no code implementations • 16 Feb 2024 • Muhammad Shihab Rashid, Jannat Ara Meem, Yue Dong, Vagelis Hristidis
We study how to maximize the re-ranking performance given a budget, by navigating the vast search spaces of prompt choices, LLM APIs, and budget splits.
no code implementations • 16 Feb 2024 • Jannat Ara Meem, Muhammad Shihab Rashid, Yue Dong, Vagelis Hristidis
Existing work on Temporal Question Answering (TQA) has predominantly focused on questions anchored to specific timestamps or events (e. g. "Who was the US president in 1970?").
1 code implementation • 7 Feb 2024 • Muhammad Shihab Rashid, Jannat Ara Meem, Vagelis Hristidis
A typical OrConvQA pipeline consists of three modules: a Retriever to retrieve relevant documents from the collection, a Reranker to rerank them given the question and the context, and a Reader to extract an answer span.