Trying to be human: Linguistic traces of stochastic empathy in language models

• URL: http://arxiv.org/abs/2410.01675v1
• Date: Wed, 2 Oct 2024 15:46:40 GMT
• Title: Trying to be human: Linguistic traces of stochastic empathy in language models
• Authors: Bennett Kleinberg, Jari Zegers, Jonas Festor, Stefana Vida, Julian Präsent, Riccardo Loconte, Sanne Peereboom,
• Abstract summary: Large language models (LLMs) are crucial drivers behind the increased quality of computer-generated content. Our work tests how two important factors contribute to the human vs AI race: empathy and an incentive to appear human.
• Score: 0.2638512174804417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Differentiating between generated and human-written content is important for navigating the modern world. Large language models (LLMs) are crucial drivers behind the increased quality of computer-generated content. Reportedly, humans find it increasingly difficult to identify whether an AI model generated a piece of text. Our work tests how two important factors contribute to the human vs AI race: empathy and an incentive to appear human. We address both aspects in two experiments: human participants and a state-of-the-art LLM wrote relationship advice (Study 1, n=530) or mere descriptions (Study 2, n=610), either instructed to be as human as possible or not. New samples of humans (n=428 and n=408) then judged the texts' source. Our findings show that when empathy is required, humans excel. Contrary to expectations, instructions to appear human were only effective for the LLM, so the human advantage diminished. Computational text analysis revealed that LLMs become more human because they may have an implicit representation of what makes a text human and effortlessly apply these heuristics. The model resorts to a conversational, self-referential, informal tone with a simpler vocabulary to mimic stochastic empathy. We discuss these findings in light of recent claims on the on-par performance of LLMs.

Related papers

• The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters [67.61587661660852]
  Theory-of-Mind (ToM) allows humans to understand and interpret the mental states of others. In this paper, we verify the importance of understanding long personal backgrounds in ToM. We assess the performance of machines' ToM capabilities in realistic evaluation scenarios.
  arXiv  Detail & Related papers  (2025-01-03T09:04:45Z)
• HLB: Benchmarking LLMs' Humanlikeness in Language Use [2.438748974410787]
  We present a comprehensive humanlikeness benchmark (HLB) evaluating 20 large language models (LLMs) We collected responses from over 2,000 human participants and compared them to outputs from the LLMs in these experiments. Our results reveal fine-grained differences in how well LLMs replicate human responses across various linguistic levels.
  arXiv  Detail & Related papers  (2024-09-24T09:02:28Z)
• Strong and weak alignment of large language models with human values [1.6590638305972631]
  Minimizing negative impacts of Artificial Intelligent (AI) systems requires them to be able to align with human values. We argue that this is required for AI systems like large language models (LLMs) to be able to recognize situations presenting a risk that human values may be flouted. We propose a new thought experiment that we call "the Chinese room with a word transition dictionary", in extension of John Searle's famous proposal.
  arXiv  Detail & Related papers  (2024-08-05T11:27:51Z)
• Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance [73.19687314438133]
  We study how reliance is affected by contextual features of an interaction. We find that contextual characteristics significantly affect human reliance behavior. Our results show that calibration and language quality alone are insufficient in evaluating the risks of human-LM interactions.
  arXiv  Detail & Related papers  (2024-07-10T18:00:05Z)
• Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas [14.650234624251716]
  Large language models (LLMs) are increasingly being used in human-centered social scientific tasks. These tasks are highly subjective and dependent on human factors, such as one's environment, attitudes, beliefs, and lived experiences. We examine the role of prompting LLMs with human-like personas and ask the models to answer as if they were a specific human.
  arXiv  Detail & Related papers  (2024-06-20T16:24:07Z)
• Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments [0.0]
  Large Language Models (LLMs) are already as persuasive as humans. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments.
  arXiv  Detail & Related papers  (2024-04-14T19:01:20Z)
• Divergences between Language Models and Human Brains [59.100552839650774]
  We systematically explore the divergences between human and machine language processing. We identify two domains that LMs do not capture well: social/emotional intelligence and physical commonsense. Our results show that fine-tuning LMs on these domains can improve their alignment with human brain responses.
  arXiv  Detail & Related papers  (2023-11-15T19:02:40Z)
• Humans and language models diverge when predicting repeating text [52.03471802608112]
  We present a scenario in which the performance of humans and LMs diverges. Human and GPT-2 LM predictions are strongly aligned in the first presentation of a text span, but their performance quickly diverges when memory begins to play a role. We hope that this scenario will spur future work in bringing LMs closer to human behavior.
  arXiv  Detail & Related papers  (2023-10-10T08:24:28Z)
• AI, write an essay for me: A large-scale comparison of human-written versus ChatGPT-generated essays [66.36541161082856]
  ChatGPT and similar generative AI models have attracted hundreds of millions of users. This study compares human-written versus ChatGPT-generated argumentative student essays.
  arXiv  Detail & Related papers  (2023-04-24T12:58:28Z)
• Context Limitations Make Neural Language Models More Human-Like [32.488137777336036]
  We show discrepancies in context access between modern neural language models (LMs) and humans in incremental sentence processing. Additional context limitation was needed to make LMs better simulate human reading behavior. Our analyses also showed that human-LM gaps in memory access are associated with specific syntactic constructions.
  arXiv  Detail & Related papers  (2022-05-23T17:01:13Z)
• Towards Abstract Relational Learning in Human Robot Interaction [73.67226556788498]
  Humans have a rich representation of the entities in their environment. If robots need to interact successfully with humans, they need to represent entities, attributes, and generalizations in a similar way. In this work, we address the problem of how to obtain these representations through human-robot interaction.
  arXiv  Detail & Related papers  (2020-11-20T12:06:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.