Financial News Analytics Using Fine-Tuned Llama 2 GPT Model
The paper considers the possibility to fine-tune Llama 2 GPT large language model (LLM) for the multitask analysis of financial news. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the model was fine-tuned for the following tasks: analysing a text from financial market perspectives, highlighting main points of a text, summarizing a text and extracting named entities with appropriate sentiments. The obtained results show that the fine-tuned Llama 2 model can perform a multitask financial news analysis with a specified structure of response, part of response can be a structured text and another part of data can have JSON format for further processing. Extracted sentiments for named entities can be considered as predictive features in supervised machine learning models with quantitative target variables.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingLarge Language ModelMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model
The paper considers the possibility of fine-tuning Llama 2 large language model (LLM) for the disinformation analysis and fake news detection. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the mod…
Fact CheckingFake News DetectionLanguage ModelingLanguage Modelling+1Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze se…
ManagementSentiment AnalysisSentiment ClassificationFANAL -- Financial Activity News Alerting Language Modeling Framework
In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News…
Event DetectionLanguage ModelingLanguage ModellingNavigateMultilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model
In the paper, we consider multilevel multitask analysis of cryptocurrency news using a fine-tuned Mistral 7B large language model with retrieval-augmented generation (RAG). On the first level of analytics, the fine-tuned…
Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning
In this paper, we demonstrate that non-generative, small-sized models such as FinBERT and FinDRoBERTa, when fine-tuned, can outperform GPT-3.5 and GPT-4 models in zero-shot learning settings in sentiment analysis for fin…
Sentiment Analysistext-classificationText ClassificationZero-Shot Learning