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Found 6 papers, 2 papers with code
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction
With IMBUE's additional just-in-time feedback, participants demonstrate 17% improvement in skill mastery, along with greater enhancements in self-efficacy (27% more) and reduction of negative emotions (16% more) compared to simulation-only.
A Roadmap to Pluralistic Alignment
We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses; 2) Steerably pluralistic models that can steer to reflect certain perspectives; and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution.
Towards Coding Social Science Datasets with Language Models
This provides exciting evidence that language models can serve as a critical advance in the coding of open-ended texts in a variety of applications.
Leveraging Large Language Models for Multiple Choice Question Answering
A more natural prompting approach is to present the question and answer options to the LLM jointly and have it output the symbol (e. g., "A") associated with its chosen answer option.
An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels
Pre-trained language models derive substantial linguistic and factual knowledge from the massive corpora on which they are trained, and prompt engineering seeks to align these models to specific tasks.
Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning
Large natural language models (such as GPT-3 or T5) demonstrate impressive abilities across a range of general NLP tasks.
