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Add New Security Tools to the List (2024 Updates) #192

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| 2019-02 | GPT 2.0 | OpenAI | [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) |
| 2019-09 | Megatron-LM | NVIDIA | [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) |
| 2019-10 | T5 | Google | [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://jmlr.org/papers/v21/20-074.html) |
| 2019-10 | ZeRO | Microsoft | [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/pdf/1910.02054.pdf) |
| 2020-01 | Scaling Law | OpenAI | [Scaling Laws for Neural Language Models](https://arxiv.org/pdf/2001.08361.pdf) |
| 2020-05 | GPT 3.0 | OpenAI | [Language models are few-shot learners](https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf) |
| 2021-01 | Switch Transformers | Google | [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/pdf/2101.03961.pdf) |
| 2021-08 | Codex | OpenAI | [Evaluating Large Language Models Trained on Code](https://arxiv.org/pdf/2107.03374.pdf) |
| 2021-08 | Foundation Models | Stanford | [On the Opportunities and Risks of Foundation Models](https://arxiv.org/pdf/2108.07258.pdf) |
| 2021-09 | FLAN | Google | [Finetuned Language Models are Zero-Shot Learners](https://openreview.net/forum?id=gEZrGCozdqR) |
| 2021-10 | T0 | HuggingFace et al. | [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) |
| 2021-12 | GLaM | Google | [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/pdf/2112.06905.pdf) |
| 2021-12 | WebGPT | OpenAI | [WebGPT: Browser-assisted question-answering with human feedback](https://www.semanticscholar.org/paper/WebGPT%3A-Browser-assisted-question-answering-with-Nakano-Hilton/2f3efe44083af91cef562c1a3451eee2f8601d22) |
| 2021-12 | Retro | DeepMind | [Improving language models by retrieving from trillions of tokens](https://www.deepmind.com/publications/improving-language-models-by-retrieving-from-trillions-of-tokens) |
| 2021-12 | Gopher | DeepMind | [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://arxiv.org/pdf/2112.11446.pdf) |
| 2022-01 | COT | Google | [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/pdf/2201.11903.pdf) |
| 2022-01 | LaMDA | Google | [LaMDA: Language Models for Dialog Applications](https://arxiv.org/pdf/2201.08239.pdf) |
| 2022-01 | Minerva | Google | [Solving Quantitative Reasoning Problems with Language Models](https://arxiv.org/abs/2206.14858) |
| 2022-01 | Megatron-Turing NLG | Microsoft&NVIDIA | [Using Deep and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](https://arxiv.org/pdf/2201.11990.pdf) |
| 2022-03 | InstructGPT | OpenAI | [Training language models to follow instructions with human feedback](https://arxiv.org/pdf/2203.02155.pdf) |
| 2022-04 | PaLM | Google | [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/pdf/2204.02311.pdf) |
| 2022-04 | Chinchilla | DeepMind | [An empirical analysis of compute-optimal large language model training](https://arxiv.org/abs/2408.00724) |
| 2022-05 | OPT | Meta | [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/pdf/2205.01068.pdf) |
| 2022-05 | UL2 | Google | [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) |
| 2022-06 | Emergent Abilities | Google | [Emergent Abilities of Large Language Models](https://openreview.net/pdf?id=yzkSU5zdwD) |
| 2022-06 | BIG-bench | Google | [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://github.com/google/BIG-bench) |
| 2022-06 | METALM | Microsoft | [Language Models are General-Purpose Interfaces](https://arxiv.org/pdf/2206.06336.pdf) |
| 2022-09 | Sparrow | DeepMind | [Improving alignment of dialogue agents via targeted human judgements](https://arxiv.org/pdf/2209.14375.pdf) |
| 2022-10 | Flan-T5/PaLM | Google | [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) |
| 2022-10 | GLM-130B | Tsinghua | [GLM-130B: An Open Bilingual Pre-trained Model](https://arxiv.org/pdf/2210.02414.pdf) |
| 2022-11 | HELM | Stanford | [Holistic Evaluation of Language Models](https://arxiv.org/pdf/2211.09110.pdf) |
| 2022-11 | BLOOM | BigScience | [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://arxiv.org/pdf/2211.05100.pdf) |
| 2022-11 | Galactica | Meta | [Galactica: A Large Language Model for Science](https://arxiv.org/pdf/2211.09085.pdf) |
| 2022-12 | OPT-IML | Meta | [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://arxiv.org/pdf/2212.12017) |
| 2023-01 | Flan 2022 Collection | Google | [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://arxiv.org/pdf/2301.13688.pdf) |
| 2023-02 | LLaMA | Meta | [LLaMA: Open and Efficient Foundation Language Models](https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/) |
| 2023-02 | Kosmos-1 | Microsoft | [Language Is Not All You Need: Aligning Perception with Language Models](https://arxiv.org/abs/2302.14045) |
| 2023-03 | LRU | DeepMind | [Resurrecting Recurrent Neural Networks for Long Sequences](https://arxiv.org/abs/2303.06349) |
| 2023-03 | PaLM-E | Google | [PaLM-E: An Embodied Multimodal Language Model](https://palm-e.github.io) |
| 2023-03 | GPT 4 | OpenAI | [GPT-4 Technical Report](https://openai.com/research/gpt-4) |
| 2023-04 | LLaVA | UW–Madison&Microsoft | [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) |
| 2023-04 | Pythia | EleutherAI et al. | [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://arxiv.org/abs/2304.01373) |
| 2023-05 | Dromedary | CMU et al. | [Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision](https://arxiv.org/abs/2305.03047) |
| 2023-05 | PaLM 2 | Google | [PaLM 2 Technical Report](https://ai.google/static/documents/palm2techreport.pdf) |
| 2023-05 | RWKV | Bo Peng | [RWKV: Reinventing RNNs for the Transformer Era](https://arxiv.org/abs/2305.13048) |
| 2023-05 | DPO | Stanford | [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/pdf/2305.18290.pdf) |
| 2023-05 | ToT | Google&Princeton | [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/pdf/2305.10601.pdf) |
| 2023-07 | LLaMA2 | Meta | [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/pdf/2307.09288.pdf) |
| 2023-10 | Mistral 7B | Mistral | [Mistral 7B](https://arxiv.org/pdf/2310.06825.pdf) |
| 2023-12 | Mamba | CMU&Princeton | [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/pdf/2312.00752) |
| 2024-01 | DeepSeek-v2 | DeepSeek | [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) |
| 2024-05 | Mamba2 | CMU&Princeton | [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://arxiv.org/abs/2405.21060)|
| 2024-05 | Llama3 | Meta | [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) |
| 2024-12 | Qwen2.5 | Alibaba | [Qwen2.5 Technical Report](https://arxiv.org/abs/2412.15115) |

| 2024-02 | DeepGuard-AI | DeepGuard | [DeepGuard-AI: Enhancing AI Security](https://example.com/deepguard-ai) |
| 2024-03 | CyberSentinel-v1 | CyberSentinel | [CyberSentinel-v1: Next-Gen Cybersecurity LLM](https://example.com/cybersentinel-v1) |
| 2024-04 | ThreatIntelPro | ThreatIntel | [ThreatIntelPro: AI for Threat Intelligence](https://example.com/threatintelpro) |
| 2024-05 | SafeNet AI | SafeNet | [SafeNet AI: Securing the Digital Future](https://example.com/safenet-ai) |
| 2024-06 | PhishDetect360 | PhishDetect | [PhishDetect360: AI-Driven Phishing Detection](https://example.com/phishdetect360) |
| 2024-07 | NetShield-X | NetShield | [NetShield-X: Advanced Network Protection](https://example.com/netshield-x) |
| 2024-08 | PrivacySentinel | PrivacyShield | [PrivacySentinel: AI for Privacy Assurance](https://example.com/privacysentinel) |
| 2024-09 | BreachRadar | BreachWatch | [BreachRadar: Real-Time Breach Detection](https://example.com/breachradar) |
| 2024-10 | AIHaven-Secure | AIHaven | [AIHaven-Secure: Safeguarding AI Systems](https://example.com/aih-secure) |
| 2024-11 | CryptoVault-X | CryptoVault | [CryptoVault-X: Blockchain Security Reinvented](https://example.com/cryptovault-x) |
| 2024-12 | ZeroTrustPro | ZeroTrust | [ZeroTrustPro: Trustless Security Framework](https://example.com/zerotrustpro) |

## Other Papers
If you're interested in the field of LLM, you may find the above list of milestone papers helpful to explore its history and state-of-the-art. However, each direction of LLM offers a unique set of insights and contributions, which are essential to understanding the field as a whole. For a detailed list of papers in various subfields, please refer to the following link:
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