KDD2023丨大模型论文合集

AMiner通过AI技术，对 KDD2023 收录的会议论文进行了分类整理，今日分享的是大模型主题论文！（由于篇幅关系，本篇只展现部分论文，点击阅读原文可直达KDD顶会页面查看所有论文）

1.WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences 论文详情页

https://www.aminer.cn/pub/64893b17d68f896efa9826b7/

2.Pre-training Antibody Language Models for Antigen-Specific Computational Antibody Design论文详情页

https://www.aminer.cn/pub/64af9a063fda6d7f065a6c00/

3.LightToken: A Task and Model-agnostic Lightweight Token Embedding Framework for Pre-trained Language Models论文详情页

https://www.aminer.cn/pub/64af99fd3fda6d7f065a62f2/

4.JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving论文详情页

https://www.aminer.cn/pub/64af9a043fda6d7f065a6a45/

5.BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction论文详情页

https://www.aminer.cn/pub/64af9a043fda6d7f065a69f8/

6.RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment论文详情页

https://www.aminer.cn/pub/64af9a053fda6d7f065a6b09/

7.QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search论文详情页

https://www.aminer.cn/pub/6487e9fad68f896efa482b50/

8.Automated 3D Pre-Training for Molecular Property Prediction论文详情页

https://www.aminer.cn/pub/64893b17d68f896efa982588/

9.GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation论文详情页

https://www.aminer.cn/pub/63fec3cd90e50fcafdd70322/

10.CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X论文详情页

https://www.aminer.cn/pub/64264f7b90e50fcafd68e145/

11.Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications论文详情页

https://www.aminer.cn/pub/647eaf51d68f896efad41cdb/

12.Text Is All You Need: Learning Language Representations for Sequential Recommendation论文详情页

https://www.aminer.cn/pub/646d863cd68f896efa09f2e5/

为了让更多科研人更高效的获取文献知识，AMiner基于GLM-130B大模型能力，开发了Chatpaper，帮助科研人快速提高检索、阅读论文效率，获取最新领域研究动态，让科研工作更加游刃有余。

ChatPaper：https://www.aminer.cn/chat/g

阅读原文，直达KDD顶会页面！