LLM的理论古往今来（持续更新ing...）_domain specialization as the key to make large lan

诸神缄默不语-个人CSDN博文目录

要真说追是很难追上的，反正就……
作为一个笔记集锦。

文中的年份并不严格与内容对应，尤其在跨年的情况下。

数值推理、序列标注/信息抽取相关论文不写在本篇。
在我写prompt技巧的博文（prompt工程（持续更新ing…））中出现过的论文也不在本篇中再度赘述。

2024年

1. 评估
   1. (AAAI) Avoiding Data Contamination in Language Model Evaluation: Dynamic Test Construction with Latest Materials：LatestEval评估方法，利用最新文本避免数据污染
2. 长文本
   1. Soaring from 4K to 400K: Extending LLM’s Context with Activation Beacon
3. (印度理工学院) Large Language Models aren’t all that you need：这篇就是说在传统NER方法上叠加trick其实能超越LLM
4. (谷歌) LLM Augmented LLMs: Expanding Capabilities through Composition：LLM helps LLM。通过cross attention组合LLM

2023年

1. 综述
   1. （人大高瓴人工智能学院）《A Survey of Large Language Models》及其中文版《大语言模型综述》
      A Survey of Large Language Models
      https://github.com/RUCAIBox/LLMSurvey/blob/main/assets/LLM_Survey_Chinese_0418.pdf
   2. Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
   3. On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models
   4. (华为) Aligning Large Language Models with Human: A Survey
   5. A Survey on Evaluation of Large Language Models
   6. A Survey on Multimodal Large Language Models
   7. A Comprehensive Overview of Large Language Models
   8. Igniting Language Intelligence: The Hitchhiker’s Guide From Chain-of-Thought Reasoning to Language Agents
   9. The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
   10. Data Management For Large Language Models: A Survey
   11. (ACM Computing Surveys) Survey of Hallucination in Natural Language Generation
2. 集锦
   1. Open LLM Leaderboard - a Hugging Face Space by HuggingFaceH4
3. 教程
   1. (ACL) Retrieval-based Language Models and Applications
4. The Curse of Recursion: Training on Generated Data Makes Models Forget：（第一个版本的标题比较劲爆）大意就是说用LLM生成的数据再训练LLM会使LLM效果越来越烂
5. Intelligence Primer
6. 长文本
   1. Blockwise Parallel Transformer (BPT)：Blockwise Parallel Transformer for Long Context Large Models
   2. The Impact of Positional Encoding on Length Generalization in Transformers：比较了不同PE的长度泛化效果
   3. （微软）LongNet: Scaling Transformers to 1,000,000,000 Tokens：线性计算复杂性，dilated attention（注意力随距离增加指数下降）
   4. LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models
   5. 综述：Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
   6. (2023 TACL) Lost in the Middle: How Language Models Use Long Contexts
7. (2023 ACL) Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model：将句子表征为向量空间的probability density distribution。这个方法不用retrain，可以直接插到LLM上
8. 为什么现在的LLM都是Decoder only的架构？ - 知乎
9. transformer中的attention为什么scaled? - 知乎
10. 模型蒸馏
    1. Knowledge Distillation of Large Language Models
11. 模型量化
    1. OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
12. ChatGPT大家族系列
    1. 黑客 George Hotz 爆料 GPT-4 由 8 个 MoE 模型组成，真的吗？ - 知乎：听OpenAI讲，这就是大力出奇迹！
    2. Let’s Verify Step by Step
13. ICL
    1. A Survey on In-context Learning
    2. Pre-Training to Learn in Context
    3. (ACL Findings) Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
       1. 讲解博文：清北微软深挖GPT，把上下文学习整明白了！和微调基本一致，只是参数没变而已
    4. (EMNLP) Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning
14. 微调
    1. Symbol tuning improves in-context learning in language models
    2. 优化器
       1. (微软) DeepSpeed
       2. DeepSpeedExamples/applications/DeepSpeed-Chat at master · microsoft/DeepSpeedExamples
    3. MiniChain
    4. (ICLR) Self-Consistency Improves Chain of Thought Reasoning in Language Models
       从语言模型中对不同的输出集（利用采样生成多种不同的推理路径）进行采样，并返回集合中最一致的答案→其实是集成方法，代替CoT的贪心策略
       ReadPaper AI讲解论文：https://readpaper.com/paper/666189129860624384
    5. RLHF
       1. (ICLR) Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
    6. CoT论文列表：https://github.com/Timothyxxx/Chain-of-ThoughtsPapers
    7. The Flan Collection: Designing Data and Methods for Effective Instruction Tuning：prompt集合
    8. (ACL) Self-Instruct: Aligning Language Models with Self-Generated Instructions：bootstraping扩展指令
    9. Exploring Format Consistency for Instruction Tuning：数据集间指令格式统一
    10. Instruction Tuning for Large Language Models: A Survey
    11. Tied-Lora: Enhacing parameter efficiency of LoRA with weight tying
    12. (EACL) DyLoRA: Parameter Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation
    13. Full Parameter Fine-tuning for Large Language Models with Limited Resources：LOMO方法
15. 检索+生成
    1. (ICLR) Generate rather than Retrieve: Large Language Models are Strong Context Generators：认为以前的工作都是先检索后阅读，本文先生成上下文文档，再阅读生成的文档，生成最终答案
       基于聚类的prompt方法：选择不同的prompt，以使生成的文档涵盖不同的视角，从而提高recall
       ReadPaper AI讲解论文：https://readpaper.com/paper/4670624394054221825
    2. Retrieval meets Long Context Large Language Models
    3. Model-enhanced Vector Index
    4. Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models：思路是如果检索到不相关的信息可能会误导LLM，此外LLM本身可能不知道它自身与知识库能提供的知识够不够回答问题。本文提出CON方法来提高RAG的鲁棒性：为检索到的文档生成连续阅读笔记，评估其与问题的相关性，整合生成答案
    5. Learning to Filter Context for Retrieval-Augmented Generation
    6. (苹果) Context Tuning for Retrieval Augmented Generation
    7. Large Language Models for Information Retrieval: A Survey
    8. RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models：高质量语料，包含LLM+RAG的自动回复，标注了幻觉强度等
    9. (ICLR) Quantifying Memorization Across Neural Language Models
16. (ICLR) APE
    Large Language Models Are Human-Level Prompt Engineers：总之就是用LLM自己写prompt
17. (普林斯顿) InstructEval: Systematic Evaluation of Instruction Selection Methods：评估prompt
    参考阅读博文：放弃评测大模型，普林斯顿大学已经开始评估Prompt了，提出Prompt评估框架
18. TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks
    参考阅读博文：GPT-4使用效果不好？美国奥本大学提出Prompt分类法，另辟蹊径构建Prompt设计指南
19. 推荐系统
    1. Is ChatGPT a Good Recommender? A Preliminary Study
    2. 新闻推荐
       1. (SIGIR) Prompt4NR
          Prompt Learning for News Recommendation
20. LLM+KG
    1. KGQA
       1. KAPING
          Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
21. 可靠性¹
    1. Measuring Faithfulness in Chain-of-Thought Reasoning：衡量CoT推理的忠实性
       发现有时模型会忽略生成的推理，就硬是照着自己想的来说（模型越大越容易发生这种事）
       解决方案是选择合适的模型
    2. Question Decomposition Improves the Faithfulness of Model-Generated Reasoning
       给出了解决方案：将问题分解为子问题，并分别解答
       
    3. (Meta) Head-to-Tail: How Knowledgeable are Large Language Models (LLM)? A.K.A. Will LLMs Replace Knowledge Graphs?：认为LLM不能替代LM，因为它不可靠，而且这不可靠很难优化（就常规优化LLM的方法很难解决不可靠问题本身）。
    4. 幻觉问题的综述：
       1. Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models
       2. A Survey of Hallucination in Large Foundation Models
       3. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models’ Alignment
    5. Fine-tuning Language Models for Factuality：评估文本事实真实性，微调语言模型使其产生较少的事实性错误
22. 评测
    (微软) A Survey on Evaluation of Large Language Models
    (天大) Evaluating Large Language Models: A Comprehensive Survey
    GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond
    API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
23. Toolformer
    Toolformer: Language Models Can Teach Themselves to Use Tools：LLM自学使用小工具
    Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT
24. Large Language Models as Optimizers：用自然语言描述优化任务（这个提示也可以优化），让LLM自己优化，一步一步迭代
25. agent
    1. A Survey on Large Language Model based Autonomous Agents
    2. The Rise and Potential of Large Language Model Based Agents: A Survey
26. Detecting Pretraining Data from Large Language Models：检测某段文本是否被LLM用于预训练
27. (Meta) The ART of LLM Refinement: Ask, Refine, and Trust
28. 可解释性：(Anthropic) Towards Monosemanticity: Decomposing Language Models With Dictionary Learning
29. 模型加速：Accelerating Generative AI with PyTorch II: GPT, Fast | PyTorch
30. LM-Cocktail: Resilient Tuning of Language Models via Model Merging：这篇的思路是微调模型会出现灾难性遗忘问题，所以本文将base模型和微调模型的参数加权平均，发现效果很好。但是相关fuse工作这样的结论早就有了，supermario（阿里）的就是直接求平均
31. Contrastive Chain-of-Thought Prompting：idea被scoop原来就是这样的感觉吗（喃喃）
32. Ghostbuster: Detecting Text Ghostwritten by Large Language Models：检索LLM生成的文本
33. Scalable Extraction of Training Data from (Production) Language Models：鼓励ChatGPT泄漏训练数据
34. Prompt Engineering a Prompt Engineer
35. (微软) Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine：这一篇是用提示工程在专业领域上解决任务的
36. SparQ Attention: Bandwidth-Efficient LLM Inference：推理加速
37. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
38. Why “classic” Transformers are shallow and how to make them go deep
39. MoDS: Model-oriented Data Selection for Instruction Tuning
40. Are Emergent Abilities of Large Language Models a Mirage?
41. Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
42. Improving Text Embeddings with Large Language Models

2022年

1. prompt
   1. (ACM Computing Surveys) Re33：读论文 Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Languag
   2. (CHI LBW) PromptChainer: Chaining Large Language Model Prompts through Visual Programming：这个是将多个LLM链接（也就是先过一个LLM，然后再过一个LLM……etc）的可视化编程工具
      
   3. LangChain
   4. LLamaIndex
2. 预训练 / 微调策略
   1. (ICLR) LoRA: Low-Rank Adaptation of Large Language Models
   2. RLHF / instruction following / 指令对齐
      (NeurIPS) Training language models to follow instructions with human feedback
      官方博文：Aligning language models to follow instructions
   3. Scaling Instruction-Finetuned Language Models
   4. instruction-based finetuning / instruction tuning / FLAN：通过instruction（可以理解成“简介”之类的）描述一堆数据集，LM在上面微调，然后LM就能在别的用instruction描述的数据集上面的表现效果也有所提升。就提升LM的零样本学习效果（拿来做实验用的是LaMDA模型）
      论文：(ICLR 谷歌) Finetuned Language Models Are Zero-Shot Learners
      
      
3. 检索
   1. (DeepMind) RETRO
      Improving language models by retrieving from trillions of tokens：检索文档chunk，调节AR语言模型。
      用BERT将token数据库嵌入chunk中
      chunked cross-attention mechanism
4. 涌现能力：(TMLR) Emergent Abilities of Large Language Models

2021年

1. prefix-tuning
   1. (ACL) Prefix-Tuning: Optimizing Continuous Prompts for Generation
      1. 官方讲解视频：Underline | Prefix-Tuning: Optimizing Continuous Prompts for Generation
2. LoRA: Low-Rank Adaptation of Large Language Models
   1. GitHub：microsoft/LoRA: Code for loralib, an implementation of “LoRA: Low-Rank Adaptation of Large Language Models”
3. prompt
   1. thunlp/OpenPrompt: An Open-Source Framework for Prompt-Learning.：支持transformers模型
   2. (ACL) Making Pre-trained Language Models Better Few-shot Learners
      参考学习博文：【综述】Prompting: 更好地将语言模型应用到NLP任务
4. (EACL) Language Models as Knowledge Bases: On Entity Representations, Storage Capacity, and Paraphrased Queries
   关注LLM中的知识如何表示，以及LLM的知识存量（和LLM规模基本线性增长）
5. (EMNLP Findings) Retrieval Augmentation Reduces Hallucination in Conversation：用检索解决幻觉问题

2020年

1. (ACL) Re26：读论文 Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks：大致是说LLM在通用域上预训练之后，在领域和任务上再继续预训练会提升模型效果
2. Adapter
   1. Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning
   2. K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters：解决注入知识的灾难性遗忘问题，用adapter实现
3. RAG
   1. (NeurIPS) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
4. Scaling Laws for Neural Language Models

2019年

1. Adapter：怎么说呢感觉就是在LLM里新加了一个模块，然后只调这个模块，不调全局参数。
   这样使得微调效率upup
   1. 论文：(ICML) Parameter-Efficient Transfer Learning for NLP adapters
   2. 介绍博文：NLP中的Adapters是什么？ | Finisky Garden
   3. AdapterHub - 572 adapters for 76 text tasks and 50 languages
2. LAnguage Model Analysis (LAMA)
   1. 论文：(2019 EMNLP) Language Models as Knowledge Bases?：探查语言模型中的关系知识。具体的解决方案就是将问题作为填空题给出，让模型计算mask位置的结果。结果是LLM做得还不错（我感觉如果用新的LLM可能会效果更好）
3. (NAACL) Linguistic Knowledge and Transferability of Contextual Representations：对比了BERT / ELMo / GPT（文中叫OpenAI transformer），主要探讨了语言模型编码了什么特征和不同层、不同预训练任务的transferibility问题
   
   （这篇论文的实验也太多了，就算以现在的目光来看也只能感慨真是有钱有时间！）
   参考讲解博文：ELMo/GPT/BERT对比 - 知乎
4. (ACL 香侬科技) Is Word Segmentation Necessary for Deep Learning of Chinese Representations?：得到了字几乎总是优于词的结论（苏神认为这篇的实验不足，这篇实验对比字嵌入和词嵌入都用得是随机初始化的嵌入矩阵，这导致以词为单位的模型嵌入层参数更多，容易过拟合。但事实上一般用的都是预训练好的词向量，但这篇文章里没有关注到这个场景。苏神后来做的WoBERT认为以词为单位其实效果更好）
5. (ACL) Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling

2018年

1. (EMNLP) Why Self-Attention? A Targeted Evaluation of Neural Machine Translation Architectures：结合知识图

2016年

1. A Neural Knowledge Language Model：将知识图提供的符号知识与 RNN 语言模型相结合。通过预测要生成的单词是否具有潜在事实，模型可以通过复制预测事实的描述来生成此类与知识相关的单词
2. Reference-Aware Language Models：参考外部信息实现LM

参考资料

1. 我还没读的集成类内容：
   1. thunlp/PromptPapers: Must-read papers on prompt-based tuning for pre-trained language models.
   2. KSESEU/LLMPapers: Papers & Works for large languange models (ChatGPT, GPT-3, Codex etc.).

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1. Measuring and Improving the Faithfulness of Model-Generated Reasoning — LessWrong ↩︎