Mitigating the Carbon Footprint of Chatbots as Consumers

TL;DR

This paper investigates how users can reduce the environmental impact of chatbots by understanding the energy costs of conversational memory and proposing sustainable usage practices based on data analysis and simulations.

Contribution

It identifies the environmental impact of chatbot conversational memory and offers practical recommendations for users to minimize emissions.

Findings

Analysis of real-world conversation data reveals energy consumption patterns.

Simulations estimate significant emission reductions through sustainable chatbot usage.

Practical guidelines can help users lower the carbon footprint of their interactions.

Abstract

In the context of the high energy demand of large language models (LLMs) and growing concerns about global warming, there is significant demand for actionable recommendations that can help reduce emissions when utilizing such technologies. This paper examines the environmental impact linked to a fundamental function of LLM-based conversational systems that might be less well known to end users: the conversational memory, which enables the system to maintain context throughout the dialog. After analyzing conversation patterns using anonymized token data from a real world system, a recommendation for individuals on how they could use chatbots in a more sustainable way is derived. Based on a simulation, the savings potential resulting from the adoption of such an ecological gesture is estimated.