4 citations · 6 across the 6 of their papers we have counts for
6 papers
Teaching Large Language Models to Maintain Contextual Faithfulness via Synthetic Tasks and Reinforcement Learning
Shuzheng Si, Haozhe Zhao, Cheng Gao +11
Teaching large language models (LLMs) to be faithful in the provided context is crucial for building reliable information-seeking systems. Therefore, we propose a systematic framew…
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion
Kangyang Luo, Yuzhuo Bai, Cheng Gao +11
Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs. However, integrating the vital structural information of KGs into Larg…
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
Jinyi Hu, Shengding Hu, Yuxuan Song +6
Autoregressive and diffusion models have achieved remarkable progress in language models and visual generation, respectively. We present ACDiT, a novel Autoregressive blockwise Con…
Configurable Foundation Models: Building LLMs from a Modular Perspective
Chaojun Xiao, Zhengyan Zhang, Chenyang Song +20
Advancements in LLMs have recently unveiled challenges tied to computational efficiency and continual scalability due to their requirements of huge parameters, making the applicati…
FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
Yufei Huang, Xu Han, Maosong Sun
Open Domain Question Answering (ODQA) has been advancing rapidly in recent times, driven by significant developments in dense passage retrieval and pretrained language models. Curr…
Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition
Yufei Huang, Shengding Hu, Xu Han +2
Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models, which challenge human intuit…