Large language models (LLMs) exhibit us-vs.-them bias: A behavioral asymmetry in which prompts framed around an ingroup (``we''/``us'') receive systematically more positive continuations than matched prompts framed around an outgroup (``they''/``them''). Using Edge Attribution Patching (EAP), we localize this behavior...
Tabia Tanzin Prama, J. Zimmerman, C. Danforth et al.· 0 citations
The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they cannot move beyond statistical pattern matching into abstraction or reasoning, remaining ontologically near the lower bound of pattern reuse despite producing alluringly fluent tex...
J. Zimmerman, C. Beauregard, Tabia Tanzin Prama et al.· 0 citations
The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they cannot move beyond statistical pattern matching into abstraction or reasoning, remaining ontologically near the lower bound of pattern reuse despite producing alluringly fluent tex...
J. Zimmerman, C. Beauregard, Tabia Tanzin Prama et al.· 0 citations
This work map the self-reported personality archetypes of 22 LLMs spanning closed-source frontier systems and open-source models, providing a reproducible, character-grounded framework for evaluating what LLMs are, not just what they do.
Tabia Tanzin Prama, C. Beauregard, C. Danforth et al.· 0 citations
Storytelling inherently revolves around characters. Using the television sitcom `Friends'as a case study, we investigate how well archetype vectors capture both individual characterization and the relational structure of a specific ensemble. Our work is based on the archetypometrics framework, which locates 2,000 ficti...
Shunyou Zhang, Tabia Tanzin Prama, C. M. Danforth et al.· 0 citations
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