7 papers · 1 filter
Hybrid Verified Decoding: Learning to Allocate Verification in Speculative Decoding
Xin Su, Dawid Majchrowski, Fangyuan Yu +5
Large Language Model (LLM) generation remains expensive because autoregressive decoding calls the model once for each new token. Speculative decoding reduces this cost by drafting…
Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation
Prafulla Kumar Choubey, Xin Su, Man Luo +9
Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economic…
MedCritical: Enhancing Medical Reasoning in Small Language Models via Self-Collaborative Correction
Xinchun Su, Chunxu Luo, Yixuan Li +2
In the field of medicine, complex reasoning tasks such as clinical diagnosis, treatment planning, and medical knowledge integration pose significant challenges, where small languag…
Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Sezen Perçin, Xin Su, Qutub Sha Syed +4
Large language models (LLMs) are very costly and inefficient to update with new information. To address this limitation, retrieval-augmented generation (RAG) has been proposed as a…
A Semantic Parsing Framework for End-to-End Time Normalization
Xin Su, Sungduk Yu, Phillip Howard +1
Time normalization is the task of converting natural language temporal expressions into machine-readable representations. It underpins many downstream applications in information r…
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs
Xin Su, Man Luo, Kris W Pan +3
Multimodal retrieval augmented generation (RAG) plays a crucial role in domains such as knowledge-based visual question answering (KB-VQA), where external knowledge is needed to an…