Awesome-Multimodal-LLM

✨✨✨ Behold our meticulously curated trove of Multimodal Large Language Models (MLLM) resources! 📚🔍 Feast your eyes on an assortment of datasets, techniques for tuning multimodal instructions, methods for multimodal in-context learning, approaches for multimodal chain-of-thought, visual reasoning aided by gargantuan language models, foundational models, and much more. 🌟🔥

✨✨✨ This compilation shall forever stay in sync with the vanguard of breakthroughs in the realm of MLLM. 🔄 We are committed to its perpetual evolution, ensuring that you never miss out on the latest developments. 🚀💡

✨✨✨ And hold your breath, for we are diligently crafting a survey paper on latest LLM & MLLM, which shall soon grace the world with its wisdom. Stay tuned for its grand debut! 🎉📑

Table of Contents

• Awesome Papers
  • Multimodal Instruction Tuning
  • Multimodal In-Context Learning
  • Multimodal Chain-of-Thought
  • LLM-Aided Visual Reasoning
  • Foundation Models
  • Others
• Awesome Datasets
  • Datasets of Pre-Training for Alignment
  • Datasets of Multimodal Instruction Tuning
  • Datasets of In-Context Learning
  • Datasets of Multimodal Chain-of-Thought
  • RLHFdataset

---

LLM Learning MindMap

---

Trending LLM Projects

• llm-course - Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
• Mixtral 8x7B - a high-quality sparse mixture of experts model (SMoE) with open weights.
• promptbase - All things prompt engineering.
• ollama - Get up and running with Llama 2 and other large language models locally.
• Devika Devin alternate SDE LLM
• anything-llm - A private ChatGPT to chat with anything!
• phi-2 - a 2.7 billion-parameter language model that demonstrates outstanding reasoning and language understanding capabilities, showcasing state-of-the-art performance among base language models with less than 13 billion parameters.

Practical Guides for Prompting (Helpful)

• OpenAI Cookbook. Blog
• Prompt Engineering. Blog
• ChatGPT Prompt Engineering for Developers! Course

High-quality generation

• [2023/10] Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond Liang Chen et al. arXiv. [paper] [code]
  • This work proposes PCA-EVAL, which benchmarks embodied decision making via MLLM-based End-to-End method and LLM-based Tool-Using methods from Perception, Cognition and Action Levels.
• [2023/08] A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity. Yejin Bang et al. arXiv. [paper]
  • This work evaluates the multitask, multilingual and multimodal aspects of ChatGPT using 21 data sets covering 8 different common NLP application tasks.
• [2023/06] LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models. Yen-Ting Lin et al. arXiv. [paper]
  • The LLM-EVAL method evaluates multiple dimensions of evaluation, such as content, grammar, relevance, and appropriateness.
• [2023/04] Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation. Tao Fang et al. arXiv. [paper]
  • The results of evaluation demonstrate that ChatGPT has excellent error detection capabilities and can freely correct errors to make the corrected sentences very fluent. Additionally, its performance in non-English and low-resource settings highlights its potential in multilingual GEC tasks.

Deep understanding

• [2023/06] Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models. Natalie Shapira et al. arXiv. [paper]
  • LLMs exhibit certain theory of mind abilities, but this behavior is far from being robust.
• [2022/08] Inferring Rewards from Language in Context. Jessy Lin et al. ACL. [paper]
  • This work presents a model that infers rewards from language and predicts optimal actions in unseen environment.
• [2021/10] Theory of Mind Based Assistive Communication in Complex Human Robot Cooperation. Moritz C. Buehler et al. arXiv. [paper]
  • This work designs an agent Sushi with an understanding of the human during interaction.

Memory capability

Raising the length limit of Transformers

• [2023/10] MemGPT: Towards LLMs as Operating Systems. Charles Packer (UC Berkeley) et al. arXiv. [paper] [project page] [code] [dataset]
• [2023/05] Randomized Positional Encodings Boost Length Generalization of Transformers. Anian Ruoss (DeepMind) et al. arXiv. [paper] [code]
• [2023-03] CoLT5: Faster Long-Range Transformers with Conditional Computation. Joshua Ainslie (Google Research) et al. arXiv. [paper]
• [2022/03] Efficient Classification of Long Documents Using Transformers. Hyunji Hayley Park (Illinois University) et al. arXiv. [paper] [code]
• [2021/12] LongT5: Efficient Text-To-Text Transformer for Long Sequences. Mandy Guo (Google Research) et al. arXiv. [paper] [code]
• [2019/10] BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Michael Lewis (Facebook AI) et al. arXiv. [paper] [code]

Summarizing memory

• [2023/10] Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading Howard Chen (Princeton University) et al. arXiv. [paper]
• [2023/09] Empowering Private Tutoring by Chaining Large Language Models Yulin Chen (Tsinghua University) et al. arXiv. [paper]
• [2023/08] ExpeL: LLM Agents Are Experiential Learners. Andrew Zhao (Tsinghua University) et al. arXiv. [paper] [code]
• [2023/08] ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate. Chi-Min Chan (Tsinghua University) et al. arXiv. [paper] [code]
• [2023/05] MemoryBank: Enhancing Large Language Models with Long-Term Memory. Wanjun Zhong (Harbin Institute of Technology) et al. arXiv. [paper] [code]
• [2023/04] Generative Agents: Interactive Simulacra of Human Behavior. Joon Sung Park (Stanford University) et al. arXiv. [paper] [code]
• [2023/04] Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System. Xinnian Liang (Beihang University) et al. arXiv. [paper] [code]
• [2023/03] Reflexion: Language Agents with Verbal Reinforcement Learning. Noah Shinn (Northeastern University) et al. arXiv. [paper] [code]
• [2023/05] RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text. Wangchunshu Zhou (AIWaves) et al. arXiv. [paper] [code]

Compressing memories with vectors or data structures

• [2023/07] Communicative Agents for Software Development. Chen Qian (Tsinghua University) et al. arXiv. [paper] [code]
• [2023/06] ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory. Chenxu Hu (Tsinghua University) et al. arXiv. [paper] [code]
• [2023/05] Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory. Xizhou Zhu (Tsinghua University) et al. arXiv. [paper] [code]
• [2023/05] RET-LLM: Towards a General Read-Write Memory for Large Language Models. Ali Modarressi (LMU Munich) et al. arXiv. [paper] [code]
• [2023/05] RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text. Wangchunshu Zhou (AIWaves) et al. arXiv. [paper] [code]

Memory retrieval

• [2023/08] Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents. Ziheng Huang (University of California—San Diego) et al. arXiv. [paper]
• [2023/08] AgentSims: An Open-Source Sandbox for Large Language Model Evaluation. Jiaju Lin (PTA Studio) et al. arXiv. [paper] [project page] [code]
• [2023/06] ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory. Chenxu Hu (Tsinghua University) et al. arXiv. [paper] [code]
• [2023/05] MemoryBank: Enhancing Large Language Models with Long-Term Memory. Wanjun Zhong (Harbin Institute of Technology) et al. arXiv. [paper] [code]
• [2023/04] Generative Agents: Interactive Simulacra of Human Behavior. Joon Sung Park (Stanford) et al. arXiv. [paper] [code]
• [2023/05] RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text. Wangchunshu Zhou (AIWaves) et al. arXiv. [paper] [code]

Awesome Papers

Multimodal Instruction Tuning

Title	Venue	Date	Code	Demo
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models	arXiv	2023-06-08	Github	Demo
MIMIC-IT: Multi-Modal In-Context Instruction Tuning	arXiv	2023-06-08	Github	Demo
M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning	arXiv	2023-06-07	-	-
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding	arXiv	2023-06-05	Github	Demo
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day	arXiv	2023-06-01	Github	-
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction	arXiv	2023-05-30	Github	Demo
PandaGPT: One Model To Instruction-Follow Them All	arXiv	2023-05-25	Github	Demo
ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst	arXiv	2023-05-25	Github	-
Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models	arXiv	2023-05-24	Github	Local Demo
DetGPT: Detect What You Need via Reasoning	arXiv	2023-05-23	Github	Demo
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks	arXiv	2023-05-18	Github	Demo
VisualGLM-6B	-	2023-05-17	Github	Local Demo
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering	arXiv	2023-05-17	Github	-
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning	arXiv	2023-05-11	Github	Local Demo
VideoChat: Chat-Centric Video Understanding	arXiv	2023-05-10	Github	Demo
MultiModal-GPT: A Vision and Language Model for Dialogue with Humans	arXiv	2023-05-08	Github	Demo
X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages	arXiv	2023-05-07	Github	-
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model	arXiv	2023-04-28	Github	Demo
mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality	arXiv	2023-04-27	Github	Demo
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models	arXiv	2023-04-20	Github	-
Visual Instruction Tuning	arXiv	2023-04-17	GitHub	Demo
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention	arXiv	2023-03-28	Github	Demo
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning	arXiv	2022-12-21	-	-

Multimodal In-Context Learning

Title	Venue	Date	Code	Demo
MIMIC-IT: Multi-Modal In-Context Instruction Tuning	arXiv	2023-06-08	Github	Demo
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models	arXiv	2023-04-19	Github	Demo
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace	arXiv	2023-03-30	Github	Demo
MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action	arXiv	2023-03-20	Github	Demo
Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering	CVPR	2023-03-03	Github	-
Visual Programming: Compositional visual reasoning without training	CVPR	2022-11-18	Github	Local Demo
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA	AAAI	2022-06-28	Github	-
Flamingo: a Visual Language Model for Few-Shot Learning	NeurIPS	2022-04-29	Github	Demo
Multimodal Few-Shot Learning with Frozen Language Models	NeurIPS	2021-06-25	-	-

Multimodal Chain-of-Thought

Title	Venue	Date	Code	Demo
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought	arXiv	2023-05-24	Github	-
Let’s Think Frame by Frame: Evaluating Video Chain of Thought with Video Infilling and Prediction	arXiv	2023-05-23	-	-
Caption Anything: Interactive Image Description with Diverse Multimodal Controls	arXiv	2023-05-04	Github	Demo
Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings	arXiv	2023-05-03	Coming soon	-
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models	arXiv	2023-04-19	Github	Demo
Chain of Thought Prompt Tuning in Vision Language Models	arXiv	2023-04-16	Coming soon	-
MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action	arXiv	2023-03-20	Github	Demo
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models	arXiv	2023-03-08	Github	Demo
Multimodal Chain-of-Thought Reasoning in Language Models	arXiv	2023-02-02	Github	-
Visual Programming: Compositional visual reasoning without training	CVPR	2022-11-18	Github	Local Demo
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering	NeurIPS	2022-09-20	Github	-

LLM-Aided Visual Reasoning

Title	Venue	Date	Code	Demo
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction	arXiv	2023-05-30	Github	Demo
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models	arXiv	2023-05-24	Github	-
IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models	arXiv	2023-05-24	Github	Local Demo
Caption Anything: Interactive Image Description with Diverse Multimodal Controls	arXiv	2023-05-04	Github	Demo
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models	arXiv	2023-04-19	Github	Demo
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace	arXiv	2023-03-30	Github	Demo
MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action	arXiv	2023-03-20	Github	Demo
ViperGPT: Visual Inference via Python Execution for Reasoning	arXiv	2023-03-14	Github	Local Demo
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions	arXiv	2023-03-12	Github	Local Demo
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models	arXiv	2023-03-08	Github	Demo
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners	CVPR	2023-03-03	Github	-
PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning	CVPR	2022-11-21	Github	-
Visual Programming: Compositional visual reasoning without training	CVPR	2022-11-18	Github	Local Demo
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language	arXiv	2022-04-01	Github	-

Foundation Models

Title	Venue	Date	Code	Demo
Transfer Visual Prompt Generator across LLMs	arXiv	2023-05-02	Github	Demo
GPT-4 Technical Report	arXiv	2023-03-15	-	-
PaLM-E: An Embodied Multimodal Language Model	arXiv	2023-03-06	-	Demo
Prismer: A Vision-Language Model with An Ensemble of Experts	arXiv	2023-03-04	Github	Demo
Language Is Not All You Need: Aligning Perception with Language Models	arXiv	2023-02-27	Github	-
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models	arXiv	2023-01-30	Github	Demo
VIMA: General Robot Manipulation with Multimodal Prompts	ICML	2022-10-06	Github	Local Demo
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge	NeurIPS	2022-06-17	Github	-

Milestone Papers

Date	keywords	Institute	Paper	Publication
2017-06	Transformers	Google	Attention Is All You Need	NeurIPS
2018-06	GPT 1.0	OpenAI	Improving Language Understanding by Generative Pre-Training
2018-10	BERT	Google	BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	NAACL
2019-02	GPT 2.0	OpenAI	Language Models are Unsupervised Multitask Learners
2019-09	Megatron-LM	NVIDIA	Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
2019-10	T5	Google	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	JMLR
2019-10	ZeRO	Microsoft	ZeRO: Memory Optimizations Toward Training Trillion Parameter Models	SC
2020-01	Scaling Law	OpenAI	Scaling Laws for Neural Language Models
2020-05	GPT 3.0	OpenAI	Language models are few-shot learners	NeurIPS
2021-01	Switch Transformers	Google	Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	JMLR
2021-08	Codex	OpenAI	Evaluating Large Language Models Trained on Code
2021-08	Foundation Models	Stanford	On the Opportunities and Risks of Foundation Models
2021-09	FLAN	Google	Finetuned Language Models are Zero-Shot Learners	ICLR
2021-10	T0	HuggingFace et al.	Multitask Prompted Training Enables Zero-Shot Task Generalization	ICLR
2021-12	GLaM	Google	GLaM: Efficient Scaling of Language Models with Mixture-of-Experts	ICML
2021-12	WebGPT	OpenAI	WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing
2021-12	Retro	DeepMind	Improving language models by retrieving from trillions of tokens	ICML
2021-12	Gopher	DeepMind	Scaling Language Models: Methods, Analysis & Insights from Training Gopher
2022-01	COT	Google	Chain-of-Thought Prompting Elicits Reasoning in Large Language Models	NeurIPS
2022-01	LaMDA	Google	LaMDA: Language Models for Dialog Applications
2022-01	Minerva	Google	Solving Quantitative Reasoning Problems with Language Models	NeurIPS
2022-01	Megatron-Turing NLG	Microsoft&NVIDIA	Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model
2022-03	InstructGPT	OpenAI	Training language models to follow instructions with human feedback
2022-04	PaLM	Google	PaLM: Scaling Language Modeling with Pathways
2022-04	Chinchilla	DeepMind	An empirical analysis of compute-optimal large language model training	NeurIPS
2022-05	OPT	Meta	OPT: Open Pre-trained Transformer Language Models
2022-05	UL2	Google	Unifying Language Learning Paradigms
2022-06	Emergent Abilities	Google	Emergent Abilities of Large Language Models	TMLR
2022-06	BIG-bench	Google	Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
2022-06	METALM	Microsoft	Language Models are General-Purpose Interfaces
2022-09	Sparrow	DeepMind	Improving alignment of dialogue agents via targeted human judgements
2022-10	Flan-T5/PaLM	Google	Scaling Instruction-Finetuned Language Models
2022-10	GLM-130B	Tsinghua	GLM-130B: An Open Bilingual Pre-trained Model	ICLR
2022-11	HELM	Stanford	Holistic Evaluation of Language Models
2022-11	BLOOM	BigScience	BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
2022-11	Galactica	Meta	Galactica: A Large Language Model for Science
2022-12	OPT-IML	Meta	OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
2023-01	Flan 2022 Collection	Google	The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
2023-02	LLaMA	Meta	LLaMA: Open and Efficient Foundation Language Models
2023-02	Kosmos-1	Microsoft	Language Is Not All You Need: Aligning Perception with Language Models
2023-03	PaLM-E	Google	PaLM-E: An Embodied Multimodal Language Model
2023-03	GPT 4	OpenAI	GPT-4 Technical Report
2023-04	Pythia	EleutherAI et al.	Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling	ICML
2023-05	Dromedary	CMU et al.	Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
2023-05	PaLM 2	Google	PaLM 2 Technical Report
2023-05	RWKV	Bo Peng	RWKV: Reinventing RNNs for the Transformer Era
2024-02	Microsoft		The-Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Others

Title	Venue	Date	Code	Demo
Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs	arXiv	2023-11-24	Github	-
Can Large Pre-trained Models Help Vision Models on Perception Tasks?	arXiv	2023-06-01	Coming soon	-
Contextual Object Detection with Multimodal Large Language Models	arXiv	2023-05-29	Github	Demo
Generating Images with Multimodal Language Models	arXiv	2023-05-26	Github	-
On Evaluating Adversarial Robustness of Large Vision-Language Models	arXiv	2023-05-26	Github	-
Evaluating Object Hallucination in Large Vision-Language Models	arXiv	2023-05-17	Github	-
Grounding Language Models to Images for Multimodal Inputs and Outputs	ICML	2023-01-31	Github	Demo

---

Awesome Datasets

Datasets of Pre-Training for Alignment

Name	Paper	Type	Modalities
MS-COCO	Microsoft COCO: Common Objects in Context	Caption	Image-Text
SBU Captions	Im2Text: Describing Images Using 1 Million Captioned Photographs	Caption	Image-Text
Conceptual Captions	Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning	Caption	Image-Text
LAION-400M	LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs	Caption	Image-Text
VG Captions	Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations	Caption	Image-Text
Flickr30k	Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models	Caption	Image-Text
AI-Caps	AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding	Caption	Image-Text
Wukong Captions	Wukong: A 100 Million Large-scale Chinese Cross-modal Pre-training Benchmark	Caption	Image-Text
Youku-mPLUG	Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks	Caption	Video-Text
MSR-VTT	MSR-VTT: A Large Video Description Dataset for Bridging Video and Language	Caption	Video-Text
Webvid10M	Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval	Caption	Video-Text
WavCaps	WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research	Caption	Audio-Text
AISHELL-1	AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline	ASR	Audio-Text
AISHELL-2	AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale	ASR	Audio-Text
VSDial-CN	X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages	ASR	Image-Audio-Text

Tutorials about LLM

• [Andrej Karpathy] State of GPT video
• [Hyung Won Chung] Instruction finetuning and RLHF lecture Youtube
• [Jason Wei] Scaling, emergence, and reasoning in large language models Slides
• [Susan Zhang] Open Pretrained Transformers Youtube
• [Ameet Deshpande] How Does ChatGPT Work? Slides
• [Yao Fu] 预训练，指令微调，对齐，专业化：论大语言模型能力的来源 Bilibili
• [Hung-yi Lee] ChatGPT 原理剖析 Youtube
• [Jay Mody] GPT in 60 Lines of NumPy Link
• [ICML 2022] Welcome to the "Big Model" Era: Techniques and Systems to Train and Serve Bigger Models Link
• [NeurIPS 2022] Foundational Robustness of Foundation Models Link
• [Andrej Karpathy] Let's build GPT: from scratch, in code, spelled out. Video|Code
• [DAIR.AI] Prompt Engineering Guide Link
• [邱锡鹏] 大型语言模型的能力分析与应用 Slides | Video
• [Philipp Schmid] Fine-tune FLAN-T5 XL/XXL using DeepSpeed & Hugging Face Transformers Link
• [HuggingFace] Illustrating Reinforcement Learning from Human Feedback (RLHF) Link
• [HuggingFace] What Makes a Dialog Agent Useful? Link
• [张俊林]通向AGI之路：大型语言模型(LLM)技术精要 Link
• [大师兄]ChatGPT/InstructGPT详解 Link
• [HeptaAI]ChatGPT内核：InstructGPT，基于反馈指令的PPO强化学习 Link
• [Yao Fu] How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources Link
• [Stephen Wolfram] What Is ChatGPT Doing … and Why Does It Work? Link
• [Jingfeng Yang] Why did all of the public reproduction of GPT-3 fail? Link
• [Hung-yi Lee] ChatGPT (可能)是怎麼煉成的 - GPT 社會化的過程 Video
• [Keyvan Kambakhsh] Pure Rust implementation of a minimal Generative Pretrained Transformer code

Open Source LLM

• LLaMA2 - A revolutionary version of llama , 70 - 13 - 7 -billion-parameter large language model. LLaMA2 HF - TheBloke/Llama-2-13B-GPTQ
• LLaMA - A foundational, 65-billion-parameter large language model. LLaMA.cpp Lit-LLaMA
  • Alpaca - A model fine-tuned from the LLaMA 7B model on 52K instruction-following demonstrations. Alpaca.cpp Alpaca-LoRA
  • Flan-Alpaca - Instruction Tuning from Humans and Machines.
  • Baize - Baize is an open-source chat model trained with LoRA. It uses 100k dialogs generated by letting ChatGPT chat with itself.
  • Cabrita - A portuguese finetuned instruction LLaMA.
  • Vicuna - An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality.
  • Vicuna - An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality.
  • Llama-X - Open Academic Research on Improving LLaMA to SOTA LLM.
  • Chinese-Vicuna - A Chinese Instruction-following LLaMA-based Model.
  • GPTQ-for-LLaMA - 4 bits quantization of LLaMA using GPTQ.
  • GPT4All - Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa.
  • Koala - A Dialogue Model for Academic Research
  • BELLE - Be Everyone's Large Language model Engine
  • StackLLaMA - A hands-on guide to train LLaMA with RLHF.
  • RedPajama - An Open Source Recipe to Reproduce LLaMA training dataset.
  • Chimera - Latin Phoenix.
  • WizardLM|WizardCoder - Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder.
  • CaMA - a Chinese-English Bilingual LLaMA Model.
  • Orca - Microsoft's finetuned LLaMA model that reportedly matches GPT3.5, finetuned against 5M of data, ChatGPT, and GPT4
  • BayLing - an English/Chinese LLM equipped with advanced language alignment, showing superior capability in English/Chinese generation, instruction following and multi-turn interaction.
  • UltraLM - Large-scale, Informative, and Diverse Multi-round Chat Models.
  • Guanaco - QLoRA tuned LLaMA
• BLOOM - BigScience Large Open-science Open-access Multilingual Language Model BLOOM-LoRA
  • BLOOMZ&mT0 - a family of models capable of following human instructions in dozens of languages zero-shot.
  • Phoenix
• T5 - Text-to-Text Transfer Transformer
  • T0 - Multitask Prompted Training Enables Zero-Shot Task Generalization
• OPT - Open Pre-trained Transformer Language Models.
• UL2 - a unified framework for pretraining models that are universally effective across datasets and setups.
• GLM- GLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on various natural language understanding and generation tasks.
  • ChatGLM-6B - ChatGLM-6B 是一个开源的、支持中英双语的对话语言模型，基于 General Language Model (GLM) 架构，具有 62 亿参数.
  • ChatGLM2-6B - An Open Bilingual Chat LLM | 开源双语对话语言模型
• RWKV - Parallelizable RNN with Transformer-level LLM Performance.
  • ChatRWKV - ChatRWKV is like ChatGPT but powered by my RWKV (100% RNN) language model.
• StableLM - Stability AI Language Models.
• YaLM - a GPT-like neural network for generating and processing text. It can be used freely by developers and researchers from all over the world.
• GPT-Neo - An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library.
• GPT-J - A 6 billion parameter, autoregressive text generation model trained on The Pile.
  • Dolly - a cheap-to-build LLM that exhibits a surprising degree of the instruction following capabilities exhibited by ChatGPT.
• Pythia - Interpreting Autoregressive Transformers Across Time and Scale
• Dolly 2.0 - the first open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use.
• OpenFlamingo - an open-source reproduction of DeepMind's Flamingo model.
• Cerebras-GPT - A Family of Open, Compute-efficient, Large Language Models.
• GALACTICA - The GALACTICA models are trained on a large-scale scientific corpus.
  • GALPACA - GALACTICA 30B fine-tuned on the Alpaca dataset.
• Palmyra - Palmyra Base was primarily pre-trained with English text.
• Camel - a state-of-the-art instruction-following large language model designed to deliver exceptional performance and versatility.
• h2oGPT
• PanGu-α - PanGu-α is a 200B parameter autoregressive pretrained Chinese language model develped by Huawei Noah's Ark Lab, MindSpore Team and Peng Cheng Laboratory.
• MOSS - MOSS是一个支持中英双语和多种插件的开源对话语言模型.
• Open-Assistant - a project meant to give everyone access to a great chat based large language model.
  • HuggingChat - Powered by Open Assistant's latest model – the best open source chat model right now and @huggingface Inference API.
• StarCoder - Hugging Face LLM for Code
• MPT-7B - Open LLM for commercial use by MosaicML
• Falcon - Falcon LLM is a foundational large language model (LLM) with 40 billion parameters trained on one trillion tokens. TII has now released Falcon LLM – a 40B model.
• XGen - Salesforce open-source LLMs with 8k sequence length.
• baichuan-7B - baichuan-7B 是由百川智能开发的一个开源可商用的大规模预训练语言模型.
• Aquila - 悟道·天鹰语言大模型是首个具备中英双语知识、支持商用许可协议、国内数据合规需求的开源语言大模型。

LLM Training Frameworks

• DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
• Megatron-DeepSpeed - DeepSpeed version of NVIDIA's Megatron-LM that adds additional support for several features such as MoE model training, Curriculum Learning, 3D Parallelism, and others.
• FairScale - FairScale is a PyTorch extension library for high performance and large scale training.
• Megatron-LM - Ongoing research training transformer models at scale.
• Colossal-AI - Making large AI models cheaper, faster, and more accessible.
• BMTrain - Efficient Training for Big Models.
• Mesh Tensorflow - Mesh TensorFlow: Model Parallelism Made Easier.
• maxtext - A simple, performant and scalable Jax LLM!
• Alpa - Alpa is a system for training and serving large-scale neural networks.
• GPT-NeoX - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.

Tools for deploying LLM

• FastChat - A distributed multi-model LLM serving system with web UI and OpenAI-compatible RESTful APIs.
• SkyPilot - Run LLMs and batch jobs on any cloud. Get maximum cost savings, highest GPU availability, and managed execution -- all with a simple interface.
• vLLM - A high-throughput and memory-efficient inference and serving engine for LLMs
• Text Generation Inference - A Rust, Python and gRPC server for text generation inference. Used in production at HuggingFace to power LLMs api-inference widgets.
• Haystack - an open-source NLP framework that allows you to use LLMs and transformer-based models from Hugging Face, OpenAI and Cohere to interact with your own data.
• Sidekick - Data integration platform for LLMs.
• LangChain - Building applications with LLMs through composability
• wechat-chatgpt - Use ChatGPT On Wechat via wechaty
• promptfoo - Test your prompts. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality.
• Agenta - Easily build, version, evaluate and deploy your LLM-powered apps.
• Embedchain - Framework to create ChatGPT like bots over your dataset.

Courses about LLM

• [DeepLearning.AI] ChatGPT Prompt Engineering for Developers Homepage
• [Princeton] Understanding Large Language Models Homepage
• [OpenBMB] 大模型公开课 主页
• [Stanford] CS224N-Lecture 11: Prompting, Instruction Finetuning, and RLHF Slides
• [Stanford] CS324-Large Language Models Homepage
• [Stanford] CS25-Transformers United V2 Homepage
• [Stanford Webinar] GPT-3 & Beyond Video
• [李沐] InstructGPT论文精读 Bilibili Youtube
• [陳縕儂] OpenAI InstructGPT 從人類回饋中學習 ChatGPT 的前身 Youtube
• [李沐] HELM全面语言模型评测 Bilibili
• [李沐] GPT，GPT-2，GPT-3 论文精读 Bilibili Youtube
• [Aston Zhang] Chain of Thought论文 Bilibili Youtube
• [MIT] Introduction to Data-Centric AI Homepage

Datasets of Multimodal Instruction Tuning

Name	Paper	Link	Notes
Video-ChatGPT	Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models	Link	100K high-quality video instruction dataset
MIMIC-IT	MIMIC-IT: Multi-Modal In-Context Instruction Tuning	Coming soon	Multimodal in-context instruction tuning
M3IT	M3IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning	Link	Large-scale, broad-coverage multimodal instruction tuning dataset
LLaVA-Med	LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day	Coming soon	A large-scale, broad-coverage biomedical instruction-following dataset
GPT4Tools	GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction	Link	Tool-related instruction datasets
MULTIS	ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst	Coming soon	Multimodal instruction tuning dataset covering 16 multimodal tasks
DetGPT	DetGPT: Detect What You Need via Reasoning	Link	Instruction-tuning dataset with 5000 images and around 30000 query-answer pairs
PMC-VQA	PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering	Coming soon	Large-scale medical visual question-answering dataset
VideoChat	VideoChat: Chat-Centric Video Understanding	Link	Video-centric multimodal instruction dataset
X-LLM	X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages	Link	Chinese multimodal instruction dataset
OwlEval	mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality	Link	Dataset for evaluation on multiple capabilities
cc-sbu-align	MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models	Link	Multimodal aligned dataset for improving model's usability and generation's fluency
LLaVA-Instruct-150K	Visual Instruction Tuning	Link	Multimodal instruction-following data generated by GPT
MultiInstruct	MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning	-	The first multimodal instruction tuning benchmark dataset

Other useful resources

• Mistral - Mistral-7B-v0.1 is a small, yet powerful model adaptable to many use-cases including code and 8k sequence length. Apache 2.0 licence.
• Mixtral 8x7B - a high-quality sparse mixture of experts model (SMoE) with open weights.
• AutoGPT - an experimental open-source application showcasing the capabilities of the GPT-4 language model.
• OpenAGI - When LLM Meets Domain Experts.
• HuggingGPT - Solving AI Tasks with ChatGPT and its Friends in HuggingFace.
• EasyEdit - An easy-to-use framework to edit large language models.
• chatgpt-shroud - A Chrome extension for OpenAI's ChatGPT, enhancing user privacy by enabling easy hiding and unhiding of chat history. Ideal for privacy during screen shares.
• Arize-Phoenix - Open-source tool for ML observability that runs in your notebook environment. Monitor and fine tune LLM, CV and Tabular Models.
• Emergent Mind - The latest AI news, curated & explained by GPT-4.
• ShareGPT - Share your wildest ChatGPT conversations with one click.
• Major LLMs + Data Availability
• 500+ Best AI Tools
• Cohere Summarize Beta - Introducing Cohere Summarize Beta: A New Endpoint for Text Summarization
• chatgpt-wrapper - ChatGPT Wrapper is an open-source unofficial Python API and CLI that lets you interact with ChatGPT.
• Open-evals - A framework extend openai's Evals for different language model.
• Cursor - Write, edit, and chat about your code with a powerful AI.

Prompting libraries & tools

• YiVal — Evaluate and Evolve: YiVal is an open-source GenAI-Ops tool for tuning and evaluating prompts, configurations, and model parameters using customizable datasets, evaluation methods, and improvement strategies.
• Guidance — A handy looking Python library from Microsoft that uses Handlebars templating to interleave generation, prompting, and logical control.
• LangChain — A popular Python/JavaScript library for chaining sequences of language model prompts.
• FLAML (A Fast Library for Automated Machine Learning & Tuning): A Python library for automating selection of models, hyperparameters, and other tunable choices.
• Chainlit — A Python library for making chatbot interfaces.
• Guardrails.ai — A Python library for validating outputs and retrying failures. Still in alpha, so expect sharp edges and bugs.
• Semantic Kernel — A Python/C#/Java library from Microsoft that supports prompt templating, function chaining, vectorized memory, and intelligent planning.
• Prompttools — Open-source Python tools for testing and evaluating models, vector DBs, and prompts.
• Outlines — A Python library that provides a domain-specific language to simplify prompting and constrain generation.
• Promptify — A small Python library for using language models to perform NLP tasks.
• Scale Spellbook — A paid product for building, comparing, and shipping language model apps.
• PromptPerfect — A paid product for testing and improving prompts.
• Weights & Biases — A paid product for tracking model training and prompt engineering experiments.
• OpenAI Evals — An open-source library for evaluating task performance of language models and prompts.
• LlamaIndex — A Python library for augmenting LLM apps with data.
• Arthur Shield — A paid product for detecting toxicity, hallucination, prompt injection, etc.
• LMQL — A programming language for LLM interaction with support for typed prompting, control flow, constraints, and tools.
• ModelFusion - A TypeScript library for building apps with LLMs and other ML models (speech-to-text, text-to-speech, image generation).
• Flappy — Production-Ready LLM Agent SDK for Every Developer.
• GPTRouter - GPTRouter is an open source LLM API Gateway that offers a universal API for 30+ LLMs, vision, and image models, with smart fallbacks based on uptime and latency, automatic retries, and streaming. Stay operational even when OpenAI is down

Datasets of In-Context Learning

Name	Paper	Link	Notes
MIMIC-IT	MIMIC-IT: Multi-Modal In-Context Instruction Tuning	Coming soon	Multimodal in-context instruction dataset

Datasets of Multimodal Chain-of-Thought

Name	Paper	Link	Notes
EgoCOT	EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought	Coming soon	Large-scale embodied planning dataset
VIP	Let’s Think Frame by Frame: Evaluating Video Chain of Thought with Video Infilling and Prediction	Coming soon	An inference-time dataset that can be used to evaluate VideoCOT
ScienceQA	Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering	Link	Large-scale multi-choice dataset, featuring multimodal science questions and diverse domains

Practical Guide for Data

Pretraining data

• RedPajama, 2023. Repo
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling, Arxiv 2020. Paper
• How does the pre-training objective affect what large language models learn about linguistic properties?, ACL 2022. Paper
• Scaling laws for neural language models, 2020. Paper
• Data-centric artificial intelligence: A survey, 2023. Paper
• How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources, 2022. Blog

Finetuning data

• Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach, EMNLP 2019. Paper
• Language Models are Few-Shot Learners, NIPS 2020. Paper
• Does Synthetic Data Generation of LLMs Help Clinical Text Mining? Arxiv 2023 Paper

Test data/user data

• Shortcut learning of large language models in natural language understanding: A survey, Arxiv 2023. Paper
• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective Arxiv, 2023. Paper
• SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems Arxiv 2019. Paper

Practical Guide for NLP Tasks

We build a decision flow for choosing LLMs or fine-tuned models~\protect\footnotemark for user's NLP applications. The decision flow helps users assess whether their downstream NLP applications at hand meet specific conditions and, based on that evaluation, determine whether LLMs or fine-tuned models are the most suitable choice for their applications.

Traditional NLU tasks

• A benchmark for toxic comment classification on civil comments dataset Arxiv 2023 Paper
• Is chatgpt a general-purpose natural language processing task solver? Arxiv 2023Paper
• Benchmarking large language models for news summarization Arxiv 2022 Paper

Generation tasks

• News summarization and evaluation in the era of gpt-3 Arxiv 2022 Paper
• Is chatgpt a good translator? yes with gpt-4 as the engine Arxiv 2023 Paper
• Multilingual machine translation systems from Microsoft for WMT21 shared task, WMT2021 Paper
• Can ChatGPT understand too? a comparative study on chatgpt and fine-tuned bert, Arxiv 2023, Paper

Knowledge-intensive tasks

• Measuring massive multitask language understanding, ICLR 2021 Paper
• Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, Arxiv 2022 Paper
• Inverse scaling prize, 2022 Link
• Atlas: Few-shot Learning with Retrieval Augmented Language Models, Arxiv 2022 Paper
• Large Language Models Encode Clinical Knowledge, Arxiv 2022 Paper

Abilities with Scaling

• Training Compute-Optimal Large Language Models, NeurIPS 2022 Paper
• Scaling Laws for Neural Language Models, Arxiv 2020 Paper
• Solving math word problems with process- and outcome-based feedback, Arxiv 2022 Paper
• Chain of thought prompting elicits reasoning in large language models, NeurIPS 2022 Paper
• Emergent abilities of large language models, TMLR 2022 Paper
• Inverse scaling can become U-shaped, Arxiv 2022 Paper
• Towards Reasoning in Large Language Models: A Survey, Arxiv 2022 Paper

Specific tasks

• Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks, Arixv 2022 Paper
• PaLI: A Jointly-Scaled Multilingual Language-Image Model, Arxiv 2022 Paper
• AugGPT: Leveraging ChatGPT for Text Data Augmentation, Arxiv 2023 Paper
• Is gpt-3 a good data annotator?, Arxiv 2022 Paper
• Want To Reduce Labeling Cost? GPT-3 Can Help, EMNLP findings 2021 Paper
• GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation, EMNLP findings 2021 Paper
• LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability, Arxiv 2023 Paper
• ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks, Arxiv 2023 Paper
• G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment, Arxiv 2023 Paper
• GPTScore: Evaluate as You Desire, Arxiv 2023 Paper
• Large Language Models Are State-of-the-Art Evaluators of Translation Quality, Arxiv 2023 Paper
• Is ChatGPT a Good NLG Evaluator? A Preliminary Study, Arxiv 2023 Paper
• GPT4GEO: How a Language Model Sees the World's Geography, NeurIPSW 2023 Paper, Code

Real-World ''Tasks''

• Sparks of Artificial General Intelligence: Early experiments with GPT-4, Arxiv 2023 Paper

Efficiency

1. Cost

• Openai’s gpt-3 language model: A technical overview, 2020. Blog Post
• Measuring the carbon intensity of ai in cloud instances, FaccT 2022. Paper
• In AI, is bigger always better?, Nature Article 2023. Article
• Language Models are Few-Shot Learners, NeurIPS 2020. Paper
• Pricing, OpenAI. Blog Post

2. Latency

• HELM: Holistic evaluation of language models, Arxiv 2022. Paper

3. Parameter-Efficient Fine-Tuning

• LoRA: Low-Rank Adaptation of Large Language Models, Arxiv 2021. Paper
• Prefix-Tuning: Optimizing Continuous Prompts for Generation, ACL 2021. Paper
• P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks, ACL 2022. Paper
• P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks, Arxiv 2022. Paper

4. Pretraining System

• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, Arxiv 2019. Paper
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, Arxiv 2019. Paper
• Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM, Arxiv 2021. Paper
• Reducing Activation Recomputation in Large Transformer Models, Arxiv 2021. Paper

RLHFdataset

• HH-RLHF
  • Ben Mann, Deep Ganguli
  • Keyword: Human preference dataset, Red teaming data, machine-written
  • Task: Open-source dataset for human preference data about helpfulness and harmlessness
• Stanford Human Preferences Dataset(SHP)
  • Ethayarajh, Kawin and Zhang, Heidi and Wang, Yizhong and Jurafsky, Dan
  • Keyword: Naturally occurring and human-written dataset,18 different subject areas
  • Task: Intended to be used for training RLHF reward models
• PromptSource
  • Stephen H. Bach, Victor Sanh, Zheng-Xin Yong et al.
  • Keyword: Prompted English datasets, Mapping a data example into natural language
  • Task: Toolkit for creating, Sharing and using natural language prompts
• Structured Knowledge Grounding(SKG) Resources Collections
  • Tianbao Xie, Chen Henry Wu, Peng Shi et al.
  • Keyword: Structured Knowledge Grounding
  • Task: Collection of datasets are related to structured knowledge grounding
• The Flan Collection
  • Longpre Shayne, Hou Le, Vu Tu et al.
  • Task: Collection compiles datasets from Flan 2021, P3, Super-Natural Instructions
• rlhf-reward-datasets
  • Yiting Xie
  • Keyword: Machine-written dataset
• webgpt_comparisons
  • OpenAI
  • Keyword: Human-written dataset, Long form question answering
  • Task: Train a long form question answering model to align with human preferences
• summarize_from_feedback
  • OpenAI
  • Keyword: Human-written dataset, summarization
  • Task: Train a summarization model to align with human preferences
• Dahoas/synthetic-instruct-gptj-pairwise
  • Dahoas
  • Keyword: Human-written dataset, synthetic dataset
• Stable Alignment - Alignment Learning in Social Games
  • Ruibo Liu, Ruixin (Ray) Yang, Qiang Peng
  • Keyword: Interaction data used for alignment training, Run in Sandbox
  • Task: Train on the recorded interaction data in simulated social games
• LIMA
  • Meta AI
  • Keyword: without any RLHF, few carefully curated prompts and responses
  • Task: Dataset used for training the LIMA model

Test data/user data

• Shortcut learning of large language models in natural language understanding: A survey, Arxiv 2023. Paper
• On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective Arxiv, 2023. Paper
• SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems Arxiv 2019. Paper

Star History