Search Results for author: Guoqing Zheng

Found 23 papers, 10 papers with code

Axiomatic Preference Modeling for Longform Question Answering

no code implementations2 Dec 2023 Corby Rosset, Guoqing Zheng, Victor Dibia, Ahmed Awadallah, Paul Bennett

The remarkable abilities of large language models (LLMs) like GPT-4 partially stem from post-training processes like Reinforcement Learning from Human Feedback (RLHF) involving human preferences encoded in a reward model.

Question Answering

Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation

no code implementations4 Oct 2023 Chen Dun, Mirian Hipolito Garcia, Guoqing Zheng, Ahmed Hassan Awadallah, Anastasios Kyrillidis, Robert Sim

Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind.

Model Compression Text Summarization

Boosting Natural Language Generation from Instructions with Meta-Learning

no code implementations20 Oct 2022 Budhaditya Deb, Guoqing Zheng, Ahmed Hassan Awadallah

Recent work has shown that language models (LMs) trained with multi-task \textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved performance compared to prompt tuning.

Meta-Learning Text Generation

Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity

no code implementations26 Aug 2022 Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, Ryen White

The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice.

Data Augmentation Node Classification

ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels

1 code implementation24 Aug 2022 Yue Zhao, Guoqing Zheng, Subhabrata Mukherjee, Robert McCann, Ahmed Awadallah

In this work, we propose a method to leverage weak/noisy labels (e. g., risk scores generated by machine rules for detecting malware) that are cheaper to obtain for anomaly detection.

Anomaly Detection

Pathologies of Pre-trained Language Models in Few-shot Fine-tuning

no code implementations insights (ACL) 2022 Hanjie Chen, Guoqing Zheng, Ahmed Hassan Awadallah, Yangfeng Ji

Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance gain comes from.

text-classification Text Classification

Knowledge Infused Decoding

1 code implementation ICLR 2022 Ruibo Liu, Guoqing Zheng, Shashank Gupta, Radhika Gaonkar, Chongyang Gao, Soroush Vosoughi, Milad Shokouhi, Ahmed Hassan Awadallah

Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks.

counterfactual Question Answering +1

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

1 code implementation4 Nov 2021 Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng, Saghar Hosseini, Hao Cheng, Greg Yang, Christopher Meek, Ahmed Hassan Awadallah, Jianfeng Gao

We demonstrate that while recent models reach human performance when they have access to large amounts of labeled data, there is a huge gap in performance in the few-shot setting for most tasks.

Few-Shot Learning Natural Language Understanding

MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces

no code implementations9 Sep 2021 Srinagesh Sharma, Guoqing Zheng, Ahmed Hassan Awadallah

In this paper, we aim to the address of the problem of few shot task learning by exploiting and transferring from a different task which admits a related but disparate label space.

Language Modelling

WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding

no code implementations NAACL 2022 Guoqing Zheng, Giannis Karamanolakis, Kai Shu, Ahmed Hassan Awadallah

In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU.

Natural Language Understanding Weakly-supervised Learning

MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning

2 code implementations NAACL 2021 Mengzhou Xia, Guoqing Zheng, Subhabrata Mukherjee, Milad Shokouhi, Graham Neubig, Ahmed Hassan Awadallah

Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach.

Cross-Lingual Transfer Meta-Learning +5

Self-Training with Weak Supervision

1 code implementation NAACL 2021 Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah

In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task.

text-classification Text Classification

Learning with Weak Supervision for Email Intent Detection

no code implementations26 May 2020 Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah, Milad Shokouhi, Susan Dumais

In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.

intent-classification Intent Classification +2

Meta Label Correction for Noisy Label Learning

1 code implementation10 Nov 2019 Guoqing Zheng, Ahmed Hassan Awadallah, Susan Dumais

We view the label correction procedure as a meta-process and propose a new meta-learning based framework termed MLC (Meta Label Correction) for learning with noisy labels.

Ranked #9 on Image Classification on Clothing1M (using clean data) (using extra training data)

Learning with noisy labels Meta-Learning +2

Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data

1 code implementation15 Jun 2018 Guokun Lai, Bohan Li, Guoqing Zheng, Yiming Yang

In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure.

Asymmetric Variational Autoencoders

1 code implementation20 Nov 2017 Guoqing Zheng, Yiming Yang, Jaime Carbonell

However, freely enriching the family of variational distribution is challenging since the ELBO requires variational likelihood evaluations of the latent variables.

Density Estimation Variational Inference

Convolutional Normalizing Flows

1 code implementation ICLR 2018 Guoqing Zheng, Yiming Yang, Jaime Carbonell

Variational inference provides one way to approximate the posterior distribution, however its expressive power is limited and so is the accuracy of resulting approximation.

Variational Inference

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