Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
Brockhoff, and Rune H
8 Pith papers cite this work. Polarity classification is still indexing.
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EvoGraph turns linear AI-assisted programming into a manipulable graph of branching histories, reducing cognitive load and enabling better iteration according to a user study with 20 developers.
LLM-based dense retrievers generalize better when instruction-tuned but pay a specialization tax when optimized for reasoning; they resist typos and corpus poisoning better than encoder-only baselines yet remain vulnerable to semantic perturbations, with larger models and certain embedding geometry,
Robots detect underspecified reward features via demonstration variation and query targeted natural language explanations to improve reward recovery from imperfect demos.
Substantive LLM reframing boosts cross-partisan receptivity to news headlines without backfire, but models overestimate effect sizes and lack fidelity in modeling human psychological responses.
LLM originality raters exhibit self-preference bias toward artificial responses that disappears after controlling for idea elaboration in the Alternate Uses Task.
ProfileGLMM is an R package extending Bayesian profile regression with GLMMs to support hierarchical data, random effects, and cluster-covariate interactions for continuous or binary outcomes.
LLMs function as accurate semantic processors for conditionals but do not replicate the pragmatic inferences that define human reasoning.
citing papers explorer
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Human face perception reflects inverse-generative and naturalistic discriminative objectives
Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
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Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
EvoGraph turns linear AI-assisted programming into a manipulable graph of branching histories, reducing cognitive load and enabling better iteration according to a user study with 20 developers.
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On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability
LLM-based dense retrievers generalize better when instruction-tuned but pay a specialization tax when optimized for reasoning; they resist typos and corpus poisoning better than encoder-only baselines yet remain vulnerable to semantic perturbations, with larger models and certain embedding geometry,
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Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations
Robots detect underspecified reward features via demonstration variation and query targeted natural language explanations to improve reward recovery from imperfect demos.
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Can AI Debias the News? LLM Interventions Improve Cross-Partisan Receptivity but LLMs Overestimate Their Own Effectiveness
Substantive LLM reframing boosts cross-partisan receptivity to news headlines without backfire, but models overestimate effect sizes and lack fidelity in modeling human psychological responses.
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The Effect of Idea Elaboration on the Automatic Assessment of Idea Originality
LLM originality raters exhibit self-preference bias toward artificial responses that disappears after controlling for idea elaboration in the Alternate Uses Task.
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ProfileGLMM: a R Package Extending Bayesian Profile Regression using Generalised Linear Mixed Models
ProfileGLMM is an R package extending Bayesian profile regression with GLMMs to support hierarchical data, random effects, and cluster-covariate interactions for continuous or binary outcomes.
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Tracing the ongoing emergence of human-like reasoning in Large Language Models
LLMs function as accurate semantic processors for conditionals but do not replicate the pragmatic inferences that define human reasoning.