Yongqi Li

Data-efficient Fine-tuning for LLM-based Recommendation

2024 · arXiv (Cornell University)

Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits their practical …

Enhancing Tool Retrieval with Iterative Feedback from Large Language Models

2024 · arXiv (Cornell University)

Tool learning aims to enhance and expand large language models' (LLMs) capabilities with external tools, which has gained significant attention recently. Current methods have shown that LLMs can effectively handle a certain amount of tools …

Efficient Inference for Large Language Model-based Generative Recommendation

2024 · arXiv (Cornell University)

Large Language Model (LLM)-based generative recommendation has achieved notable success, yet its practical deployment is costly particularly due to excessive inference latency caused by autoregressive decoding. For lossless LLM decoding acceleration, Speculative Decoding (SD) has …

Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning

2025 · arXiv (Cornell University)

Training deep neural networks is challenging. To accelerate training and enhance performance, we propose PadamP, a novel optimization algorithm. PadamP is derived by applying the adaptive estimation of the p-th power of the second-order moments …