204 citations · 276 across the 17 of their papers we have counts for
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It's All Relative! -- A Synthetic Query Generation Approach for Improving Zero-Shot Relevance Prediction
Aditi Chaudhary, Karthik Raman, Michael Bendersky
Recent developments in large language models (LLMs) have shown promise in their ability to generate synthetic query-document pairs by prompting with as few as 8 demonstrations. Thi…
Take One Step at a Time to Know Incremental Utility of Demonstration: An Analysis on Reranking for Few-Shot In-Context Learning
Kazuma Hashimoto, Karthik Raman, Michael Bendersky
In-Context Learning (ICL) is an emergent capability of Large Language Models (LLMs). Only a few demonstrations enable LLMs to be used as blackbox for new tasks. Previous studies ha…
Ambiguity-Aware In-Context Learning with Large Language Models
Lingyu Gao, Aditi Chaudhary, Krishna Srinivasan +3
In-context learning (ICL) i.e. showing LLMs only a few task-specific demonstrations has led to downstream gains with no task-specific fine-tuning required. However, LLMs are sensit…
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
Aditi Chaudhary, Karthik Raman, Krishna Srinivasan +3
Query-document relevance prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained) transformer-based models w…