Papers by Boya Wu

2 papers
Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL (2026.findings-acl)

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Challenge: Modern language models demonstrate impressive coding capabilities in common programming languages (PLs) but their performance in lower-resource PLs is often limited by training data availability.
Approach: They propose a zero-shot cross-programming-language transfer task for code RL . they propose RL training in a source PL fails to improve performance on other target PLs .
Outcome: The proposed approach improves transferability in Llama-3.1 code generation on parallel-stack model . it also improves performance on other target PLs, compared to single-PL SFT .
STELLA: A Multimodal LLM for Protein Functional Annotation via Unified Sequence-Structure Encoding (2026.findings-acl)

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Challenge: a multimodal protein language model (LLM) integrates sequence, structure, and function into functional annotation.
Approach: They propose a multimodal protein language model that synergistically aligns bimodal representations with the textual modality to advance protein functional annotation.
Outcome: The proposed model synergizes bimodal representations with the textual modality to advance protein functional annotation.

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