5 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…
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…
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…
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…
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…