This report outlines a transformative initiative in the financial investment
industry, where the conventional decision-making process, laden with
labor-intensive tasks such as sifting through voluminous documents, is being
reimagined. Leveraging language models, our experiments aim to automate
information summarization and investment idea generation. We seek to evaluate
the effectiveness of fine-tuning methods on a base model (Llama2) to achieve
specific application-level goals, including providing insights into the impact
of events on companies and sectors, understanding market condition
relationships, generating investor-aligned investment ideas, and formatting
results with stock recommendations and detailed explanations. Through
state-of-the-art generative modeling techniques, the ultimate objective is to
develop an AI agent prototype, liberating human investors from repetitive tasks
and allowing a focus on high-level strategic thinking. The project encompasses
a diverse corpus dataset, including research reports, investment memos, market
news, and extensive time-series market data. We conducted three experiments
applying unsupervised and supervised LoRA fine-tuning on the llama2_7b_hf_chat
as the base model, as well as instruction fine-tuning on the GPT3.5 model.
Statistical and human evaluations both show that the fine-tuned versions
perform better in solving text modeling, summarization, reasoning, and finance
domain questions, demonstrating a pivotal step towards enhancing
decision-making processes in the financial domain. Code implementation for the
project can be found on GitHub: this https URL

通过使用语言模型，该研究旨在自动化信息摘要和投资理念生成，以评估 Fine-tuning 方法在 Llama2 基础模型上实现特定应用层面目标的有效性，并通过采用最先进的生成建模技术开发 AI 代理原型，解放人类投资者，使他们能够集中于高层级战略思考。

多模式基本投资研究的智能生成算法

Multimodal Gen-AI for Fundamental Investment Research

With the explosive growth of scientific publications, making the synthesis of
scientific knowledge and fact checking becomes an increasingly complex task. In
this paper, we propose a multi-task approach for verifying the scientific
questions based on a joint reasoning from facts and evidence in research
articles. We propose an intelligent combination of (1) an automatic information
summarization and (2) a Boolean Question Answering which allows to generate an
answer to a scientific question from only extracts obtained after
summarization. Thus on a given topic, our proposed approach conducts structured
content modeling based on paper abstracts to answer a scientific question while
highlighting texts from paper that discuss the topic. We based our final system
on an end-to-end Extractive Question Answering (EQA) combined with a three
outputs classification model to perform in-depth semantic understanding of a
question to illustrate the aggregation of multiple responses. With our light
and fast proposed architecture, we achieved an average error rate of 4% and a
F1-score of 95.6%. Our results are supported via experiments with two QA models
(BERT, RoBERTa) over 3 Million Open Access (OA) articles in the medical and
health domains on Europe PMC.

本文提出了一种基于多任务学习的科学问题验证方法，该方法结合了信息概括、布尔问答、提取式问题回答和语义理解技术。该方法在欧洲 PMC 的 300 万篇医学和健康领域 OA 文章上，在 BERT 和 RoBERTa 问答模型的实验中，实现了 4％的平均误差率和 95.6％的 F1 分数。