3 papers
cs.SE2026
IncreRTL: Traceability-Guided Incremental RTL Generation under Requirement Evolution
Luanrong Chen, Renzhi Chen, Xinyu Li +3
Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods remain static and struggle to adapt to evolving desi…
cs.CL2025
Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
Shanglin Wu, Lihui Liu, Jinho D. Choi +1
Large Language Models (LLMs) often struggle with producing factually consistent answers due to limitations in their parametric memory. Retrieval-Augmented Generation (RAG) paradigm…
cs.CL2024
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
Liyan Xu, Zhenlin Su, Mo Yu +4
Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stron…