Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models

06/29/2023
by   Guido Zuccon, et al.
0

As in other fields of artificial intelligence, the information retrieval community has grown interested in investigating the power consumption associated with neural models, particularly models of search. This interest has become particularly relevant as the energy consumption of information retrieval models has risen with new neural models based on large language models, leading to an associated increase of CO2 emissions, albeit relatively low compared to fields such as natural language processing.

READ FULL TEXT

page 2

page 5

research
07/27/2022

Lecture Notes on Neural Information Retrieval

These lecture notes focus on the recent advancements in neural informati...
research
08/14/2023

Large Language Models for Information Retrieval: A Survey

As a primary means of information acquisition, information retrieval (IR...
research
11/08/2016

Getting Started with Neural Models for Semantic Matching in Web Search

The vocabulary mismatch problem is a long-standing problem in informatio...
research
06/11/2018

Distributed Evaluations: Ending Neural Point Metrics

With the rise of neural models across the field of information retrieval...
research
09/29/1998

Using Local Optimality Criteria for Efficient Information Retrieval with Redundant Information Filters

We consider information retrieval when the data, for instance multimedia...
research
04/02/2021

Humor@IITK at SemEval-2021 Task 7: Large Language Models for Quantifying Humor and Offensiveness

Humor and Offense are highly subjective due to multiple word senses, cul...
research
08/04/2023

ChatGPT for GTFS: From Words to Information

The General Transit Feed Specification (GTFS) standard for publishing tr...

Please sign up or login with your details

Forgot password? Click here to reset