Topics, emotional valence, and Big Five personality traits — extracted from embedding geometry. One model, no LLM, just cosine distances to anchor texts and k-means clustering. What your posting reveals.
WebSeitz/wiki
aka FiveFactor, OCEAN - Personality Test - In psychological trait theory, the Big Five personality traits, also known as the OCEAN model is a suggested taxonomy, or grouping, for personality traits,[1] developed from the 1980s onwards. When factor analysis (a statistical technique) is applied to personality survey data, it reveals semantic associations: some words used to describe aspects of personality are often applied to the same person. For example, someone described as conscientious is more likely to be described as "always prepared" rather than "messy". These associations suggest five broad dimensions used in common language to describe the human personality and psyche.[2][3] The theory identifies five factors: openness to experience (inventive/curious vs. consistent/cautious); conscientiousness (efficient/organized vs. extravagant/careless); extraversion (outgoing/energetic vs. solitary/reserved); agreeableness (friendly/compassionate vs. challenging/callous); neuroticism (sensitive/nervous vs. resilient/confident) https://en.wikipedia.org/wiki/Big_Five_personality_traits (more)
Carryover Effect in LLM Personality Measurement
Large language models are increasingly evaluated with human personality inventories, yet prior critiques mainly frame contextual variation as instability or explain directional shifts as user-targeted adaptation, such as sycophancy. We propose the personality carryover effect, a content-targeted contextual bias in which personality-related content in prior context shifts later questionnaire responses even when it does not reflect the user's own stance. Across MBTI and BFI-2 on two model families, third-person personality narratives systematically shift measured personality toward the described profile. Crucially, the shift persists when the user explicitly states dislike of that profile, distinguishing carryover from purely user-targeted adaptation. These findings identify a distinct validity threat to in-context personality measurement in LLMs.
The Case Against LLMs as Rerankers
Authors: Apoorva Joshi, Zhenmei Shi, Akshay Goindani, Hong LiuResearch Leads: Zhenmei Shi, Akshay Goindani, Hong Liu Large language models are increasingly being used for a broad range of tasks, in…
Giving LLMs a personality is just good engineering
AI skeptics often argue that current AI systems shouldn’t be so human-like. The idea - most recently expressed in this opinion piece by Nathan Beacom - is that language models should explicitly be tools, like calculators or search engines. Although they can pretend to be people, they shouldn’t, because it encourages users to overestimate AI capabilities and (at worst) slip into AI psychosis. Here’s a representative paragraph from the piece:
Emotion concepts in a large language model
All modern language models sometimes act like they have emotions. What’s behind these behaviors? Our interpretability team investigates.
novelty — mino.mobi
How much does a poster explore their semantic space? Each post is embedded and measured against the running centroid of everything they've said before. High novelty = departing from the usual. Low = on-brand. Based on Zimmerman 2026.
A sampling model of social judgment.
The Chameleon's Limit Investigating Persona Collapse and Homogenization in Large Language Models
The Chameleon's Limit Investigating Persona Collapse and Homogenization in Large Language Models
Meandering on Manifolds: The Neural Geometry of Stories Over Time
To fully understand LLM representations, we must understand how they change dynamically, over the course of a prompt or conversation. We investigate these temporal dynamics with a simple case study: how do LLMs represent human emotions while reading short stories, both geometrically (in activation space) and temporally (changing from sentence to sentence)?
Meandering on Manifolds: The Neural Geometry of Stories Over Time
To fully understand LLM representations, we must understand how they change dynamically, over the course of a prompt or conversation. We investigate these temporal dynamics with a simple case study: how do LLMs represent human emotions while reading short stories, both geometrically (in activation space) and temporally (changing from sentence to sentence)?
The Fragility Of Moral Judgment In Large Language Models
People increasingly use large language models (LLMs) for everyday moral and interpersonal guidance, yet these systems cannot interrogate missing context and judge dilemmas as presented. We introduce a perturbation framework for testing the stability and manipulability of LLM moral judgments while holding the underlying moral conflict constant. Using 2,939 dilemmas from r/AmItheAsshole (January-March 2025), we generate three families of content perturbations: surface edits (lexical/structural noise), point-of-view shifts (voice and stance neutralization), and persuasion cues (self-positioning, social proof, pattern admissions, victim framing). We also vary the evaluation protocol (output ordering, instruction placement, and unstructured prompting). We evaluated all variants with four models (GPT-4.1, Claude 3.7 Sonnet, DeepSeek V3, Qwen2.5-72B) (N=129,156 judgments). Surface perturbations produce low flip rates (7.5%), largely within the self-consistency noise floor (4-13%), whereas point-of-view shifts induce substantially higher instability (24.3%). A large subset of dilemmas (37.9%) is robust to surface noise yet flips under perspective changes, indicating that models condition on narrative voice as a pragmatic cue. Instability concentrates in morally ambiguous cases; scenarios where no party is assigned blame are most susceptible. Persuasion perturbations yield systematic directional shifts. Protocol choices dominate all other factors: agreement between structured protocols is only 67.6% (kappa=0.55), and only 35.7% of model-scenario units match across all three protocols. These results show that LLM moral judgments are co-produced by narrative form and task scaffolding, raising reproducibility and equity concerns when outcomes depend on presentation skill rather than moral substance.
NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs'...
Large language models (LLMs) are increasingly used in peer review at major AI conferences, yet novelty remains a persistent weak point. Existing benchmarks assess novelty as a single holistic...
Abeba Birhane on Twitter / X
a classic case of “the human mind is afforded less complexity than is owed, and the computer is afforded more wisdom than is due.” Baria and Cross (2021)that "vibe" is at the core of what makes us human. it defies formalization and datafication https://t.co/AXELAqlYcq— Abeba Birhane (@Abebab) February 17, 2024