Improving Retrieval for RAG based Question Answering Models on Financial Documents

no code implementations • 23 Mar 2024 • Spurthi Setty, Katherine Jijo, Eden Chung, Natan Vidra

The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques.

Chunking Question Answering +3

Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately

no code implementations • 27 Jan 2024 • Liang Zhang, Katherine Jijo, Spurthi Setty, Eden Chung, Fatima Javid, Natan Vidra, Tommy Clifford

Large Language Models (LLMs) generate responses to questions; however, their effectiveness is often hindered by sub-optimal quality of answers and occasional failures to provide accurate responses to questions.

GPT-3.5 Language Modelling +2

Improving Classification Performance With Human Feedback: Label a few, we label the rest

no code implementations • 17 Jan 2024 • Natan Vidra, Thomas Clifford, Katherine Jijo, Eden Chung, Liang Zhang

In the realm of artificial intelligence, where a vast majority of data is unstructured, obtaining substantial amounts of labeled data to train supervised machine learning models poses a significant challenge.

Active Learning GPT-3.5 +2