Search Results for author:
Found 19 papers, 5 papers with code
Improving Text-to-Image Consistency via Automatic Prompt Optimization
In this paper, we address these challenges and introduce a T2I optimization-by-prompting framework, OPT2I, which leverages a large language model (LLM) to improve prompt-image consistency in T2I models.
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Curation methods for massive vision-language datasets trade off between dataset size and quality.
Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models
We compare models trained on our new dataset to existing pairwise-trained dialogue models, as well as large language models with few-shot prompting.
Infusing Commonsense World Models with Graph Knowledge
While language models have become more capable of producing compelling language, we find there are still gaps in maintaining consistency, especially when describing events in a dynamically changing world.
Mephisto: A Framework for Portable, Reproducible, and Iterative Crowdsourcing
In this whitepaper we discuss the current state of data collection and annotation in ML research, establish the motivation for building a shared framework to enable researchers to create and open-source data collection and annotation tools as part of their publication, and outline a set of suggested requirements for a system to facilitate these goals.
Breaking The Limits of Text-conditioned 3D Motion Synthesis with Elaborative Descriptions
Given its wide applications, there is increasing focus on generating 3D human motions from textual descriptions.
Am I Me or You? State-of-the-Art Dialogue Models Cannot Maintain an Identity
State-of-the-art dialogue models still often stumble with regards to factual accuracy and self-contradiction.
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Large language models can produce fluent dialogue but often hallucinate factual inaccuracies.
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
We seek to create agents that both act and communicate with other agents in pursuit of a goal.
Deploying Lifelong Open-Domain Dialogue Learning
As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013).
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.
I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.
Generating Interactive Worlds with Text
We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Models often easily learn biases present in the training data, and their predictions directly reflect this bias.
Learning to Speak and Act in a Fantasy Text Adventure Game
We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.
The Second Conversational Intelligence Challenge (ConvAI2)
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating.
Ranked #5 on
Dialogue Generation
on Persona-Chat
(using extra training data)
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment.
