Performance Gains of LLMs With Humans in a World of LLMs Versus Humans
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908362567843840 |
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| author | McCullum, Lucas Agassi, Pelagie Ami Celi, Leo Anthony Ebner, Daniel K. Fernandes, Chrystinne Oliveira Hicklen, Rachel S. Koumbia, Mkliwa Lehmann, Lisa Soleymani Restrepo, David |
| author_facet | McCullum, Lucas Agassi, Pelagie Ami Celi, Leo Anthony Ebner, Daniel K. Fernandes, Chrystinne Oliveira Hicklen, Rachel S. Koumbia, Mkliwa Lehmann, Lisa Soleymani Restrepo, David |
| contents | Currently, a considerable research effort is devoted to comparing LLMs to a group of human experts, where the term "expert" is often ill-defined or variable, at best, in a state of constantly updating LLM releases. Without proper safeguards in place, LLMs will threaten to cause harm to the established structure of safe delivery of patient care which has been carefully developed throughout history to keep the safety of the patient at the forefront. A key driver of LLM innovation is founded on community research efforts which, if continuing to operate under "humans versus LLMs" principles, will expedite this trend. Therefore, research efforts moving forward must focus on effectively characterizing the safe use of LLMs in clinical settings that persist across the rapid development of novel LLM models. In this communication, we demonstrate that rather than comparing LLMs to humans, there is a need to develop strategies enabling efficient work of humans with LLMs in an almost symbiotic manner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08902 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Performance Gains of LLMs With Humans in a World of LLMs Versus Humans McCullum, Lucas Agassi, Pelagie Ami Celi, Leo Anthony Ebner, Daniel K. Fernandes, Chrystinne Oliveira Hicklen, Rachel S. Koumbia, Mkliwa Lehmann, Lisa Soleymani Restrepo, David Human-Computer Interaction Artificial Intelligence Computation and Language Currently, a considerable research effort is devoted to comparing LLMs to a group of human experts, where the term "expert" is often ill-defined or variable, at best, in a state of constantly updating LLM releases. Without proper safeguards in place, LLMs will threaten to cause harm to the established structure of safe delivery of patient care which has been carefully developed throughout history to keep the safety of the patient at the forefront. A key driver of LLM innovation is founded on community research efforts which, if continuing to operate under "humans versus LLMs" principles, will expedite this trend. Therefore, research efforts moving forward must focus on effectively characterizing the safe use of LLMs in clinical settings that persist across the rapid development of novel LLM models. In this communication, we demonstrate that rather than comparing LLMs to humans, there is a need to develop strategies enabling efficient work of humans with LLMs in an almost symbiotic manner. |
| title | Performance Gains of LLMs With Humans in a World of LLMs Versus Humans |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.08902 |