Deception Abilities Emerged in Large Language Models

• URL: http://arxiv.org/abs/2307.16513v2
• Date: Fri, 2 Feb 2024 12:16:12 GMT
• Title: Deception Abilities Emerged in Large Language Models
• Authors: Thilo Hagendorff
• Abstract summary: Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. Thus, aligning them with human values is of great importance. However, given the steady increase in reasoning abilities, future LLMs are under suspicion of becoming able to deceive human operators and utilizing this ability to bypass monitoring efforts. As a prerequisite to this, LLMs need to possess a conceptual understanding of deception strategies. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents, that their performance in complex deception scenarios can be amplified utilizing chain-of-thought reasoning, and that eliciting Machiavellianism in LLMs can alter their propensity to deceive. In sum, revealing hitherto unknown machine behavior in LLMs, our study contributes to the nascent field of machine psychology.

Related papers

• Causality for Large Language Models [37.10970529459278]
  Large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks. Recent research highlights that LLMs function as causal parrots, capable of reciting causal knowledge without truly understanding or applying it. This survey aims to explore how causality can enhance LLMs at every stage of their lifecycle.
  arXiv  Detail & Related papers  (2024-10-20T07:22:23Z)
• Cognitive Overload Attack:Prompt Injection for Long Context [39.61095361609769]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in performing tasks without needing explicit retraining. This capability, known as In-Context Learning (ICL), exposes LLMs to adversarial prompts and jailbreaks that manipulate safety-trained LLMs into generating undesired or harmful output. We apply the principles of Cognitive Load Theory in LLMs and empirically validate that similar to human cognition, LLMs also suffer from cognitive overload. We show that advanced models such as GPT-4, Claude-3.5 Sonnet, Claude-3 OPUS, Llama-3-70B-Instruct, Gemini-1.0-Pro, and
  arXiv  Detail & Related papers  (2024-10-15T04:53:34Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models [51.91448005607405]
  We evaluate key human ToM precursors by annotating characters' perceptions on ToMi and FANToM. We present PercepToM, a novel ToM method leveraging LLMs' strong perception inference capability while supplementing their limited perception-to-belief inference.
  arXiv  Detail & Related papers  (2024-07-08T14:58:29Z)
• When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation [66.01754585188739]
  Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence.
  arXiv  Detail & Related papers  (2024-02-18T04:57:19Z)
• Systematic Biases in LLM Simulations of Debates [12.933509143906141]
  We study the limitations of Large Language Models in simulating human interactions. Our findings indicate a tendency for LLM agents to conform to the model's inherent social biases. These results underscore the need for further research to develop methods that help agents overcome these biases.
  arXiv  Detail & Related papers  (2024-02-06T14:51:55Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding [1.3654846342364308]
  Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. This position paper critically assesses three points recurring in critiques of LLM capacities. We outline a pragmatic perspective on the issue of real' understanding and intentionality in LLMs.
  arXiv  Detail & Related papers  (2023-10-30T15:51:04Z)
• Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation [80.126717170151]
  This study utilizes the intricate Avalon game as a testbed to explore LLMs' potential in deceptive environments. We introduce a novel framework, Recursive Contemplation (ReCon), to enhance LLMs' ability to identify and counteract deceptive information.
  arXiv  Detail & Related papers  (2023-10-02T16:27:36Z)
• Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs [0.0]
  It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI. Social and ethical matters regarding LLMs are very briefly touched from this perspective.
  arXiv  Detail & Related papers  (2023-09-19T07:12:55Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)

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.