10 citations · 13 across the 6 of their papers we have counts for
4 papers · 1 filter
Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning
Ho-Lam Chung, Teng-Yun Hsiao, Hsiao-Ying Huang +4
Test-Time Scaling (TTS) improves the reasoning performance of Large Language Models (LLMs) by allocating additional compute during inference. We conduct a structured survey of TTS…
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions
Yu-Ang Lee, Guan-Ting Yi, Mei-Yi Liu +3
Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple comp…
Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
Wei-Hsiang Huang, Chen-Wei Ke, Wei-Ning Chiu +5
Large language models (LLMs) have introduced new paradigms for recommender systems by enabling richer semantic understanding and incorporating implicit world knowledge. In this stu…
Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback
Yen-Ting Lin, Di Jin, Tengyu Xu +11
Large language models (LLMs) have recently demonstrated remarkable success in mathematical reasoning. Despite progress in methods like chain-of-thought prompting and self-consisten…