42 citations · 258 across the 28 of their papers we have counts for
8 papers · 1 filter
Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages
Mofetoluwa Adeyemi, Akintunde Oladipo, Ronak Pradeep +1
Large language models (LLMs) have shown impressive zero-shot capabilities in various document reranking tasks. Despite their successful implementations, there is still a gap in exi…
Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models
Manveer Singh Tamber, Ronak Pradeep, Jimmy Lin
Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results. However, these methods are not without drawbacks. The proposed methods rely on large L…
RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!
Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin
In information retrieval, proprietary large language models (LLMs) such as GPT-4 and open-source counterparts such as LLaMA and Vicuna have played a vital role in reranking. Howeve…
RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Ronak Pradeep, Sahel Sharifymoghaddam, Jimmy Lin
Researchers have successfully applied large language models (LLMs) such as ChatGPT to reranking in an information retrieval context, but to date, such work has mostly been built on…
Vector Search with OpenAI Embeddings: Lucene Is All You Need
Jimmy Lin, Ronak Pradeep, Tommaso Teofili +1
We provide a reproducible, end-to-end demonstration of vector search with OpenAI embeddings using Lucene on the popular MS MARCO passage ranking test collection. The main goal of o…
ReadProbe: A Demo of Retrieval-Enhanced Large Language Models to Support Lateral Reading
Dake Zhang, Ronak Pradeep
With the rapid growth and spread of online misinformation, people need tools to help them evaluate the credibility and accuracy of online information. Lateral reading, a strategy t…