115 citations · 219 across the 12 of their papers we have counts for
13 papers
SCREWS: A Modular Framework for Reasoning with Revisions
Kumar Shridhar, Harsh Jhamtani, Hao Fang +3
Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions c…
Few-Shot Adaptation for Parsing Contextual Utterances with LLMs
Kevin Lin, Patrick Xia, Hao Fang
We evaluate the ability of semantic parsers based on large language models (LLMs) to handle contextual utterances. In real-world settings, there typically exists only a limited num…
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
Hao Fang, Bin Chen, Xuan Wang +2
Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradient…
Natural Language Decomposition and Interpretation of Complex Utterances
Harsh Jhamtani, Hao Fang, Patrick Xia +3
Designing natural language interfaces has historically required collecting supervised data to translate user requests into carefully designed intent representations. This requires…
Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering
Hao Cheng, Hao Fang, Xiaodong Liu +1
Given its effectiveness on knowledge-intensive natural language processing tasks, dense retrieval models have become increasingly popular. Specifically, the de-facto architecture f…
When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems
Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls +5
In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning re…
Sounding Board: A User-Centric and Content-Driven Social Chatbot
Hao Fang, Hao Cheng, Maarten Sap +5
We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize. The system architecture consists of several components including spoken language processing, dialo…
Learning Latent Local Conversation Modes for Predicting Community Endorsement in Online Discussions
Hao Fang, Hao Cheng, Mari Ostendorf
Many social media platforms offer a mechanism for readers to react to comments, both positively and negatively, which in aggregate can be thought of as community endorsement. This…
Talking to the crowd: What do people react to in online discussions?
Aaron Jaech, Victoria Zayats, Hao Fang +2
This paper addresses the question of how language use affects community reaction to comments in online discussion forums, and the relative importance of the message vs. the messeng…
Language Models for Image Captioning: The Quirks and What Works
Jacob Devlin, Hao Cheng, Hao Fang +5
Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional…
Performance Limits of Segmented Compressive Sampling: Correlated Samples versus Bits
Hao Fang, Sergiy A. Vorobyov, Hai Jiang
This paper gives performance limits of the segmented compressive sampling (CS) which collects correlated samples. It is shown that the effect of correlation among samples for the s…
Permutation Enhanced Parallel Reconstruction with A Linear Compressive Sampling Device
Hao Fang, Sergiy A. Vorobyov, Hai Jiang
In this letter, a permutation enhanced parallel reconstruction architecture for compressive sampling is proposed. In this architecture, a measurement matrix is constructed from a b…
Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals
Hao Fang, Sergiy A. Vorobyov, Hai Jiang +1
Traditional compressed sensing considers sampling a 1D signal. For a multidimensional signal, if reshaped into a vector, the required size of the sensing matrix becomes dramaticall…