Fostering effective hybrid human-LLM reasoning and decision making
The impressive performance of modern Large Language Models (LLMs) across a wide range of tasks, along with their often non-trivial errors, has garnered unprecedented attention regarding the potential of AI and its impact on everyday life. While considerable effort has been and continues to be dedica...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1464690/full |
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| author | Andrea Passerini Aryo Gema Pasquale Minervini Burcu Sayin Katya Tentori |
| author_facet | Andrea Passerini Aryo Gema Pasquale Minervini Burcu Sayin Katya Tentori |
| author_sort | Andrea Passerini |
| collection | DOAJ |
| description | The impressive performance of modern Large Language Models (LLMs) across a wide range of tasks, along with their often non-trivial errors, has garnered unprecedented attention regarding the potential of AI and its impact on everyday life. While considerable effort has been and continues to be dedicated to overcoming the limitations of current models, the potentials and risks of human-LLM collaboration remain largely underexplored. In this perspective, we argue that enhancing the focus on human-LLM interaction should be a primary target for future LLM research. Specifically, we will briefly examine some of the biases that may hinder effective collaboration between humans and machines, explore potential solutions, and discuss two broader goals—mutual understanding and complementary team performance—that, in our view, future research should address to enhance effective human-LLM reasoning and decision-making. |
| format | Article |
| id | doaj-art-d61fcc9959834a5eb6cdd0bc5f6fec93 |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-d61fcc9959834a5eb6cdd0bc5f6fec932025-01-08T06:12:23ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01710.3389/frai.2024.14646901464690Fostering effective hybrid human-LLM reasoning and decision makingAndrea Passerini0Aryo Gema1Pasquale Minervini2Burcu Sayin3Katya Tentori4Department of Information Engineering and Computer Science, University of Trento, Trento, ItalySchool of Informatics, University of Edinburgh, Edinburgh, United KingdomSchool of Informatics, University of Edinburgh, Edinburgh, United KingdomDepartment of Information Engineering and Computer Science, University of Trento, Trento, ItalyCenter for Mind/Brain Sciences, University of Trento, Trento, ItalyThe impressive performance of modern Large Language Models (LLMs) across a wide range of tasks, along with their often non-trivial errors, has garnered unprecedented attention regarding the potential of AI and its impact on everyday life. While considerable effort has been and continues to be dedicated to overcoming the limitations of current models, the potentials and risks of human-LLM collaboration remain largely underexplored. In this perspective, we argue that enhancing the focus on human-LLM interaction should be a primary target for future LLM research. Specifically, we will briefly examine some of the biases that may hinder effective collaboration between humans and machines, explore potential solutions, and discuss two broader goals—mutual understanding and complementary team performance—that, in our view, future research should address to enhance effective human-LLM reasoning and decision-making.https://www.frontiersin.org/articles/10.3389/frai.2024.1464690/fullhybrid intelligencehuman-AI collaborationLLMsbiasesmutual understandingcomplementary team performance |
| spellingShingle | Andrea Passerini Aryo Gema Pasquale Minervini Burcu Sayin Katya Tentori Fostering effective hybrid human-LLM reasoning and decision making Frontiers in Artificial Intelligence hybrid intelligence human-AI collaboration LLMs biases mutual understanding complementary team performance |
| title | Fostering effective hybrid human-LLM reasoning and decision making |
| title_full | Fostering effective hybrid human-LLM reasoning and decision making |
| title_fullStr | Fostering effective hybrid human-LLM reasoning and decision making |
| title_full_unstemmed | Fostering effective hybrid human-LLM reasoning and decision making |
| title_short | Fostering effective hybrid human-LLM reasoning and decision making |
| title_sort | fostering effective hybrid human llm reasoning and decision making |
| topic | hybrid intelligence human-AI collaboration LLMs biases mutual understanding complementary team performance |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1464690/full |
| work_keys_str_mv | AT andreapasserini fosteringeffectivehybridhumanllmreasoninganddecisionmaking AT aryogema fosteringeffectivehybridhumanllmreasoninganddecisionmaking AT pasqualeminervini fosteringeffectivehybridhumanllmreasoninganddecisionmaking AT burcusayin fosteringeffectivehybridhumanllmreasoninganddecisionmaking AT katyatentori fosteringeffectivehybridhumanllmreasoninganddecisionmaking |