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Found 10 papers, 3 papers with code
A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI
This has led to growing concerns about harms from AI persuasion and how they can be mitigated, highlighting the need for a systematic study of AI persuasion.
Fine-tuning language models to find agreement among humans with diverse preferences
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user.
Assessing Group-level Gender Bias in Professional Evaluations: The Case of Medical Student End-of-Shift Feedback
Although approximately 50% of medical school graduates today are women, female physicians tend to be underrepresented in senior positions, make less money than their male counterparts and receive fewer promotions.
Can language models learn from explanations in context?
In summary, explanations can support the in-context learning of large LMs on challenging tasks.
Learning to solve complex tasks by growing knowledge culturally across generations
The video game paradigm we pioneer here is thus a rich test bed for developing AI systems capable of acquiring and transmitting cultural knowledge.
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning
Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2").
Communicating Natural Programs to Humans and Machines
We present LARC, the \textit{Language-complete ARC}: a collection of natural language descriptions by a group of human participants who instruct each other on how to solve ARC tasks using language alone, which contains successful instructions for 88\% of the ARC tasks.
A practical introduction to the Rational Speech Act modeling framework
Recent advances in computational cognitive science (i. e., simulation-based probabilistic programs) have paved the way for significant progress in formal, implementable models of pragmatics.
Practical optimal experiment design with probabilistic programs
PPLs offer a clean separation between declaring problems and solving them, which means that the scientist can automate experiment design by simply declaring her model and experiment spaces in the PPL without having to worry about the details of calculating information gain.
The Language of Generalization
Language provides simple ways of communicating generalizable knowledge to each other (e. g., "Birds fly", "John hikes", "Fire makes smoke").
