Search Results for author:
Found 53 papers, 30 papers with code
Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code
In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code.
What Makes Good In-Context Examples for GPT-3?
In this work, we investigate whether there are more effective strategies for judiciously selecting in-context examples (relative to random sampling) that better leverage GPT-3’s in-context learning capabilities. Inspired by the recent success of leveraging a retrieval module to augment neural networks, we propose to retrieve examples that are semantically-similar to a test query sample to formulate its corresponding prompt.
Echoes in AI: Quantifying Lack of Plot Diversity in LLM Outputs
We examine two state-of-the-art LLMs, GPT-4 and LLaMA-3, on story generation and discover that LLM-generated stories often consist of plot elements that are echoed across a number of generations.
Game Plot Design with an LLM-powered Assistant: An Empirical Study with Game Designers
We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process.
MCPDial: A Minecraft Persona-driven Dialogue Dataset
We propose a novel approach that uses large language models (LLMs) to generate persona-driven conversations between Players and Non-Player Characters (NPC) in games.
Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft
The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driven non-player characters (NPCs) to become widely deployed.
Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs
Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs).
Player-Driven Emergence in LLM-Driven Game Narrative
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.
GENEVA: GENErating and Visualizing branching narratives using LLMs
Dialogue-based Role Playing Games (RPGs) require powerful storytelling.
Investigating Agency of LLMs in Human-AI Collaboration Tasks
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.
Interactive Text Generation
Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help.
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation
We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages.
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog.
Towards More Efficient Insertion Transformer with Fractional Positional Encoding
Nevertheless, due to the incompatibility between absolute positional encoding and insertion-based generation schemes, it needs to refresh the encoding of every token in the generated partial hypothesis at each step, which could be costly.
Automatic Document Sketching: Generating Drafts from Analogous Texts
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.
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.
An Adversarially-Learned Turing Test for Dialog Generation Models
To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.
What Makes Good In-Context Examples for GPT-$3$?
Inspired by the recent success of leveraging a retrieval module to augment large-scale neural network models, we propose to retrieve examples that are semantically-similar to a test sample to formulate its corresponding prompt.
Narrative Incoherence Detection
We propose the task of narrative incoherence detection as a new arena for inter-sentential semantic understanding: Given a multi-sentence narrative, decide whether there exist any semantic discrepancies in the narrative flow.
Text Editing by Command
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step.
Substance over Style: Document-Level Targeted Content Transfer
Existing language models excel at writing from scratch, but many real-world scenarios require rewriting an existing document to fit a set of constraints.
Contextualized Perturbation for Textual Adversarial Attack
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DIALOGPT (dialogue generative pre-trained transformer).
Reparameterized Variational Divergence Minimization for Stable Imitation
While recent state-of-the-art results for adversarial imitation-learning algorithms are encouraging, recent works exploring the imitation learning from observation (ILO) setting, where trajectories \textit{only} contain expert observations, have not been met with the same success.
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
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.
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform
We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
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.
A Controllable Model of Grounded Response Generation
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.
Contrastive Multi-document Question Generation
In the SL stage, a single-document question generator is trained.
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer).
Structuring Latent Spaces for Stylized Response Generation
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.
Domain Adaptive Text Style Transfer
These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.
Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository.
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
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.
Consistent Dialogue Generation with Self-supervised Feature Learning
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.
Jointly Optimizing Diversity and Relevance in Neural Response Generation
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.
Ranked #1 on
Dialogue Generation
on Reddit (multi-ref)
Dialog System Technology Challenge 7
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.
Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention
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.
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Responses generated by neural conversational models tend to lack informativeness and diversity.
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Neural conversation models tend to generate safe, generic responses for most inputs.
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training.
A Knowledge-Grounded Neural Conversation Model
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.
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations.
A Persona-Based Neural Conversation Model
We present persona-based models for handling the issue of speaker consistency in neural response generation.
A Diversity-Promoting Objective Function for Neural Conversation Models
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.
deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs.
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
We present a novel response generation system that can be trained end to end on large quantities of unstructured Twitter conversations.
