Search Results for author:
Found 7 papers, 4 papers with code
Alignment is not sufficient to prevent large language models from generating harmful information: A psychoanalytic perspective
Large Language Models (LLMs) are central to a multitude of applications but struggle with significant risks, notably in generating harmful content and biases.
Emotional Intelligence of Large Language Models
Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI, suitable for both humans and LLMs.
End-to-End Face Parsing via Interlinked Convolutional Neural Networks
Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.
The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation
We show both theoretically and empirically that the global anchor method is equivalent to the alignment method, a widely-used method for comparing word embeddings, in terms of detecting corpus-level language shifts.
On the Dimensionality of Word Embedding
In this paper, we provide a theoretical understanding of word embedding and its dimensionality.
Understand Functionality and Dimensionality of Vector Embeddings: the Distributional Hypothesis, the Pairwise Inner Product Loss and Its Bias-Variance Trade-off
We demonstrate that the PIP loss captures the difference in functionality between embeddings, and that the PIP loss is tightly connect with two basic properties of vector embeddings, namely similarity and compositionality.
DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks
Three applications, namely a rewritter, a relevance scorer and a chatbot for ad recommendation, were built around DeepProbe, with the first two serving as precursory building blocks for the third.
