6 papers
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…
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…
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…
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Chao-Han Huck Yang, Taejin Park, Yuan Gong +18
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen,…
A Survey of Useful LLM Evaluation
Ji-Lun Peng, Sijia Cheng, Egil Diau +4
LLMs have gotten attention across various research domains due to their exceptional performance on a wide range of complex tasks. Therefore, refined methods to evaluate the capabil…