时间：3月18日上午9:30

地点：新校区新珈楼B103教室

报告题目：浅谈论文调研与研究方法

报告人：覃立波教授

报告人简介：覃立波，中南大学计算机学院特聘教授，主要研究方向为任务型对话系统和自然语言处理。在ACL、EMNLP、AAAI、IJCAI等国际会议上发表论文多篇，研究成果曾入选Paper Digest高影响力论文及获得EMNLP2022 MMNLU Workshop最佳论文奖。担任IJCAI2021高级程序会委员（SPC）、EMNLP2022领域主席。

报告摘要：在科研过程中，初入科研的同学往往会有较长的不适应过渡期。对于初学同学来说最重要的是第一个idea的产生。在这个过程中，如何进行调研，如何去有思路的找到创新的问题都是一个极大的挑战。在这个报告中，我将浅析如何进行一个方向的调研工作，并讲解如何逐步剖析去寻找到一个科研问题，希望能帮助初入科研同学快速进入状态。

报告题目：ChatGPT, using chat to connect human with language models

报告人：王本友助理教授

报告人简介：Benyou Wang is an assistant professor in the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He was a Marie Curie Researcher at the European Union and got his Ph.D. degree from the University of Padua, Italy in 2022. So far, he and his collaborators have won the Best Paper Nomination Award in SIGIR 2017, Best Explainable NLP Paper in NAACL 2019, and Best Paper in NLPCC 2022. He is committed to building novel, explainable, robust, and efficient natural language processing systems that are with both technical rationality and linguistic motivations.

报告摘要：In just three months, ChatGPT, a groundbreaking language model, has gained worldwide recognition. This talk will introduce the concept of "language models" and "prompts" where prompts serve as an interface connecting human with language models. By using text prompts, one could enjoy the merits of ChatGPT for various features including zero-shot generation, in-context learning, complex reasoning, chain of thought, interactive chat, etc; although some of them have already explored by ChatGPT's earlier versions. We will further discuss new features, technical map, limitations, as well as some case study of ChatGPT. To help you make the most of ChatGPT, we will also provide some basic usage guides that can help improve your productivity. Lastly, we will discuss the possibility of creating a local version of ChatGPT and our work on a medical ChatGPT. We are excited to witness the impact that ChatGPT will have on the industry, as well as the wider scientific and engineering communities.

报告题目：快手推荐系统技术分享--- WWW2023专题报告

报告人：刘殊畅博士

报告人简介：刘殊畅，快手高级算法工程师，毕业于罗格斯大学，师从张永锋老师。主要研究方向为推荐系统、迁移学习和端计算，发表CCF-A/B类论文10余篇。WWW/SIGIR/KDD/IJCAI/AAAI评审委员，TORS期刊评审委员。

报告摘要：1月25日，国际学术会议WWW 2023论文接收结果公布。快手社区科学线有多篇论文被录用，本次报告将介绍其中快手推荐策略中台组的3篇工作。快手推荐场景相比传统推荐场景具有大流量、更新快、玩法复杂、用户频繁交互的特点，尤其在持续交互和留存优化等长期目标上使用强化学习解决方案时，这些挑战被进一步放大。报告分享内容将着重探讨其中多目标推荐、强化学习行为空间探索、以及留存优化问题的应对策略与思路。

邀请人：邹立新副教授、李晨亮教授