AI chatbots using reason emit more carbon than those responding concisely, study finds
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A study found that carbon emissions from chat-based generative AI can be six times higher when responding to complex prompts, like abstract algebra or philosophy, compared to simpler prompts, such as high school history."The environmental impact of questioning trained ( large-language models ) is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," first author Maximilian Dauner, a researcher at Hochschule Munchen University of Applied Sciences, Germany, said."We found that reasoning-enabled models produced up to 50 times more (carbon dioxide) emissions than concise response models ," Dauner added.The study, published in the journal Frontiers in Communication, evaluated how 14 large-language models (which power chatbots), including DeepSeek and Cogito, process information before responding to 1,000 benchmark questions -- 500 multiple-choice and 500 subjective.Each model responded to 100 questions on each of the five subjects chosen for the analysis -- philosophy, high school world history, international law, abstract algebra, and high school mathematics."Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain history items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an abstract algebra prompt," the authors wrote.Tokens are virtual objects created by conversational AI when processing a user's prompt in natural language. More tokens lead to increased carbon dioxide emissions.Chatbots equipped with an ability to reason, or ' reasoning models ', produced 543.5 'thinking' tokens per question, whereas concise models -- producing one-word answers -- required just 37.7 tokens per question, the researchers found.Thinking tokens are additional ones that reasoning models generate before producing an answer, they explained.However, more thinking tokens do not necessarily guarantee correct responses, as the team said, elaborate detail is not always essential for correctness.Dauner said, "None of the models that kept emissions below 500 grams of CO₂ equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly.""Currently, we see a clear accuracy-sustainability trade-off inherent in (large-language model) technologies," the author added.The most accurate performance was seen in the reasoning model Cogito, with a nearly 85 per cent accuracy in responses, whilst producing three times more carbon dioxide emissions than similar-sized models generating concise answers."In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand," the authors wrote."Optimising reasoning efficiency and response brevity, particularly for challenging subjects like abstract algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies," they wrote.
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Time of India
2 days ago
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Algebra, philosophy and…: These AI chatbot queries cause most harm to environment, study claims
Representative Image Queries demanding complex reasoning from AI chatbots, such as those related to abstract algebra or philosophy, generate significantly more carbon emissions than simpler questions, a new study reveals. These high-level computational tasks can produce up to six times more emissions than straightforward inquiries like basic history questions. A study conducted by researchers at Germany's Hochschule München University of Applied Sciences, published in the journal Frontiers (seen by The Independent), found that the energy consumption and subsequent carbon dioxide emissions of large language models (LLMs) like OpenAI's ChatGPT vary based on the chatbot, user, and subject matter. An analysis of 14 different AI models consistently showed that questions requiring extensive logical thought and reasoning led to higher emissions. To mitigate their environmental impact, the researchers have advised frequent users of AI chatbots to consider adjusting the complexity of their queries. Why do these queries cause more carbon emissions by AI chatbots In the study, author Maximilian Dauner wrote: 'The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions. We found that reasoning-enabled models produced up to 50 times more carbon dioxide emissions than concise response models.' by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Americans Are Freaking Out Over This All-New Hyundai Tucson (Take a Look) Smartfinancetips Learn More Undo The study evaluated 14 large language models (LLMs) using 1,000 standardised questions to compare their carbon emissions. It explains that AI chatbots generate emissions through processes like converting user queries into numerical data. On average, reasoning models produce 543.5 tokens per question, significantly more than concise models, which use only 40 tokens. 'A higher token footprint always means higher CO2 emissions,' the study adds. The study highlights that Cogito, one of the most accurate models with around 85% accuracy, generates three times more carbon emissions than other similarly sized models that offer concise responses. 'Currently, we see a clear accuracy-sustainability trade-off inherent in LLM technologies. None of the models that kept emissions below 500 grams of carbon dioxide equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly,' Dauner explained. Researchers used carbon dioxide equivalent to measure the climate impact of AI models and hope that their findings encourage more informed usage. For example, answering 600,000 questions with DeepSeek R1 can emit as much carbon as a round-trip flight from London to New York. In comparison, Alibaba Cloud's Qwen 2.5 can answer over three times more questions with similar accuracy while producing the same emissions. 'Users can significantly reduce emissions by prompting AI to generate concise answers or limiting the use of high-capacity models to tasks that genuinely require that power,' Dauner noted. AI Masterclass for Students. Upskill Young Ones Today!– Join Now
Time of India
2 days ago
- Time of India
AI chatbots using reason emit more carbon than those responding concisely, study finds
HighlightsA study revealed that carbon emissions from chat-based generative artificial intelligence can be up to six times higher when processing complex prompts, such as abstract algebra or philosophy, compared to simpler prompts like high school history. The research, conducted by Maximilian Dauner at Hochschule Munchen University of Applied Sciences, found that reasoning-enabled models produced significantly more carbon dioxide emissions than concise response models, with emissions reaching up to 50 times higher. The findings suggest a clear accuracy-sustainability trade-off in large-language model technologies, with the most accurate model, Cogito, achieving nearly 85 percent accuracy while generating three times more carbon dioxide emissions than smaller models. A study found that carbon emissions from chat-based generative AI can be six times higher when responding to complex prompts, like abstract algebra or philosophy, compared to simpler prompts, such as high school history. "The environmental impact of questioning trained ( large-language models ) is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions," first author Maximilian Dauner, a researcher at Hochschule Munchen University of Applied Sciences, Germany, said. "We found that reasoning-enabled models produced up to 50 times more (carbon dioxide) emissions than concise response models ," Dauner added. The study, published in the journal Frontiers in Communication, evaluated how 14 large-language models (which power chatbots), including DeepSeek and Cogito, process information before responding to 1,000 benchmark questions -- 500 multiple-choice and 500 subjective. Each model responded to 100 questions on each of the five subjects chosen for the analysis -- philosophy, high school world history, international law, abstract algebra, and high school mathematics. "Zero-token reasoning traces appear when no intermediate text is needed (e.g. Cogito 70B reasoning on certain history items), whereas the maximum reasoning burden (6.716 tokens) is observed for the Deepseek R1 7B model on an abstract algebra prompt," the authors wrote. Tokens are virtual objects created by conversational AI when processing a user's prompt in natural language. More tokens lead to increased carbon dioxide emissions. Chatbots equipped with an ability to reason, or ' reasoning models ', produced 543.5 'thinking' tokens per question, whereas concise models -- producing one-word answers -- required just 37.7 tokens per question, the researchers found. Thinking tokens are additional ones that reasoning models generate before producing an answer, they explained. However, more thinking tokens do not necessarily guarantee correct responses, as the team said, elaborate detail is not always essential for correctness. Dauner said, "None of the models that kept emissions below 500 grams of CO₂ equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly." "Currently, we see a clear accuracy-sustainability trade-off inherent in (large-language model) technologies," the author added. The most accurate performance was seen in the reasoning model Cogito, with a nearly 85 per cent accuracy in responses, whilst producing three times more carbon dioxide emissions than similar-sized models generating concise answers. "In conclusion, while larger and reasoning-enhanced models significantly outperform smaller counterparts in terms of accuracy, this improvement comes with steep increases in emissions and computational demand," the authors wrote. "Optimising reasoning efficiency and response brevity, particularly for challenging subjects like abstract algebra, is crucial for advancing more sustainable and environmentally conscious artificial intelligence technologies," they wrote.
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