3 papers
cs.CL2026
Cross-Context Verification: Hierarchical Detection of Benchmark Contamination through Session-Isolated Analysis
Tae-Eun Song
LLM coding benchmarks face a credibility crisis: widespread solution leakage and test quality issues undermine SWE-bench Verified, while existing detection methods--paraphrase cons…
cs.CL2026
Cross-Context Review: Improving LLM Output Quality by Separating Production and Review Sessions
Tae-Eun Song
Large language models struggle to catch errors in their own outputs when the review happens in the same session that produced them. This paper introduces Cross-Context Review (CCR)…
cs.CL2026
FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation
Juhyun Oh, Nayeon Lee, Chani Jung +5
Large Language Models (LLMs) often generate overly cautious and vague responses on sensitive topics, sacrificing helpfulness for safety. Existing evaluation frameworks lack systema…