The Mitch Daniels School of Business offers internationally ranked programs at the bachelor’s, master’s and doctoral levels. Long named the Purdue School of Management, with the graduate school bearing the Krannert name, we are now the Daniels School of Business. We are integrated into one of the world’s leading STEM universities, building the future of business upon a foundation of 150-plus years of leadership and innovation. Our students harness communication skills, hands-on learning, and analytical excellence to turn new discoveries into societal change.
Kudos to the unique & innovative mind's working on a most important field of healthcare, which I am sure would help many 👌💐👏👏👏👍. Congrats & Best wishes....
While synthetic data protects privacy and aids compliance, it may miss complex, real-world health patterns essential for accurate analytics. Analytical results from synthetic data alone can lack depth and reliability. Therefore, validation with real patient data is crucial for dependable healthcare insights.
Nice one! So basically lemme explain: - We did call APIs for the model, because building a model from scratch would have been difficult and expensive. But the issue with these base models were that they didn't perform well. models like the text-bison-32k were giving very low accuracy along with Gemini. It wasn't able to retrieve the right information. - Although we couldn't alter the model, we were able to change a lot of factors out of which the following worked for this problem statement. 1) Query Rephrasing: By transforming the query "What is the PPNE for 2018?" to "What is the Plant, Property, & Equipment value for 2018?", the model facilitates better matching in the documents where the acronym may not be explicitly used. The original text from SEC filings often does not directly match the query terms due to variations in terminology. By rephrasing the queries, we could bridge the gap between the query language and the language used in the documents, improving the effectiveness of text matching. 2) Dynamic Context Window Adjustment: The "dynamic adjustment" of the context window in the model refers to an iterative process where the model evaluates and adjusts the amount of text it considers generating accurate responses. Initially, the model retrieves a context that might typically suffice for answering a query. If this context, assessed by a character count (e.g., less than 100,000 characters), is deemed insufficient for a precise response, the model expands the window by including additional text or documents. This process continues until the context is sufficiently large, ensuring that the nuances and details necessary for accurate financial reporting are preserved in the model's responses, thereby adapting to the variable information density and relevance found in different sections of financial documents. These are about 2 out of the 7 points that we changed. Thanks so much for showing interest, id love to answer any more questions.
@@sohamagarwal00 so other than the simple api request and parameter changes. my main concern is promoting this half assed chatbot as ai powered when in truth it is nothing but a search and query feature for SEC filings. and saying that it still produces errors is basically stating that its a really bad search and query bot no better than a simple sql library.
