Vagelis Hristidis — accepted AI-conference papers

2024

EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models

Muhammad Rashid, Jannat Meem, Yue Dong, Vagelis Hristidis

ACL 2024findings

Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strategies. A limitation of these ranking strategies with LLMs is their cost: the pro…

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2024

PAT-Questions: A Self-Updating Benchmark for Present-Anchored Temporal Question-Answering

Jannat Meem, Muhammad Rashid, Yue Dong, Vagelis Hristidis

ACL 2024findings

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?’). Little work has studied questions whose temporal context is relative to the present time (e.g. ‘Who was the previous US pres…

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