33 citations · 92 across the 23 of their papers we have counts for
Showing 2024 · cs.AIShow all
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cs.AI2024
Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?
Kuei-Chun Kao, Ruochen Wang, Cho-Jui Hsieh
Large Language Models (LLMs) have demonstrated remarkable performance in solving math problems, a hallmark of human intelligence. Despite high success rates on current benchmarks;…
cs.AI2024
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Ruochen Wang, Sohyun An, Minhao Cheng +3
Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and in-context demos. Since this ability sensitively…
cs.AI2024★ 2 cited
Large Language Models are Interpretable Learners
Ruochen Wang, Si Si, Felix Yu +3
The trade-off between expressiveness and interpretability remains a core challenge when building human-centric predictive models for classification and decision-making. While symbo…