Search Results for author: Linqing Liu

Found 15 papers, 7 papers with code

Query Expansion Using Contextual Clue Sampling with Language Models

no code implementations13 Oct 2022 Linqing Liu, Minghan Li, Jimmy Lin, Sebastian Riedel, Pontus Stenetorp

To balance these two considerations, we propose a combination of an effective filtering strategy and fusion of the retrieved documents based on the generation probability of each context.

Information Retrieval Language Modelling +1

What the DAAM: Interpreting Stable Diffusion Using Cross Attention

1 code implementation10 Oct 2022 Raphael Tang, Linqing Liu, Akshat Pandey, Zhiying Jiang, Gefei Yang, Karun Kumar, Pontus Stenetorp, Jimmy Lin, Ferhan Ture

Large-scale diffusion neural networks represent a substantial milestone in text-to-image generation, but they remain poorly understood, lacking interpretability analyses.

Denoising Descriptive +3

When Do Flat Minima Optimizers Work?

1 code implementation1 Feb 2022 Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.

Benchmarking Graph Learning +9

Challenges in Generalization in Open Domain Question Answering

1 code implementation Findings (NAACL) 2022 Linqing Liu, Patrick Lewis, Sebastian Riedel, Pontus Stenetorp

Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.

Natural Questions Open-Domain Question Answering +3

Generative Adversarial Network for Abstractive Text Summarization

1 code implementation26 Nov 2017 Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li

In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization.

Abstractive Text Summarization Generative Adversarial Network +2

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