399 citations · 4k across the 100 of their papers we have counts for
40 papers · 1 filter
Generalized Decoding for Pixel, Image, and Language
Xueyan Zou, Zi-Yi Dou, Jianwei Yang +11
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) gen…
Efficient Long Sequence Modeling via State Space Augmented Transformer
Simiao Zuo, Xiaodong Liu, Jian Jiao +4
Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its…
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
Faeze Brahman, Baolin Peng, Michel Galley +4
Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-t…
Language Models as Inductive Reasoners
Zonglin Yang, Li Dong, Xinya Du +5
Inductive reasoning is a core component of human intelligence. In the past research of inductive reasoning within computer science, formal language is used as representations of kn…
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization
Yu Li, Baolin Peng, Pengcheng He +3
Dialogue summarization has recently garnered significant attention due to its wide range of applications. However, existing methods for summarizing dialogues have limitations becau…
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
Miaoran Li, Baolin Peng, Michel Galley +2
Many efforts have been made to construct dialog systems for different types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD). To better mimic human…
Execution-based Evaluation for Data Science Code Generation Models
Junjie Huang, Chenglong Wang, Jipeng Zhang +6
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions. An important measure of the modeling progress…
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee +4
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of…
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Qi Zhu, Christian Geishauser, Hsien-chin Lin +10
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for build…
Lafite2: Few-shot Text-to-Image Generation
Yufan Zhou, Chunyuan Li, Changyou Chen +2
Text-to-image generation models have progressed considerably in recent years, which can now generate impressive realistic images from arbitrary text. Most of such models are traine…
Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge
Kaixin Ma, Hao Cheng, Xiaodong Liu +2
We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. The key novelty of our method is…
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
Zhe Gan, Linjie Li, Chunyuan Li +3
This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categ…
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation
Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu +6
Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing works in MoE mostly consider a homogeneous design wher…
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…
Explaining Patterns in Data with Language Models via Interpretable Autoprompting
Chandan Singh, John X. Morris, Jyoti Aneja +2
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learne…
Augmenting Interpretable Models with LLMs during Training
Chandan Singh, Armin Askari, Rich Caruana +1
Recent large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains (e.g. me…
Structural Biases for Improving Transformers on Translation into Morphologically Rich Languages
Paul Soulos, Sudha Rao, Caitlin Smith +9
Machine translation has seen rapid progress with the advent of Transformer-based models. These models have no explicit linguistic structure built into them, yet they may still impl…
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning
Sheng Zhang, Hao Cheng, Jianfeng Gao +1
We present a bi-encoder framework for named entity recognition (NER), which applies contrastive learning to map candidate text spans and entity types into the same vector represent…
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Pengcheng He, Baolin Peng, Liyang Lu +11
This paper presents Z-Code++, a new pre-trained language model optimized for abstractive text summarization. The model extends the state of the art encoder-decoder model using thre…
Interactive Code Generation via Test-Driven User-Intent Formalization
Shuvendu K. Lahiri, Sarah Fakhoury, Aaditya Naik +8
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, w…
OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience
Miaoran Li, Baolin Peng, Jianfeng Gao +1
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. Towards the goal of constructing a conversational agent…
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog
Baolin Peng, Michel Galley, Pengcheng He +6
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new p…
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
Zi-Yi Dou, Aishwarya Kamath, Zhe Gan +9
Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as…
GLIPv2: Unifying Localization and Vision-Language Understanding
Haotian Zhang, Pengchuan Zhang, Xiaowei Hu +7
We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tas…