Chunking, a technique used to break down longer texts into smaller chunks, can significantly improve the accuracy of Large Language Models (LLMs) when answering questions. By segmenting larger contexts into more digestible pieces, these models can better consider all relevant information, irrespective of its position in the overall context. This enhancement is particularly beneficial for numerical question answering, as it allows LLMs to interpret and analyze complex financial data more effectively.
Numerical Reasoning
Financial reports often contain tabulated data that requires nuanced interpretation to comprehend intricate information. Our project aims to fortify the understanding of these data points by enhancing numerical reasoning specifically tailored for financial reports. This endeavor focuses on developing an end-to-end pipeline that extracts and generates insights directly from financial report PDFs, empowering users with swift access to crucial data points.
Chunking
To enhance numerical reasoning, we utilize chunking to segment larger contexts into smaller pieces. By using the FAISS DB to fetch similarity scores between questions and text chunks, we select the most relevant segments for analysis. This process ensures that LLMs consider all relevant information when answering questions, resulting in more accurate outputs.
Benefits
The chunking technique has several benefits, including improved accuracy, reduced incidence of erroneous or unrelated outputs, and enhanced overall efficiency. By providing LLMs with compact and focused context portions, these models can better consider all relevant information, irrespective of its position in the overall context. This approach enables faster processing times, allowing for real-time analysis of financial reports and rapid decision-making in dynamic market environments.
Conclusion
In summary, our project focuses on advancing research in two areas: enhancing numerical reasoning specifically tailored for financial reports and developing an end-to-end pipeline that extracts and generates insights directly from financial report PDFs. By utilizing chunking, we improve the accuracy of Large Language Models when answering questions, empowering users with swift access to crucial insights. This approach streamlines the process of financial analysis, enhancing overall efficiency in dynamic market environments.
