Search Results for author:
Found 17 papers, 5 papers with code
What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations
We propose a logistic Bradley-Terry probe which predicts word pair preferences of LLMs from the words' hidden vectors.
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking.
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale
In this paper, we explore training and deploying an ASR system in the label-scarce, compute-limited setting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
What the DAAM: Interpreting Stable Diffusion Using Cross Attention
Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses.
Streaming Voice Query Recognition using Causal Convolutional Recurrent Neural Networks
Voice-enabled commercial products are ubiquitous, typically enabled by lightweight on-device keyword spotting (KWS) and full automatic speech recognition (ASR) in the cloud.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SyntaViz: Visualizing Voice Queries through a Syntax-Driven Hierarchical Ontology
This paper describes SyntaViz, a visualization interface specifically designed for analyzing natural-language queries that were created by users of a voice-enabled product.
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.
No Need to Pay Attention: Simple Recurrent Neural Networks Work!
Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB.
Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams
To our knowledge, we are the first to integrate lexical and temporal signals in an end-to-end neural network architecture, in which existing neural ranking models are used to generate query-document similarity vectors that feed into a bidirectional LSTM layer for temporal modeling.
Learning to Translate for Multilingual Question Answering
In multilingual question answering, either the question needs to be translated into the document language, or vice versa.
No Need to Pay Attention: Simple Recurrent Neural Networks Work! (for Answering "Simple" Questions)
Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB.
