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Researchers induce pathology-like behaviors in language models via fine-tuning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework to fine-tune language models, inducing specific behavioral patterns like depression and paranoia. This process modifies the models' policies, leading to stable, context-general shifts in their generative distributions, such as assigning higher probabilities to negative and threat-related interpretations. The study demonstrates that these induced behavioral profiles are partially specific, with different training objectives leading to distinct response tendencies, suggesting that structured behavioral training can shape emergent representational structures in LLMs. AI

IMPACT This research highlights the potential for controlled behavioral manipulation in LLMs, raising questions about their use as cognitive models and the safety implications of inducing specific behavioral biases.

RANK_REASON The cluster contains an academic paper detailing a new method for fine-tuning language models.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researchers induce pathology-like behaviors in language models via fine-tuning

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The cluster contains an academic paper detailing a new method for fine-tuning language models.
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103 days old
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Nicola Milano, Davide Marocco ·

    Modeling Pathology-Like Behavioral Patterns in Language Models Through Behavioral Fine-Tuning

    arXiv:2605.22356v1 Announce Type: new Abstract: Large language models are increasingly used as computational tools for modeling human-like behavior. We introduce a behavioral induction framework that modifies model policies through fine-tuning on structured decision-making tasks:…

  2. arXiv cs.CL TIER_1 English(EN) · Davide Marocco ·

    Modeling Pathology-Like Behavioral Patterns in Language Models Through Behavioral Fine-Tuning

    Large language models are increasingly used as computational tools for modeling human-like behavior. We introduce a behavioral induction framework that modifies model policies through fine-tuning on structured decision-making tasks: using synthetic datasets inspired by maladaptiv…

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