Compositional generalization for multi-label text classification: A data-augmentation approach
Despite significant advancements in multi-label text classification, the ability of existing
models to generalize to novel and seldom-encountered complex concepts, which are …
models to generalize to novel and seldom-encountered complex concepts, which are …
Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
Molecule synthesis through machine learning is one of the fundamental problems in drug
discovery. Current data-driven strategies employ one-step retrosynthesis models and search …
discovery. Current data-driven strategies employ one-step retrosynthesis models and search …
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach
Post-editing has proven effective in improving the quality of text generated by large
language models (LLMs) such as GPT-3.5 or GPT-4, particularly when direct updating of …
language models (LLMs) such as GPT-3.5 or GPT-4, particularly when direct updating of …