Nebula: A discourse aware Minecraft Builder
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Abstract (arXiv)
When engaging in collaborative tasks, humans efficiently exploit the semantic structure of a conversation to optimize verbal and nonverbal interactions. But in recent "language to code" or "language to action" models, this information is lacking. We show how incorporating the prior discourse and nonlinguistic context of a conversation situated in a nonlinguistic environment can improve the "language to action" component of such interactions. We finetune an LLM to predict actions based on prior context; our model, Nebula, doubles the net-action F1 score over the baseline on this task of Jayannavar et al.(2020). We also investigate our model's ability to construct shapes and understand location descriptions using a synthetic dataset
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 26 Jun 2024.
Builds on
9 of 21 references.
References · 9
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
- 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172
- 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
- 2023Language to Rewards for Robotic Skill Synthesiscited by 307
- 2022Learning to Execute Actions or Ask Clarification Questionscited by 30
- 2022Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021cited by 23
- 2023Transforming Human-Centered AI Collaboration: Redefining Embodied Agents Capabilities through Interactive Grounded Language Instructionscited by 13
- 2024Large Language Models as Minecraft Agentscited by 7
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.