4 papers
Argument Collapse: LLMs Flatten Long-Form Public Debate
Yekyung Kim, Yapei Chang, Chau Minh Pham +1
As LLMs are increasingly used to draft publicfacing arguments, they may flatten public debate by repeatedly introducing the same polished, plausible arguments. We study argument co…
BLEUBERI: BLEU is a surprisingly effective reward for instruction following
Yapei Chang, Yekyung Kim, Michael Krumdick +4
Reward models are central to aligning LLMs with human preferences, but they are costly to train, requiring large-scale human-labeled preference data and powerful pretrained LLM bac…
One ruler to measure them all: Benchmarking multilingual long-context language models
Yekyung Kim, Jenna Russell, Marzena Karpinska +1
We present ONERULER, a multilingual benchmark designed to evaluate long-context language models across 26 languages. ONERULER adapts the English-only RULER benchmark (Hsieh et al.,…
VERISCORE: Evaluating the factuality of verifiable claims in long-form text generation
Yixiao Song, Yekyung Kim, Mohit Iyyer
Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et al., 2023) and SAFE (Wei et al., 2024), decompose an input text into "atomic claims" and…