3 papers
cs.CL2024
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
Xuefei Ning, Zifu Wang, Shiyao Li +7
Teaching to improve student models (e.g., knowledge distillation) is an extensively studied methodology in LLMs. However, for humans, teaching improves not only students but also t…
cs.CL2024
Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLM
Peiran Yao, Kostyantyn Guzhva, Denilson Barbosa
Symbolic sentence meaning representations, such as AMR (Abstract Meaning Representation) provide expressive and structured semantic graphs that act as intermediates that simplify d…
cs.CL2024
Accurate and Nuanced Open-QA Evaluation Through Textual Entailment
Peiran Yao, Denilson Barbosa
Open-domain question answering (Open-QA) is a common task for evaluating large language models (LLMs). However, current Open-QA evaluations are criticized for the ambiguity in ques…