xiangxiang Zeng

Aligning Large Language Models (LLMs) to human preferences is pivotal for Machine Translation (MT), yet current approaches are often hindered by misleading reward signals. Our analysis reveals that prevailing Quality Estimation (QE) models exhibit a systematic blind spot towards partial errors —s...

Transfer learning has demonstrated efficacy in single-property constraint molecular generation. However, real-world drug discovery demands molecules to satisfy multiple property constraints. Existing paradigms often struggle with this challenge due to catastrophic forgetting or gradient conflicts...