Improving Retrieval for RAG based Question Answering Models on Financial Documents
By Spurthi Setty, Katherine Jijo, Eden Chung, Natan Vidra
Rating
1197
Battle Count: 69
Relevance
7/10
Highly relevant for analyzing financial documents and extracting information, but not directly applicable to trading strategies
Implementation Complexity
8/10
Involves multiple complex techniques and requires domain expertise in both NLP and finance
Reproducibility
3/5
The paper describes methodologies but does not provide specific implementation details or results
About this paper
Methodology: Retrieval Augmented Generation. Problem types: Natural Language Processing, Information Retrieval, Question Answering.
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