3 papers
cs.CL2026
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.SE2026
ReCUBE: Evaluating Repository-Level Context Utilization in Code Generation
Jiseung Hong, Benjamin G. Ascoli, Jinho D. Choi
Large Language Models (LLMs) have recently emerged as capable coding assistants that operate over large codebases through either agentic exploration or full-context generation. Exi…
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…