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Found 29 papers, 17 papers with code
IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents
Seamless interaction between AI agents and humans using natural language remains a key goal in AI research.
Assessing and Verifying Task Utility in LLM-Powered Applications
The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks.
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications
The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks.
Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructions
Human intelligence's adaptability is remarkable, allowing us to adjust to new tasks and multi-modal environments swiftly.
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback
We explore multiple types of help players can give to the AI to guide it and analyze the impact of this help in AI behavior, resulting in performance improvements.
Collecting Interactive Multi-modal Datasets for Grounded Language Understanding
Human intelligence can remarkably adapt quickly to new tasks and environments.
Learning to Solve Voxel Building Embodied Tasks from Pixels and Natural Language Instructions
The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy.
IGLU Gridworld: Simple and Fast Environment for Embodied Dialog Agents
We present the IGLU Gridworld: a reinforcement learning environment for building and evaluating language conditioned embodied agents in a scalable way.
IGLU 2022: Interactive Grounded Language Understanding in a Collaborative Environment at NeurIPS 2022
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021
The primary goal of the competition is to approach the problem of how to build interactive agents that learn to solve a task while provided with grounded natural language instructions in a collaborative environment.
Making Large Language Models Interactive: A Pioneer Study on Supporting Complex Information-Seeking Tasks with Implicit Constraints
Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences e. g.,"find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.
Summarization with Graphical Elements
Motivated from these two angles, we propose a new task: summarization with graphical elements, and we verify that these summaries are helpful for a critical mass of people.
NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment
Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response.
Learning to Decompose and Organize Complex Tasks
Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.
Improving Response Quality with Backward Reasoning in Open-domain Dialogue Systems
The proposed backward reasoning step pushes the model to produce more informative and coherent content because the forward generation step's output is used to infer the dialogue context in the backward direction.
DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn.
What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research
Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.
Guided Dialogue Policy Learning without Adversarial Learning in the Loop
Reinforcement learning methods have emerged as a popular choice for training an efficient and effective dialogue policy.
ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)
The main aim of the conversational systems is to return an appropriate answer in response to the user requests.
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems
Then, the traditional multi-label classification solution for dialogue policy learning is extended by adding dense layers to improve the dialogue agent performance.
Optimizing Interactive Systems via Data-Driven Objectives
Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.
Guided Dialog Policy Learning without Adversarial Learning in the Loop
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.
How to Not Measure Disentanglement
We conclude that existing metrics of disentanglement were created to reflect different characteristics of disentanglement and do not satisfy two basic desirable properties: (1) assign a high score to representations that are disentangled according to the definition; and (2) assign a low score to representations that are entangled according to the definition.
SEntNet: Source-aware Recurrent Entity Network for Dialogue Response Selection
Obtaining key information from a complex, long dialogue context is challenging, especially when different sources of information are available, e. g., the user's utterances, the system's responses, and results retrieved from a knowledge base (KB).
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator.
Learning Data-Driven Objectives to Optimize Interactive Systems
Effective optimization is essential for interactive systems to provide a satisfactory user experience.
Modeling Label Ambiguity for Neural List-Wise Learning to Rank
List-wise learning to rank methods are considered to be the state-of-the-art.
