Papers by Austin Wang
Direct Judgement Preference Optimization (2025.emnlp-main)
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| Challenge: | Existing judge models are largely trained with supervised finetuning on small data scales to perform limited types of evaluation tasks, limiting generalization. |
| Approach: | They propose to train judge models at large data scales with direct preference optimization . they use four training tasks to form three types of preference pairs targeting different aspects of evaluation . |
| Outcome: | The proposed model outperforms GPT-4o and other similar models on 13 benchmarks. |
ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding (2025.acl-long)
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| Challenge: | 3D visual grounding models localize entities in a scene referred to by natural language text . recent studies focused on LLM-based scaling of 3DVG datasets, but these do not capture the full range of potential prompts which could be specified in the English language. |
| Approach: | They propose a framework for linguistically analyzing 3DVG prompts and introduce a diagnostic dataset for evaluating 3D visual grounding methods against a diverse set of language patterns. |
| Outcome: | The proposed framework scales up and tests against a representative set of prompts in the english language. |