4 citations · 11 across the 9 of their papers we have counts for
5 papers · 1 filter
LLM-as-a-Judge & Reward Model: What They Can and Cannot Do
Guijin Son, Hyunwoo Ko, Hoyoung Lee +2
LLM-as-a-Judge and reward models are widely used alternatives of multiple-choice questions or human annotators for large language model (LLM) evaluation. Their efficacy shines in e…
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…
ESG Classification by Implicit Rule Learning via GPT-4
Hyo Jeong Yun, Chanyoung Kim, Moonjeong Hahm +2
Environmental, social, and governance (ESG) factors are widely adopted as higher investment return indicators. Accordingly, ongoing efforts are being made to automate ESG evaluatio…
Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?
Guijin Son, Sangwon Baek, Sangdae Nam +2
Large language models (LLMs) are typically prompted to follow a single instruction per inference call. In this work, we analyze whether LLMs also hold the capability to handle mult…
KMMLU: Measuring Massive Multitask Language Understanding in Korean
Guijin Son, Hanwool Lee, Sungdong Kim +6
We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. While prior Korean benchmarks are tr…