百篇论文纵览大型语言模型最新研究进展
© 作者｜王晓磊
机构｜中国人民大学
方向 | 对话式信息获取
来自 | RUC AI Box
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本文整理了2022年以来发表在顶级会议上的大语言模型相关论文。
导读
去年底，OpenAI 推出的 ChatGPT 在短短数月内已经风靡全球。这个基于 GPT-3.5 的大型语言模型，具备惊人的自然语言生成和理解能力，可以像人类一样进行对话、翻译、摘要等任务。由于其优秀的表现，ChatGPT 及其背后的大型语言模型迅速成为人工智能领域的热门话题，吸引了广大科研人员和开发者的关注和参与。
本文整理了 2022 年在各大顶会（ACL、EMNLP、ICLR、ICML、NeurIPS等）发表的和大型语言模型相关的论文，共计 100 篇。论文列表已经同步更新到 Github仓库(https://github.com/RUCAIBox/Top-conference-paper-list)，欢迎大家关注和 Star。
Catalog(目录)
Training【训练】 Pre-Training【预训练】 Instruction Tuning【指令微调】 Utilization【使用】 In-Context Learning【上下文学习】 Chain-of-Thought Prompting【思维链提示】 Compression【压缩】 Others【其他】 Application【应用】 Multi-Modal【多模态】 Code【代码】 Retrieval【检索】 Text Generation【文本生成】 Others【其他】 Analysis & Evaluation【分析与评测】
Training【训练】
Pre-Training【预训练】
UL2: Unifying Language Learning Paradigms Learning to Grow Pretrained Models for Efficient Transformer Training Efficient Large Scale Language Modeling with Mixtures of Experts Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis InCoder: A Generative Model for Code Infilling and Synthesis CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining GLM-130B: An Open Bilingual Pre-trained Model When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain
Instruction Tuning【指令微调】
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks Boosting Natural Language Generation from Instructions with Meta-Learning Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing Multitask Instruction-based Prompting for Fallacy Recognition Not All Tasks Are Born Equal: Understanding Zero-Shot Generalization HypeR: Multitask Hyper-Prompted Training Enables Large-Scale Retrieval Generalization
Utilization【使用】
In-Context Learning【上下文学习】
What learning algorithm is in-context learning? Investigations with linear models Ask Me Anything: A simple strategy for prompting language models Large Language Models are Human-Level Prompt Engineers Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks kNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners Selective Annotation Makes Language Models Better Few-Shot Learners Active Example Selection for In-Context Learning Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? In-Context Learning for Few-Shot Dialogue State Tracking Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback Controllable Dialogue Simulation with In-context Learning Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing On the Compositional Generalization Gap of In-Context Learning Towards In-Context Non-Expert Evaluation of Reflection Generation for Counselling Conversations Towards Few-Shot Identification of Morality Frames using In-Context Learning
Chain-of-Thought Prompting【思维链提示】
ReAct: Synergizing Reasoning and Acting in Language Models Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning Neuro-Symbolic Procedural Planning with Commonsense Prompting Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales Decomposed Prompting: A Modular Approach for Solving Complex Tasks Complexity-Based Prompting for Multi-step Reasoning Automatic Chain of Thought Prompting in Large Language Models Compositional Semantic Parsing with Large Language Models Self-Consistency Improves Chain of Thought Reasoning in Language Models Least-to-Most Prompting Enables Complex Reasoning in Large Language Models Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning Iteratively Prompt Pre-trained Language Models for Chain of Thought ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models
Compression【压缩】
Understanding and Improving Knowledge Distillation for Quantization Aware Training of Large Transformer Encoders The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
Others【其他】
BBTv2: Towards a Gradient-Free Future with Large Language Models Compositional Task Representations for Large Language Models Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Application【应用】
Multi-Modal【多模态】
Visual Classification via Description from Large Language Models Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training
Code【代码】
DocPrompting: Generating Code by Retrieving the Docs Planning with Large Language Models for Code Generation CodeT: Code Generation with Generated Tests Language Models Can Teach Themselves to Program Better
Retrieval【检索】
Promptagator: Few-shot Dense Retrieval From 8 Examples Recitation-Augmented Language Models Generate rather than Retrieve: Large Language Models are Strong Context Generators QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Text Generation【文本生成】
Generating Sequences by Learning to Self-Correct RankGen: Improving Text Generation with Large Ranking Models Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation
Others【其他】
Systematic Rectification of Language Models via Dead-end Analysis Reward Design with Language Models Bidirectional Language Models Are Also Few-shot Learners Composing Ensembles of Pre-trained Models via Iterative Consensus Binding Language Models in Symbolic Languages Mind's Eye: Grounded Language Model Reasoning through Simulation
Analysis & Evaluation【分析与评测】
WikiWhy: Answering and Explaining Cause-and-Effect Questions ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning Quantifying Memorization Across Neural Language Models Mass-Editing Memory in a Transformer Multi-lingual Evaluation of Code Generation Models STREET: A MULTI-TASK STRUCTURED REASONING AND EXPLANATION BENCHMARK Leveraging Large Language Models for Multiple Choice Question Answering Broken Neural Scaling Laws Language models are multilingual chain-of-thought reasoners Language Models are Realistic Tabular Data Generators Task Ambiguity in Humans and Language Models Discovering Latent Knowledge in Language Models Without Supervision Prompting GPT-3 To Be Reliable Large language models are few-shot clinical information extractors How Large Language Models are Transforming Machine-Paraphrase Plagiarism Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs SLING: Sino Linguistic Evaluation of Large Language Models A Systematic Investigation of Commonsense Knowledge in Large Language Models Lexical Generalization Improves with Larger Models and Longer Training What do Large Language Models Learn beyond Language? Probing for Understanding of English Verb Classes and Alternations in Large Pre-trained Language Models
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