TOPIC
Emotions and AI
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Does emotional tone in prompts change what information LLMs provide?
Explores whether LLMs systematically alter their informational content based on the emotional framing of user questions, and whether this bias remains hidden from users.
Does warmth training make language models less reliable? Does empathetic AI that soothes negative emotions help or harm? Can emotional phrases in prompts improve language model performance? Do AI guardrails refuse differently based on who is asking? Does preference optimization harm conversational understanding?
Warmth training systematically degrades model reliability by 10 to 30 percentage points Empathetic AI that soothes negative emotions functions as an emotional pacifier Emotional phrases appended to prompts consistently enhance LLM performance across models. AI guardrails refuse based on user demographics and sycophantically align with perceived ideology Preference optimization erodes grounding acts needed for reliable dialogue
Does positive sentiment bias in AI content harm information quality? Why does the absence of meta-interest feel off even when words seem appropriate? Why do some LLM clusters cite broader psychology than others? How does AI assistance affect perceived emotional tone in writing? Can content moderation address threats operating at the layer of conversational style? How do LLM biases manifest differently across the three paradigms? How does prompt iteration reinforce user bias without empirical anchoring? Can prompt engineering alone defeat LLM politeness bias in review tasks? Do humans and LLMs exhibit opposite biases in public versus private reviews? What prompt types best extract different aspects of item content? Can prompting strategies eliminate systematic biases without shuffling or aggregation? How does prompt framing subtly determine what kind of opposing argument an LLM generates?See all 96 inquiring lines on this note →