mlsanityData to 14 Oct 2026

Performance Gains of LLMs With Humans in a World of LLMs Versus Humans

arXiv 2505.08902 ↗First posted 13 May 2025cs.HC cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 13 May 2025.

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References · 19

  1. 2022Emergent Abilities of Large Language Modelscited by 2,512
  2. 2023Language Models Represent Space and Timecited by 316
  3. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  4. 2024Evaluating the World Model Implicit in a Generative Modelcited by 113
  5. 2023Generative Language Models Exhibit Social Identity Biasescited by 111
  6. 2024Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approachcited by 76
  7. 2024A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Reviewcited by 66
  8. 2024The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunitiescited by 64
  9. 2023Emotional Intelligence of Large Language Modelscited by 58
  10. 2024Enhancing Contextual Understanding in Large Language Models through Contrastive Decodingcited by 49
  11. 2024Uncertainty Quantification for In-Context Learning of Large Language Modelscited by 45
  12. 2023Quantifying Uncertainty in Natural Language Explanations of Large Language Modelscited by 32
7 more
  1. 2024Can Large Language Models Understand Context?cited by 26
  2. 2024Can LLMs make trade-offs involving stipulated pain and pleasure states?cited by 16
  3. 2023NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMscited by 14
  4. 2024Uncertainty is Fragile: Manipulating Uncertainty in Large Language Modelscited by 13
  5. 2024Pre-Trained Language Models Represent Some Geographic Populations Better Than Otherscited by 6
  6. 2024Academic collaboration on large language model studies increases overall but varies across disciplinescited by 6
  7. 2024Empathy and the Right to Be an Exception: What LLMs Can and Cannot Docited by 4

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