35 citations · 47 across the 15 of their papers we have counts for
5 papers · 1 filter
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process
Zhijun Chen, Zeyu Ji, Qianren Mao +13
We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…
Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation
Kaidong Feng, Zhu Sun, Hui Fang +3
Large Language Models (LLMs) have shown potential in automatic bundle generation but suffer from prohibitive computational costs. Although knowledge distillation offers a pathway t…
Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?
Kaidong Feng, Zhu Sun, Jie Yang +3
LLMs are increasingly explored for bundle generation, thanks to their reasoning capabilities and knowledge. However, deploying large-scale LLMs introduces significant efficiency ch…
Large Language Models for Intent-Driven Session Recommendations
Zhu Sun, Hongyang Liu, Xinghua Qu +3
Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to th…