Hi there 👋
I'm Runsong Zhao 赵润松, an AI major student studying for creating AI from ACGN world!
Now working on:
- implementing the experiment of "Attention Is All You Need"(training now)
- reading paper about LLMs
- Improving the design of LLM in terms of architecture, data and training strategy
Future work:
- to implement gpt2(117M)
- to read paper about reinforcement learning, Memory network and Multimodal
Have read:
| Survey |
|---|
| Open-domain Dialogue Generation:What We Can Do, Cannot Do, And Should Do Next |
| (2020)Towards Unified Dialogue System Evaluation |
| 2022-A Survey for In-context Learning |
| Emergent Abilities of Large Language Models |
| Dialogue system |
| (2015)Neural Responding Machine for Short-Text Conversation |
| (2015)A Neural Conversational Model |
| (2018)Extending Neural Generative Conversational Model using External Knowledge Sources |
| (2016)A Persona-Based Neural Conversation Model |
| (2018b)Personalizing Dialogue Agents I have a dog, do you have pets too |
| (2018b) [Note] Personalizing Dialogue Agents I have a dog, do you have pets too |
| (2018)An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation |
| (2020)Enhancing Dialogue Generation via Multi-Level Contrastive Learning |
| (Zhang 2020a)Modeling Topical Relevance for Multi-Turn Dialogue Generation |
| (Ghazvininejad 2017)A knowledge-grounded neural conversation model |
| (2022 Ju)Learning to Improve Persona Consistency in Multi-party Dialogue |
| LLMs |
| (2018 Radford)Improving Language Understanding by Generative Pre-Training |
| (GPT2)language models are unsupervised multitask learners |
| GPT3 Language Models are Few-Shot Learners |
| (instructGPT)Training language models to follow instructions with human feedback |
| (Code X)Evaluating Large Language Models Trained on Code |
| GPT-4 Technical Report |
| PTM |
| BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |
| (ALBERT) A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS |
| (RoBERTa)A Robustly Optimized BERT Pretraining Approach |
| (T5)Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer |
| Reinforcement learning |
| (PPO)Proximal Policy Optimization Algorithms |
| other |
| (2002)BLEU a Method for Automatic Evaluation of Machine Translation |
| (2019 Feng)Answer-guided and Semantic Coherent Question Generation |
| (2020 Feng)A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness |
| (transformer)Attention Is All You Need |
| (latent diffusion)High-Resolution Image Synthesis with Latent Diffusion Models |
个人经历
高中毕业时想要做出许多幻想作品中的AI（例如Atri），于是第一志愿报了东北大学的人工智能专业。我当时的想法是：只要AI出现就好，不是我创造的也没关系。我当时只了解到神经网络、强化学习等名词，也不知道他们是什么意思。
大一时，我看了导论，但只了解到“符号主义”，“连接主义”，“AGI”等泛泛而谈的概念和历史，而对具体的技术一无所知。于是我去找神经网络的资料去学习，很遗憾，因为数学知识的缺乏，学习并不顺利，但还是能理解到神经网络是通过“权重”去影响决策的。到此，我的AI学习之路开始停滞，这段时间我参加了学校的ACM队。
大二上，我通过校内渠道参加了百度的AI培训班，了解到CV和NLP两个方向。我认为NLP是更触及灵魂的部分，所以我选择了NLP。
大二下，最优化学完后，我学习深度学习的技术没有太大的障碍了，但是知识面不够广。当时我认为找到了正确的方向，所以选择退出ACM队（大三才能参加ACM-ICPC，虽然很遗憾，但我不能把时间花在这里了），把重心放到学习NLP上。我在暑假完成了百度AI培训班的大部分项目，同时确认了NLP各种技术中“开放域对话系统”与我想要的AI最像并开始阅读相关的论文。同时从这个时间点开始到现在，我开始涌现出各种关于AI的idea，我把每个idea都记在了便签里（在之后的学习中，我发现许多idea已经被证实有效）。
同时也是在大二暑假开始，各种AI技术开始应用到二次元中。首先是语言合成：Tacotron2、TTS和VITS(8月)，然后是对话系统：彩云小梦2.0（8月），最后是图像合成：novelai(8月出，10月火)
大三上，专业课开始，底层的原理和逻辑也搞明白了，同时每次上课都会偶尔迸出一些idea。同时迫于升学的压力开始卷绩点。寒假，联系上了本校的冯时老师，冯老师了解了我的情况，让我去调研现在的对话系统。这是一个重要的转折点。我体验了当时（一月份）大部分有潜力的对话系统，很遗憾，大多数都是人工智障。不过辛运的是，有三个产品不是智障。他们分别是：ChatGPT、character AI和Glow(当时Neuro-sama还在封禁，Moss还没出来，不作评价)。其中Glow是国产的，当时我快对国内的产品失望了，最后找到Glow让我恢复了信心。由于三个产品中，只有ChatGPT透露了技术细节，所以我去阅读了chatGPT的相关论文。从此，我从对话系统转向LLMs，而且我认为LLMs最有潜力实现我高中时设想的AI。
现在是大三下学期，我已经不满足于AI出现，而是想要参与其中。
未来的设想
LLMs + Memory + Reinforcement learning + Multimodal = AGI
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent