过程中注入领域知识以及如何设计正确的监督微调任务来帮助模型解决实际问题，在加入检索模块并提取相关文献的情况下，我们的模型可以更可靠地生成答案。

律师 LLaMA 技术报告

Lawyer LLaMA Technical Report

The Universal Intelligence Measure is a recently proposed formal definition
of intelligence. It is mathematically specified, extremely general, and
captures the essence of many informal definitions of intelligence. It is based
on Hutter's Universal Artificial Intelligence theory, an extension of Ray
Solomonoff's pioneering work on universal induction. Since the Universal
Intelligence Measure is only asymptotically computable, building a practical
intelligence test from it is not straightforward. This paper studies the
practical issues involved in developing a real-world UIM-based performance
metric. Based on our investigation, we develop a prototype implementation which
we use to evaluate a number of different artificial agents.

本文基于 Hutter 和 Solomonoff 的理论，研究了建立基于 UIM 的实用性能度量所涉及的实际问题，并开发了一种基于原型实现的度量方法，用于评估不同人工智能代理的性能。