In this paper, we present PARAMANU-AYN, a language model based exclusively on
case documents of the Supreme Court of India, the Constitution of India, and
the Indian Penal Code. The novel Auto Regressive (AR) decoder based model is
pretrained from scratch at a context size of 8192. We evaluated our pretrained
legal model on perplexity metrics. We also instruction-tuned our pretrained
model on a set of 10,763 instructions covering various legal tasks such as
legal reasoning, judgement explanation, legal clause generation, legal
drafting, legal contract drafting, case summarization, constitutional
question-answering, etc. We also evaluated the responses of prompts for
instruction-tuned models by GPT-3.5-Turbo on clarity, relevance, completeness,
and legal reasoning metrics in a scale of 10. Our model can be run on CPU and
achieved 42.46 tokens/sec CPU inference speed. We found that our models,
despite not being pretrained on legal books, various legal contracts, and legal
documents, were able to learn the domain knowledge required for drafting
various legal contracts and legal clauses, and generalize to draft legal
contracts and legal clauses with limited instruction tuning. Hence, we conclude
that for a strong domain-specialized generative language model (such as legal),
very large amounts of data are not required to develop models from scratch. We
believe that this work is the first attempt to make a dedicated generative
legal language model from scratch for Indian Supreme Court jurisdiction or in
legal NLP overall. We plan to release our Paramanu-Ayn model at
this https URL

本研究介绍了 PARAMANU-AYN，这是一种基于印度最高法院案件文件、印度宪法和印度刑法的语言模型。该模型使用了基于自回归解码器的新型预训练方法，通过训练和评估多个指令任务，证明了即使没有大量数据，也能从头开始开发一个专门的生成式法律语言模型。

PARAMANU-AYN: 适用于印度法律案件文件的高效新型生成和指导语言模型

PARAMANU-AYN: An Efficient Novel Generative and Instruction-tuned  Language Model for Indian Legal Case Documents

Clause recommendation is the problem of recommending a clause to a legal
contract, given the context of the contract in question and the clause type to
which the clause should belong. With not much prior work being done toward the
generation of legal contracts, this problem was proposed as a first step toward
the bigger problem of contract generation. As an open-ended text generation
problem, the distinguishing characteristics of this problem lie in the nature
of legal language as a sublanguage and the considerable similarity of textual
content within the clauses of a specific type. This similarity aspect in legal
clauses drives us to investigate the importance of similar contracts'
representation for recommending clauses. In our work, we experiment with
generating clauses for 15 commonly occurring clause types in contracts
expanding upon the previous work on this problem and analyzing clause
recommendations in varying settings using information derived from similar
contracts.

本论文探讨了如何通过使用类似合同的信息来生成句子，并将此方法应用于合同中 15 种常见条款类型的条款建议。

探究子句推荐策略

Investigating Strategies for Clause Recommendation

Legal contracts, such as employment or lease agreements, are important
documents as they govern the obligations and entitlements of the various
contracting parties. However, these documents are typically long and written in
legalese resulting in lots of manual hours spent in understanding them. In this
paper, we address the task of summarizing legal contracts for each of the
contracting parties, to enable faster reviewing and improved understanding of
them. Specifically, we collect a dataset consisting of pairwise importance
comparison annotations by legal experts for ~293K sentence pairs from lease
agreements. We propose a novel extractive summarization system to automatically
produce a summary consisting of the most important obligations, entitlements,
and prohibitions in a contract. It consists of two modules: (1) a content
categorize to identify sentences containing each of the categories (i.e.,
obligation, entitlement, and prohibition) for a party, and (2) an importance
ranker to compare the importance among sentences of each category for a party
to obtain a ranked list. The final summary is produced by selecting the most
important sentences of a category for each of the parties. We demonstrate the
effectiveness of our proposed system by comparing it against several text
ranking baselines via automatic and human evaluation.

本文提出了一种新的摘要提取系统，以自动选择契约中最重要的义务、权利和禁令等内容进行合同文本的摘要生成，通过将其与几种文本排名基准进行比较，通过自动和人工评估，证明了该系统的有效性。

合同里该读什么？法律文件的当事人特定重要义务、权利和禁止事项总结

What to Read in a Contract? Party-Specific Summarization of Important Obligations, Entitlements, and Prohibitions in Legal Documents

Extracting entities and other useful information from legal contracts is an
important task whose automation can help legal professionals perform contract
reviews more efficiently and reduce relevant risks. In this paper, we tackle
the problem of detecting two different types of elements that play an important
role in a contract review, namely entities and red flags. The latter are terms
or sentences that indicate that there is some danger or other potentially
problematic situation for one or more of the signing parties. We focus on
supporting the review of lease agreements, a contract type that has received
little attention in the legal information extraction literature, and we define
the types of entities and red flags needed for that task. We release a new
benchmark dataset of 179 lease agreement documents that we have manually
annotated with the entities and red flags they contain, and which can be used
to train and test relevant extraction algorithms. Finally, we release a new
language model, called ALeaseBERT, pre-trained on this dataset and fine-tuned
for the detection of the aforementioned elements, providing a baseline for
further research

通过定义租赁协议所需的实体和红旗类型，以及发布了一个新型的预训练语言模型（ALeaseBERT）来自动化从法律合同中提取实体和有用信息以帮助法律专业人士更高效地进行合同审核，减少相关风险，为以后的研究提供了基础。