406 citations · 2.4k across the 39 of their papers we have counts for
42 papers
Self-Verification Improves Few-Shot Clinical Information Extraction
Zelalem Gero, Chandan Singh, Hao Cheng +4
Extracting patient information from unstructured text is a critical task in health decision-support and clinical research. Large language models (LLMs) have shown the potential to…
Instruction Tuning with GPT-4
Baolin Peng, Chunyuan Li, Pengcheng He +2
Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilitie…
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models
Pan Lu, Baolin Peng, Hao Cheng +5
Large language models (LLMs) have achieved remarkable progress in solving various natural language processing tasks due to emergent reasoning abilities. However, LLMs have inherent…
Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
Baolin Peng, Michel Galley, Pengcheng He +8
Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. Howev…
Interactive Text Generation
Felix Faltings, Michel Galley, Baolin Peng +5
Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between…
Guiding Large Language Models via Directional Stimulus Prompting
Zekun Li, Baolin Peng, Pengcheng He +3
We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLM…
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…
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…
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…
Probing Factually Grounded Content Transfer with Factual Ablation
Peter West, Chris Quirk, Michel Galley +1
Despite recent success, large neural models often generate factually incorrect text. Compounding this is the lack of a standard automatic evaluation for factuality--it cannot be me…
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Julia Kiseleva, Ziming Li, Mohammad Aliannejadi +12
Human intelligence has the remarkable ability to adapt to new tasks and environments quickly. Starting from a very young age, humans acquire new skills and learn how to solve new t…
Automatic Document Sketching: Generating Drafts from Analogous Texts
Zeqiu Wu, Michel Galley, Chris Brockett +2
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document. However, the high branching…
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
Yizhe Zhang, Siqi Sun, Xiang Gao +5
Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from…
An Adversarially-Learned Turing Test for Dialog Generation Models
Xiang Gao, Yizhe Zhang, Michel Galley +1
The design of better automated dialogue evaluation metrics offers the potential of accelerate evaluation research on conversational AI. However, existing trainable dialogue evaluat…
Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge
Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley +1
The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous…
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization
Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley +4
The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets. We present two QMDS training…
Text Editing by Command
Felix Faltings, Michel Galley, Gerold Hintz +4
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints t…
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Xiang Gao, Yizhe Zhang, Michel Galley +2
Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning m…
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform
Xiang Gao, Michel Galley, Bill Dolan
We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared…
A Controllable Model of Grounded Response Generation
Zeqiu Wu, Michel Galley, Chris Brockett +8
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting respon…
The Eighth Dialog System Technology Challenge
Seokhwan Kim, Michel Galley, Chulaka Gunasekara +18
This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a prag…
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang, Siqi Sun, Michel Galley +6
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…
Structuring Latent Spaces for Stylized Response Generation
Xiang Gao, Yizhe Zhang, Sungjin Lee +4
Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate response…