Search Results for author: Chris Brockett

Found 39 papers, 21 papers with code

Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs

no code implementations6 Jun 2024 Claire Jin, Sudha Rao, Xiangyu Peng, Portia Botchway, Jessica Quaye, Chris Brockett, Bill Dolan

Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs).

Game Design text-based games

Player-Driven Emergence in LLM-Driven Game Narrative

no code implementations25 Apr 2024 Xiangyu Peng, Jessica Quaye, Sudha Rao, Weijia Xu, Portia Botchway, Chris Brockett, Nebojsa Jojic, Gabriel DesGarennes, Ken Lobb, Michael Xu, Jorge Leandro, Claire Jin, Bill Dolan

We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives.

Language Modelling Large Language Model

Investigating Agency of LLMs in Human-AI Collaboration Tasks

no code implementations22 May 2023 ASHISH SHARMA, Sudha Rao, Chris Brockett, Akanksha Malhotra, Nebojsa Jojic, Bill Dolan

While LLMs are being developed to simulate human behavior and serve as human-like agents, little attention has been given to the Agency that these models should possess in order to proactively manage the direction of interaction and collaboration.

Automatic Document Sketching: Generating Drafts from Analogous Texts

no code implementations Findings (ACL) 2021 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Bill Dolan

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.

Reinforcement Learning (RL) Sentence +1

RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling

1 code implementation14 May 2021 Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.

Dialogue Generation Language Modelling +1

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

2 code implementations ACL 2022 Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, Bill Dolan

Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.

Hallucination Sentence +1

Text Editing by Command

no code implementations NAACL 2021 Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett, Chris Quirk, Jianfeng Gao, Bill Dolan

A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step.

Sentence Text Generation

Contextualized Perturbation for Textual Adversarial Attack

1 code implementation NAACL 2021 Dianqi Li, Yizhe Zhang, Hao Peng, Liqun Chen, Chris Brockett, Ming-Ting Sun, Bill Dolan

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.

Adversarial Attack Language Modelling

A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks

1 code implementation ACL 2020 Angela S. Lin, Sudha Rao, Asli Celikyilmaz, Elnaz Nouri, Chris Brockett, Debadeepta Dey, Bill Dolan

Learning to align these different instruction sets is challenging because: a) different recipes vary in their order of instructions and use of ingredients; and b) video instructions can be noisy and tend to contain far more information than text instructions.

Descriptive

POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training

1 code implementation EMNLP 2020 Yizhe Zhang, Guoyin Wang, Chunyuan Li, Zhe Gan, Chris Brockett, Bill Dolan

Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation.

Language Modelling Representation Learning +1

A Controllable Model of Grounded Response Generation

1 code implementation1 May 2020 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, Bill Dolan

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.

Informativeness Response Generation

Structuring Latent Spaces for Stylized Response Generation

1 code implementation IJCNLP 2019 Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan

This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.

Response Generation Style Transfer

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

1 code implementation ACL 2019 Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao

Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous.

Informativeness Reading Comprehension +1

Consistent Dialogue Generation with Self-supervised Feature Learning

1 code implementation13 Mar 2019 Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.

Dialogue Generation Response Generation

Jointly Optimizing Diversity and Relevance in Neural Response Generation

no code implementations NAACL 2019 Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.

Dialogue Generation Response Generation

Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention

1 code implementation CVPR 2019 Khanh Nguyen, Debadeepta Dey, Chris Brockett, Bill Dolan

We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments.

Imitation Learning Navigate +2

Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

no code implementations IJCNLP 2017 Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, Michel Galley

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training.

Multi-Task Learning

Steering Output Style and Topic in Neural Response Generation

1 code implementation EMNLP 2017 Di Wang, Nebojsa Jojic, Chris Brockett, Eric Nyberg

We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation.

Decoder Response Generation +1

A Knowledge-Grounded Neural Conversation Model

2 code implementations7 Feb 2017 Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, Michel Galley

We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.

Slot Filling

A Persona-Based Neural Conversation Model

1 code implementation ACL 2016 Jiwei Li, Michel Galley, Chris Brockett, Georgios P. Spithourakis, Jianfeng Gao, Bill Dolan

We present persona-based models for handling the issue of speaker consistency in neural response generation.

Response Generation

A Diversity-Promoting Objective Function for Neural Conversation Models

15 code implementations NAACL 2016 Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e. g., "I don't know") regardless of the input.

Conversational Response Generation Response Generation

deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets

no code implementations IJCNLP 2015 Michel Galley, Chris Brockett, Alessandro Sordoni, Yangfeng Ji, Michael Auli, Chris Quirk, Margaret Mitchell, Jianfeng Gao, Bill Dolan

We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs.

Sentence

Cannot find the paper you are looking for? You can Submit a new open access paper.