Search Results for author:
Found 11 papers, 4 papers with code
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
On single-turn dialogue and summarization, a SAMI-trained mistral-7b outperforms the initial pretrained model, with win rates between 66% and 77%.
Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models
We collected moral permissibility and intention judgments from human participants for a subset of our items and compared these judgments to those from two language models (GPT-4 and Claude-2) across eight conditions.
Stream of Search (SoS): Learning to Search in Language
In this paper, we show how language models can be taught to search by representing the process of search in language, as a flattened string -- a stream of search (SoS).
Social Contract AI: Aligning AI Assistants with Implicit Group Norms
We explore the idea of aligning an AI assistant by inverting a model of users' (unknown) preferences from observed interactions.
Exciton-Polariton Condensates: A Fourier Neural Operator Approach
Advancements in semiconductor fabrication over the past decade have catalyzed extensive research into all-optical devices driven by exciton-polariton condensates.
Understanding Social Reasoning in Language Models with Language Models
Using our framework, we create a new social reasoning benchmark (BigToM) for LLMs which consists of 25 controls and 5, 000 model-written evaluations.
Certified Deductive Reasoning with Language Models
In experiments on PrOntoQA, ProofWriter and Syllogism Validity datasets, \textsc{LogicGuide} significantly improves the performance of GPT-3, GPT-3. 5 Turbo and LLaMA (accuracy gains up to 35\%), while drastically reducing \emph{content effects} -- the interference between unwanted prior assumptions and reasoning, which humans and language models suffer from.
Strategic Reasoning with Language Models
Existing approaches to solving strategic games rely on extensive training, yielding strategies that do not generalize to new scenarios or games without retraining.
Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation.
Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others
To achieve human-like common sense about everyday life, machine learning systems must understand and reason about the goals, preferences, and actions of other agents in the environment.
Mutual exclusivity as a challenge for deep neural networks
Strong inductive biases allow children to learn in fast and adaptable ways.
