21 citations · 30 across the 7 of their papers we have counts for
4 papers · 1 filter
PokerBench: Training Large Language Models to become Professional Poker Players
Richard Zhuang, Akshat Gupta, Richard Yang +3
We introduce PokerBench - a benchmark for evaluating the poker-playing abilities of large language models (LLMs). As LLMs excel in traditional NLP tasks, their application to compl…
EmbedLLM: Learning Compact Representations of Large Language Models
Richard Zhuang, Tianhao Wu, Zhaojin Wen +3
With hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream, tasks has become increasi…
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation
Shiyang Lai, Yujin Potter, Junsol Kim +3
Large language model behavior is shaped by the language of those with whom they interact. This capacity and their increasing prevalence online portend that they will intentionally…
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Xiuying Wei, Yunchen Zhang, Yuhang Li +4
Post-training quantization~(PTQ) of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outl…