1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CL2026
Completing Missing Annotation: Multi-Agent Debate for Accurate and Scalable Relevant Assessment for IR Benchmarks
Minjeong Ban, Jeonghwan Choi, Hyangsuk Min +4
Information retrieval (IR) evaluation remains challenging due to incomplete IR benchmark datasets that contain unlabeled relevant chunks. While LLMs and LLM-human hybrid strategies…
cs.CL2025★ 1 cited
Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
Jiaqi Deng, Yuho Lee, Nicole Hee-Yeon Kim +5
We introduce HAMLET, a holistic and automated framework for evaluating the long-context comprehension of large language models (LLMs). HAMLET structures source texts into a three-l…
cs.CL2025
Aligning Extraction and Generation for Robust Retrieval-Augmented Generation
Hwanjun Song, Jeonghwan Choi, Minseok Kim
Retrieval-augmented generation (RAG) enhances LLMs with external knowledge, yet generation remains vulnerable to retrieval-induced noise and uncertain placement of relevant chunks,…