4 citations · 5 across the 7 of their papers we have counts for
7 papers
Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams
Jiyeon Kim, Hyunji Lee, Dylan Zhou +6
LLMs operating in dynamic real-world contexts often encounter knowledge that evolves continuously or emerges incrementally. To remain accurate and effective, models must adapt to n…
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
Seongyun Lee, Geewook Kim, Jiyeon Kim +4
Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromise…
Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization
Miyoung Ko, Sue Hyun Park, Joonsuk Park +1
Despite the advances in large language models (LLMs), how they use their knowledge for reasoning is not yet well understood. In this study, we propose a method that deconstructs co…
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
Seungone Kim, Juyoung Suk, Ji Yong Cho +29
As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…
Aligning to Thousands of Preferences via System Message Generalization
Seongyun Lee, Sue Hyun Park, Seungone Kim +1
Although humans inherently have diverse values, current large language model (LLM) alignment methods often assume that aligning LLMs with the general public's preferences is optima…
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Seongyun Lee, Seungone Kim, Sue Hyun Park +2
Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging. It not only requires checking whether the VLM follows the given instruction but also verify…
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision
Seongyun Lee, Sue Hyun Park, Yongrae Jo +1
Large multimodal models suffer from multimodal hallucination, where they provide incorrect responses misaligned with the given visual information. Recent works have conjectured tha…