Papers by Elvis Hsieh
Do What? Teaching Vision-Language-Action Models to Reject the Impossible (2025.findings-emnlp)
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| Challenge: | Recent studies show that VLAs can recognize, interpret, and respond to false-premise instructions. |
| Approach: | They propose a framework that detects when an instruction cannot be executed due to a false premise and engages in language-based clarification or correction. |
| Outcome: | The proposed framework detects when an instruction cannot be executed due to a false premise and engages in language-based clarification or correction. |
LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) struggle with hallucinations, handling domain-specific data effectively, and integrating experimental workflows. |
| Approach: | They propose a hierarchical multi-agent framework to emulate the materials science research workflow by combining a new uncertainty and confidence estimate to evaluate the self-consistency of responses from LLaMP and baseline methods. |
| Outcome: | The proposed framework performs better than existing methods in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations. |