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Andrew Parry

2 accepted papers

2024

Exploiting Positional Bias for Query-Agnostic Generative Content in Search

ACL 2024findings

In recent years, research shows that neural ranking models (NRMs) substantially outperform their lexical counterparts in text retrieval. In traditional search pipelines, a combination of features leads to well-defined behaviour. However, as neural approaches become increasingly prevalent as the fina…

2024

Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model

EMNLP 2024finding

A supervised ranking model, despite its effectiveness over traditional approaches, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that c…