大型語言模型（LLM）是一種基于深度學習的自然語言處理模型，它能夠學習到自然語言的文法和語義，進而可以生成人類可讀的文本。大型語言模型被用于許多領域，包括自然語言處理 (NLP)、計算機視覺、音頻和語音處理、生物學等，以及多個媒介之間進行模組化和表示學習，生成和語言翻譯任務。
ChatGPT的爆火讓更多的人關注大模型，我們根據今年AI頂會整理了大模型相關的論文，關于大模型的頂會論文清單如下（由于篇幅關系，本篇隻展現部分頂會論文，點選閱讀原文可直達頂會會議清單檢視所有論文）
1.WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
2.Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models
3.Generative Relevance Feedback with Large Language Models
4.Benchmarking Middle-Trained Language Models for Neural Search
5.What Makes Pre-trained Language Models Better Zero/Few-shot Learners?
6.GLM-130B: An Open Bilingual Pre-trained Model
7.Self-Consistency Improves Chain of Thought Reasoning in Language Models
8.Large Language Models Are Human-Level Prompt Engineers
9.EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
10.Discovering Latent Knowledge in Language Models Without Supervision
11.Language Modelling with Pixels
12.Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization
13.Learning on Large-scale Text-attributed Graphs via Variational Inference
14.Generate rather than Retrieve: Large Language Models are Strong Context Generators
15.Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning
16.Selective Annotation Makes Language Models Better Few-Shot Learners
17.PEER: A Collaborative Language Model
18.ReAct: Synergizing Reasoning and Acting in Language Models
19.Reward Design with Language Models
20.Automatic Chain of Thought Prompting in Large Language Models
21.UniMax: Fairer and More Effective Language Sampling for Large-Scale Multilingual Pretraining
22.Quantifying Memorization Across Neural Language Models
23.Compositional Semantic Parsing with Large Language Models
24.Ask Me Anything: A simple strategy for prompting language models
25.Language Models are Multilingual Chain-of-Thought Reasoners
26.Learning to Jointly Share and Prune Weights for Grounding Based Vision and Language Models
27.Generating Sequences by Learning to Self-Correct
28.Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning
29.Visual Classification via Description from Large Language Models
30.Recitation-Augmented Language Models
31.Language Models are Realistic Tabular Data Generators
32.Training language models for deeper understanding improves brain alignment
33.Progressive Prompts: Continual Learning for Language Models without Forgetting
34.Can discrete information extraction prompts generalize across language models?
35.On Pre-training Language Model for Antibody
36.Open-Vocabulary Object Detection upon Frozen Vision and Language Models
37.Mass-Editing Memory in a Transformer
38.Language Models Can Teach Themselves to Program Better
39.Out-of-Distribution Detection and Selective Generation for Conditional Language Models
40.Compositional Task Representations for Large Language Models
41.Planning with Large Language Models for Code Generation
42.Prototypical Calibration for Few-shot Learning of Language Models
43.Multi-lingual Evaluation of Code Generation Models
44.Planning with Language Models through Iterative Energy Minimization
45.Dataless Knowledge Fusion by Merging Weights of Language Models
46.Task Ambiguity in Humans and Language Models
47.Language Models Can (kind of) Reason: A Systematic Formal Analysis of Chain-of-Thought
48.Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks
49.Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small
50.Leveraging Large Language Models for Multiple Choice Question Answering
如何使用ChatPaper？
為了讓更多科研人更高效的擷取文獻知識，AMiner基于GLM-130B大模型能力，開發了Chatpaper，幫助科研人快速提高檢索、閱讀論文效率，擷取最新領域研究動态，讓科研工作更加遊刃有餘。
ChatPaper是一款集檢索、閱讀、知識問答于一體的對話式私有知識庫，AMiner希望通過技術的力量，讓大家更加高效地擷取知識。
ChatPaper：https://www.aminer.cn/chat/g