Papers by Rodolfo Corona
Modular Networks for Compositional Instruction Following (2021.naacl-main)
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| Challenge: | Standard instruction following models struggle on novel compositions of subgoals observed during training. |
| Approach: | They propose a modular architecture that follows natural language instructions that describe sequences of diverse subgoals. |
| Outcome: | The proposed architecture improves generalization to novel subgoals and environments unseen in training. |
Which One? Leveraging Context Between Objects and Multiple Views for Language Grounding (2024.naacl-long)
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| Challenge: | Existing methods for identifying object referents of language expressions consider target and distractor objects independently and pool multiple views before grounding. |
| Approach: | They propose a model that selects an object referent based on language that distinguishes between two similar objects and a multi-view approach to grounding in context model which reduces the relative error by 12.9% . |
| Outcome: | The proposed model improves on the SNARE object reference task with a relative error reduction of 12.9% and an absolute improvement of 2.7%. |
Re-evaluating the Need for Visual Signals in Unsupervised Grammar Induction (2024.findings-naacl)
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Boyi Li, Rodolfo Corona, Karttikeya Mangalam, Catherine Chen, Daniel Flaherty, Serge Belongie, Kilian Weinberger, Jitendra Malik, Trevor Darrell, Dan Klein
| Challenge: | Recent studies show multimodal inputs can improve grammar induction, but weak textual baselines are needed for training. |
| Approach: | They use a fixed grammar family to compare multimodal grammar induction methods . they find multimodal inputs can improve grammar induction by grounding textual inputs to the visual world . |
| Outcome: | The proposed model outperforms weaker baselines on four benchmark datasets. |
Enough Coin Flips Can Make LLMs Act Bayesian (2025.acl-long)
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| Challenge: | Large language models exhibit the ability to generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning. |
| Approach: | They investigate whether large language models use in-context learning to generalize given few-shot examples in their input prompt. |
| Outcome: | The proposed model can generalize given few-shot examples in their input prompt, an emergent capability known as in-context learning. |
Voxel-informed Language Grounding (2022.acl-short)
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| Challenge: | Embodied robotic agents can be used to ground objects using 3D geometry . despite typically being paired with 2D images, natural language describes a fundamentally 3D world . |
| Approach: | They propose a model that leverages 3D geometric information to ground natural language . they show that VLG significantly improves grounding accuracy on SNARE . |
| Outcome: | The proposed model significantly improves grounding accuracy on SNARE, an object reference game task. |