PrefBench benchmark shows zero-shot LLMs achieve deal rates above 0.99 but seller profits only slightly above random and far below a simple concession heuristic across 7,500 episodes.
Mohammad and Peter D
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
representative citing papers
Maximizing reachability in k-path temporal graphs via budgeted shifts is FPT when parameterized by k and b together or by k alone, but intractable in most other parameterizations with matching XP algorithms.
Introduces the first interpersonal emotion dataset from congressional tweets and demonstrates that joint neural modeling of interpersonal group relationships and emotions yields performance gains on both.
LLMs produce overly positive idealized depictions of disability in simulated social media posts that do not match real posts by people with disabilities and show topic bias favoring nondisabled people.
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.
citing papers explorer
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PrefBench: Evaluating Zero-Shot LLM Agents in Hidden-Preference Personalized Pricing Negotiations
PrefBench benchmark shows zero-shot LLMs achieve deal rates above 0.99 but seller profits only slightly above random and far below a simple concession heuristic across 7,500 episodes.
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Maximizing Reachability via Shifting of Temporal Paths
Maximizing reachability in k-path temporal graphs via budgeted shifts is FPT when parameterized by k and b together or by k alone, but intractable in most other parameterizations with matching XP algorithms.
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How people talk about each other: Modeling Generalized Intergroup Bias and Emotion
Introduces the first interpersonal emotion dataset from congressional tweets and demonstrates that joint neural modeling of interpersonal group relationships and emotions yields performance gains on both.
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Shiny Stories, Hidden Struggles: Investigating the Representation of Disability Through the Lens of LLMs
LLMs produce overly positive idealized depictions of disability in simulated social media posts that do not match real posts by people with disabilities and show topic bias favoring nondisabled people.
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A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.