Search Results for author: Ping Yu

Found 23 papers, 8 papers with code

Rethinking Sentiment Style Transfer

no code implementations Findings (EMNLP) 2021 Ping Yu, Yang Zhao, Chunyuan Li, Changyou Chen

To overcome this issue, we propose a graph-based method to extract attribute content and attribute-independent content from input sentences in the YELP dataset and IMDB dataset.

Attribute Style Transfer +1

Efficient Tool Use with Chain-of-Abstraction Reasoning

no code implementations30 Jan 2024 Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang

LLM agents trained with our method also show more efficient tool use, with inference speed being on average ~1. 4x faster than baseline tool-augmented LLMs.

Math Mathematical Reasoning +1

The ART of LLM Refinement: Ask, Refine, and Trust

no code implementations14 Nov 2023 Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ram Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz

In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations?

Arithmetic Reasoning GSM8K +2

Self-Alignment with Instruction Backtranslation

2 code implementations11 Aug 2023 Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer, Jason Weston, Mike Lewis

We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions.

Instruction Following Language Modelling

Shepherd: A Critic for Language Model Generation

1 code implementation8 Aug 2023 Tianlu Wang, Ping Yu, Xiaoqing Ellen Tan, Sean O'Brien, Ramakanth Pasunuru, Jane Dwivedi-Yu, Olga Golovneva, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz

As large language models improve, there is increasing interest in techniques that leverage these models' capabilities to refine their own outputs.

Language Modelling

OPT-R: Exploring the Role of Explanations in Finetuning and Prompting for Reasoning Skills of Large Language Models

no code implementations19 May 2023 Badr AlKhamissi, Siddharth Verma, Ping Yu, Zhijing Jin, Asli Celikyilmaz, Mona Diab

Our study entails finetuning three different sizes of OPT on a carefully curated reasoning corpus, resulting in two sets of finetuned models: OPT-R, finetuned without explanations, and OPT-RE, finetuned with explanations.

LIMA: Less Is More for Alignment

5 code implementations NeurIPS 2023 Chunting Zhou, PengFei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, Omer Levy

Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences.

Language Modelling reinforcement-learning

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

no code implementations8 May 2023 Yanna Jiang, Baihe Ma, Xu Wang, Ping Yu, Guangsheng Yu, Zhe Wang, Wei Ni, Ren Ping Liu

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world.

Federated Learning Management

IronForge: An Open, Secure, Fair, Decentralized Federated Learning

no code implementations7 Jan 2023 Guangsheng Yu, Xu Wang, Caijun Sun, Qin Wang, Ping Yu, Wei Ni, Ren Ping Liu, Xiwei Xu

Federated learning (FL) provides an effective machine learning (ML) architecture to protect data privacy in a distributed manner.

Fairness Federated Learning

OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization

1 code implementation22 Dec 2022 Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, Todor Mihaylov, Daniel Simig, Ping Yu, Kurt Shuster, Tianlu Wang, Qing Liu, Punit Singh Koura, Xian Li, Brian O'Horo, Gabriel Pereyra, Jeff Wang, Christopher Dewan, Asli Celikyilmaz, Luke Zettlemoyer, Ves Stoyanov

To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.

Language Modelling Meta-Learning +2

ALERT: Adapting Language Models to Reasoning Tasks

no code implementations16 Dec 2022 Ping Yu, Tianlu Wang, Olga Golovneva, Badr Alkhamissy, Gargi Ghosh, Mona Diab, Asli Celikyilmaz

Current large language models can perform reasonably well on complex tasks that require step-by-step reasoning with few-shot learning.

Few-Shot Learning Language Modelling +1

STT: Soft Template Tuning for Few-Shot Adaptation

no code implementations18 Jul 2022 Ping Yu, Wei Wang, Chunyuan Li, Ruiyi Zhang, Zhanpeng Jin, Changyou Chen

Significantly, it can even outperform the time- and resource-consuming fine-tuning method on sentiment classification tasks.

Few-Shot Learning Language Modelling +3

SDA: Improving Text Generation with Self Data Augmentation

no code implementations2 Jan 2021 Ping Yu, Ruiyi Zhang, Yang Zhao, Yizhe Zhang, Chunyuan Li, Changyou Chen

Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision.

Data Augmentation Imitation Learning +2

ReMP: Rectified Metric Propagation for Few-Shot Learning

no code implementations2 Dec 2020 Yang Zhao, Chunyuan Li, Ping Yu, Changyou Chen

Few-shot learning features the capability of generalizing from a few examples.

Few-Shot Learning

Structure-Aware Human-Action Generation

1 code implementation ECCV 2020 Ping Yu, Yang Zhao, Chunyuan Li, Junsong Yuan, Changyou Chen

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence.

Action Generation graph construction +1

Bayesian Meta Sampling for Fast Uncertainty Adaptation

1 code implementation ICLR 2020 Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen

Specifically, we propose a Bayesian meta sampling framework consisting of two main components: a meta sampler and a sample adapter.


Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

1 code implementation AAAI 2019 Zhenyi Wang, Ping Yu, Yang Zhao, Ruiyi Zhang, Yufan Zhou, Junsong Yuan, Changyou Chen

In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality.

Action Generation

Generating Adversarial Examples With Conditional Generative Adversarial Net

no code implementations18 Mar 2019 Ping Yu, Kaitao Song, Jianfeng Lu

Recently, deep neural networks have significant progress and successful application in various fields, but they are found vulnerable to attack instances, e. g., adversarial examples.

Generative Adversarial Network

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