6 papers
Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning
Junseok Kim, Nakyeong Yang, Kyungmin Min +1
Self-Consistency improves reasoning reliability through multi-sample aggregation, but incurs substantial inference cost. Adaptive self-consistency methods mitigate this issue by ad…
ReflectCAP: Detailed Image Captioning with Reflective Memory
Kyungmin Min, Minbeom Kim, Kang-il Lee +2
Detailed image captioning demands both factual grounding and fine-grained coverage, yet existing methods have struggled to achieve them simultaneously. We address this tension with…
Fooling the LVLM Judges: Visual Biases in LVLM-Based Evaluation
Yerin Hwang, Dongryeol Lee, Kyungmin Min +3
Recently, large vision-language models (LVLMs) have emerged as the preferred tools for judging text-image alignment, yet their robustness along the visual modality remains underexp…
Black-Box Hallucination Detection via Consistency Under the Uncertain Expression
Seongho Joo, Kyungmin Min, Jahyun Koo +1
Despite the great advancement of Language modeling in recent days, Large Language Models (LLMs) such as GPT3 are notorious for generating non-factual responses, so-called "hallucin…
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding
Kyungmin Min, Minbeom Kim, Kang-il Lee +2
Large Vision-Language Models (LVLMs) demonstrate impressive capabilities in generating detailed and coherent responses from visual inputs. However, they are prone to generate hallu…
Return of EM: Entity-driven Answer Set Expansion for QA Evaluation
Dongryeol Lee, Minwoo Lee, Kyungmin Min +2
Recently, directly using large language models (LLMs) has been shown to be the most reliable method to evaluate QA models. However, it suffers from limited interpretability, high c…