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Improving Agent Interactions in Virtual Environments with Language Models

Jack Zhang

TL;DR

This paper investigates how language models can enhance agent performance in a collaborative Minecraft building task by focusing on instruction understanding. It adopts a masked language modeling approach on build descriptions to improve language grounding and task comprehension, followed by fine-tuning and transfer to stronger models. Empirical results on the Minecraft Corpus show the proposed method outperforming baselines like BAP and LearnToAsk, with decreasing training and validation losses indicating better generalization. The work demonstrates the potential of task-specific language-model adaptation to improve human–AI collaboration in multi-modal, instruction-driven environments, suggesting a promising direction for future research in grounded dialogue and collaborative robotics in virtual ecosystems.

Abstract

Enhancing AI systems with efficient communication skills for effective human assistance necessitates proactive initiatives from the system side to discern specific circumstances and interact aptly. This research focuses on a collective building assignment in the Minecraft dataset, employing language modeling to enhance task understanding through state-of-the-art methods. These models focus on grounding multi-modal understanding and task-oriented dialogue comprehension tasks, providing insights into their interpretative and responsive capabilities. Our experimental results showcase a substantial improvement over existing methods, indicating a promising direction for future research in this domain.

Improving Agent Interactions in Virtual Environments with Language Models

TL;DR

This paper investigates how language models can enhance agent performance in a collaborative Minecraft building task by focusing on instruction understanding. It adopts a masked language modeling approach on build descriptions to improve language grounding and task comprehension, followed by fine-tuning and transfer to stronger models. Empirical results on the Minecraft Corpus show the proposed method outperforming baselines like BAP and LearnToAsk, with decreasing training and validation losses indicating better generalization. The work demonstrates the potential of task-specific language-model adaptation to improve human–AI collaboration in multi-modal, instruction-driven environments, suggesting a promising direction for future research in grounded dialogue and collaborative robotics in virtual ecosystems.

Abstract

Enhancing AI systems with efficient communication skills for effective human assistance necessitates proactive initiatives from the system side to discern specific circumstances and interact aptly. This research focuses on a collective building assignment in the Minecraft dataset, employing language modeling to enhance task understanding through state-of-the-art methods. These models focus on grounding multi-modal understanding and task-oriented dialogue comprehension tasks, providing insights into their interpretative and responsive capabilities. Our experimental results showcase a substantial improvement over existing methods, indicating a promising direction for future research in this domain.
Paper Structure (16 sections, 5 figures, 1 table)

This paper contains 16 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. Language Modeling
  4. Language Grounding Tasks
  5. Pre-trained Language Models.
  6. Self Training.
  7. Method
  8. Experiment, Results and Discussion
  9. Dataset
  10. Baseline Models
  11. Evaluation Metrics
  12. Training Details
  13. Results
  14. Analysis of Loss Metrics
  15. Learning Rate Evolution
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Within the ambit of a collaborative construction endeavor, it is incumbent upon the builder to adhere scrupulously to the directives issued by the architect. This endeavor mandates a thorough assimilation of the architect's specifications, as the culmination of the task hinges significantly on unambiguous communication and meticulous implementation. This framework accentuates the pivotal function of the builder in transmuting the architect's conceptualization into a concrete manifestation.
  • Figure 2: The example of masked language modeling devlin2018bertliu2019roberta.
  • Figure 3: The flowchart of our method.
  • Figure 4: Experimental Result: Training and Validation Loss for masked language modeling.
  • Figure 5: The change of learning rate during the training phrase.