Search Results for author:
Found 9 papers, 4 papers with code
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
This has led some researchers to conclude that LMs "lie" or otherwise encode non-cooperative communicative intents.
Nova$^+$: Generative Language Models for Binaries
We build Nova$^+$ to further boost Nova using two new pre-training tasks, i. e., optimization generation and optimization level prediction, which are designed to learn binary optimization and align equivalent binaries.
Model-agnostic Measure of Generalization Difficulty
The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models.
Open Set Recognition For Music Genre Classification
We conduct four experiments, each containing a different set of known and unknown classes, using the GTZAN and the FMA datasets to establish a baseline capacity for novel genre detection.
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way.
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Level
We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy.
Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings
This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction.
The Perils of Exploration under Competition: A Computational Modeling Approach
We empirically study the interplay between exploration and competition.
Conditional Variational Autoencoder for Neural Machine Translation
We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT).
