## Milestone Papers

|  Date  |       keywords       |    Institute    | Paper                                                                                                                                                                               | Publication |
| :-----: | :------------------: | :--------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :---------: |
| 2017-06 |     Transformers     |      Google      | [Attention Is All You Need](https://arxiv.org/pdf/1706.03762.pdf)                                                                                                                      |   NeurIPS<br>  ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F204e3073870fae3d05bcbc2f6a8e263d9b72e776%3Ffields%3DcitationCount&query=%24.citationCount&label=citation) |
| 2018-06 |       GPT 1.0       |      OpenAI      | [Improving Language Understanding by Generative Pre-Training](https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf)                                                 |  ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fcd18800a0fe0b668a1cc19f2ec95b5003d0a5035%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)          |
| 2018-10 |         BERT         |      Google      | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423.pdf)                                                              |    NAACL <br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fdf2b0e26d0599ce3e70df8a9da02e51594e0e992%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)    |
| 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)              |     ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F9405cc0d6169988371b2755e573cc28650d14dfe%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)       |
| 2019-09 |     Megatron-LM     |      NVIDIA      | [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf)                                                          |  ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8323c591e119eb09b28b29fd6c7bc76bd889df7a%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)          |
| 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)                                                           |    JMLR<br>  ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F3cfb319689f06bf04c2e28399361f414ca32c4b3%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)  |
| 2019-10 |          ZeRO          |      Microsoft      | [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/pdf/1910.02054.pdf)                                                           |    SC<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F00c957711b12468cb38424caccdf5291bb354033%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)   |
| 2020-01 |     Scaling Law     |      OpenAI      | [Scaling Laws for Neural Language Models](https://arxiv.org/pdf/2001.08361.pdf)                                                                                                        |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe6c561d02500b2596a230b341a8eb8b921ca5bf2%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2020-05 |       GPT 3.0       |      OpenAI      | [Language models are few-shot learners](https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf)                                                             |   NeurIPS <br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F6b85b63579a916f705a8e10a49bd8d849d91b1fc%3Ffields%3DcitationCount&query=%24.citationCount&label=citation) |
| 2021-01 | Switch Transformers |      Google      | [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/pdf/2101.03961.pdf)                                                   |    JMLR<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ffdacf2a732f55befdc410ea927091cad3b791f13%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)   |
| 2021-08 |        Codex        |      OpenAI      | [Evaluating Large Language Models Trained on Code](https://arxiv.org/pdf/2107.03374.pdf)                                                                                               |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Facbdbf49f9bc3f151b93d9ca9a06009f4f6eb269%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2021-08 |  Foundation Models  |     Stanford     | [On the Opportunities and Risks of Foundation Models](https://arxiv.org/pdf/2108.07258.pdf)                                                                                            |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F4f68e07c6c3173480053fd52391851d6f80d651b%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2021-09 |         FLAN         |      Google      | [Finetuned Language Models are Zero-Shot Learners](https://openreview.net/forum?id=gEZrGCozdqR)                                                                                        |    ICLR <br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fff0b2681d7b05e16c46dfb71d980cc2f605907cd%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2021-10 |         T0         |      HuggingFace et al.      | [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207)                                                                                        |    ICLR <br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F17dd3555fd1ccf1141cf984347fa1b3fd6b009ca%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2021-12 |         GLaM         |      Google      | [GLaM: Efficient Scaling of Language Models with Mixture-of-Experts](https://arxiv.org/pdf/2112.06905.pdf)                                                                             |    ICML<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F80d0116d77beeded0c23cf48946d9d10d4faee14%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 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)                                                                      |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F2f3efe44083af91cef562c1a3451eee2f8601d22%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 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)             |    ICML<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F002c256d30d6be4b23d365a8de8ae0e67e4c9641%3Ffields%3DcitationCount&query=%24.citationCount&label=citation) |
| 2021-12 |        Gopher        |     DeepMind     | [Scaling Language Models: Methods, Analysis &amp; Insights from Training Gopher](https://arxiv.org/pdf/2112.11446.pdf)                                                                 |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F68f141724814839d556a989646194be88641b143%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-01 |         COT         |      Google      | [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://arxiv.org/pdf/2201.11903.pdf)                                                                          |   NeurIPS<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F1b6e810ce0afd0dd093f789d2b2742d047e316d5%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-01 |        LaMDA        |      Google      | [LaMDA: Language Models for Dialog Applications](https://arxiv.org/pdf/2201.08239.pdf)                                                                                                 |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fb3848d32f7294ec708627897833c4097eb4d8778%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-01 |        Minerva      |      Google      | [Solving Quantitative Reasoning Problems with Language Models](https://arxiv.org/abs/2206.14858)                                                                                                 |   NeurIPS<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fab0e3d3e4d42369de5933a3b4c237780b41c0d77%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)     |
| 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)                                        | ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7cbc2a7843411a1768ab762930707af0a3c33a19%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-03 |     InstructGPT     |      OpenAI      | [Training language models to follow instructions with human feedback](https://arxiv.org/pdf/2203.02155.pdf)                                                                            |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fd766bffc357127e0dc86dd69561d5aeb520d6f4c%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-04 |         PaLM         |      Google      | [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/pdf/2204.02311.pdf)                                                                                                  |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F094ff971d6a8b8ff870946c9b3ce5aa173617bfb%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-04 |      Chinchilla      |     DeepMind     | [An empirical analysis of compute-optimal large language model training](https://www.deepmind.com/publications/an-empirical-analysis-of-compute-optimal-large-language-model-training) |   NeurIPS<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fbb0656031cb17adf6bac5fd0fe8d53dd9c291508%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)  |
| 2022-05 |         OPT         |       Meta       | [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/pdf/2205.01068.pdf)                                                                                              |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F13a0d8bb38f739990c8cd65a44061c6534f17221%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-05 |         UL2         |       Google       | [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1)                                                                                              | ICLR<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ff40aeae3e522ada1f6a9f326841b01ef5c8657b6%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-06 |  Emergent Abilities  |      Google      | [Emergent Abilities of Large Language Models](https://openreview.net/pdf?id=yzkSU5zdwD)                                                                                                |    TMLR<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fdac3a172b504f4e33c029655e9befb3386e5f63a%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-06 |      BIG-bench      |      Google      | [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://github.com/google/BIG-bench)                                                    |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F34503c0b6a615124eaf82cb0e4a1dab2866e8980%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-06 |        METALM        |    Microsoft    | [Language Models are General-Purpose Interfaces](https://arxiv.org/pdf/2206.06336.pdf)                                                                                                 |    ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fa8fd9c1625011741f74401ff9bdc1c584e25c86d%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)        |
| 2022-09 |       Sparrow       |     DeepMind     | [Improving alignment of dialogue agents via targeted human judgements](https://arxiv.org/pdf/2209.14375.pdf)                                                                           |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F74eae12620bd1c1393e268bddcb6f129a5025166%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-10 |       Flan-T5/PaLM       |      Google      | [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf)                                                                                                  |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F5484d228bfc50efbac6e86677bc2ec2ee4ede1a6%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-10 |       GLM-130B       |     Tsinghua     | [GLM-130B: An Open Bilingual Pre-trained Model](https://arxiv.org/pdf/2210.02414.pdf)                                                                                                  |    ICLR<br> ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F1d26c947406173145a4665dd7ab255e03494ea28%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-11 |         HELM         |     Stanford     | [Holistic Evaluation of Language Models](https://arxiv.org/pdf/2211.09110.pdf) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F5032c0946ee96ff11a292762f23e6377a6cf2731%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-11 |        BLOOM        |    BigScience    | [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://arxiv.org/pdf/2211.05100.pdf)                                                                                |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F964bd39b546f0f6625ff3b9ef1083f797807ef2e%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-11 |      Galactica      |       Meta       | [Galactica: A Large Language Model for Science](https://arxiv.org/pdf/2211.09085.pdf)                                                                                                  |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F7d645a3fd276918374fd9483fd675c28e46506d1%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2022-12 | OPT-IML |  Meta | [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://arxiv.org/pdf/2212.12017)  | ![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe965e93e76a9e6c4e4863d145b5c007b540d575d%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-01 | Flan 2022 Collection |      Google      | [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://arxiv.org/pdf/2301.13688.pdf)                                                               | ICML<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ff2b0017ddd77fa38760a18145e63553105a1a236%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-02 | LLaMA|Meta|[LLaMA: Open and Efficient Foundation Language Models](https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/)|![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F57e849d0de13ed5f91d086936296721d4ff75a75%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-02 | Kosmos-1|Microsoft|[Language Is Not All You Need: Aligning Perception with Language Models](https://arxiv.org/abs/2302.14045)|![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Ffbfef4723d8c8467d7bd523e1d0b703cce0e0f9c%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-03 | PaLM-E | Google | [PaLM-E: An Embodied Multimodal Language Model](https://palm-e.github.io)| ICML<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F38fe8f324d2162e63a967a9ac6648974fc4c66f3%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-03 | GPT 4 | OpenAI | [GPT-4 Technical Report](https://openai.com/research/gpt-4)|![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F8ca62fdf4c276ea3052dc96dcfd8ee96ca425a48%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-04 | Pythia | EleutherAI et al. | [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://arxiv.org/abs/2304.01373)|ICML<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fbe55e8ec4213868db08f2c3168ae666001bea4b8%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 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)| NeurIPS<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fe01515c6138bc525f7aec30fc85f2adf028d4156%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-05 | PaLM 2 | Google | [PaLM 2 Technical Report](https://ai.google/static/documents/palm2techreport.pdf)|![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Feccee350691708972370b7a12c2a78ad3bddd159%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-05 | RWKV | Bo Peng | [RWKV: Reinventing RNNs for the Transformer Era](https://arxiv.org/abs/2305.13048) |EMNLP<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F026b3396a63ed5772329708b7580d633bb86bec9%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-05 | DPO | Stanford | [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/pdf/2305.18290.pdf) |Neurips<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F0d1c76d45afa012ded7ab741194baf142117c495%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-05 | ToT | Google&Princeton | [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/pdf/2305.10601.pdf) | NeurIPS<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F2f3822eb380b5e753a6d579f31dfc3ec4c4a0820%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-07 | LLaMA 2 | Meta | [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://arxiv.org/pdf/2307.09288.pdf) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F104b0bb1da562d53cbda87aec79ef6a2827d191a%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
|2023-10| Mistral 7B| Mistral |[Mistral 7B](https://arxiv.org/pdf/2310.06825.pdf)|<br>![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fdb633c6b1c286c0386f0078d8a2e6224e03a6227%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2023-12 | Mamba | CMU&Princeton | [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/ftp/arxiv/papers/2312/2312.00752.pdf) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2F432bef8e34014d726c674bc458008ac895297b51%3Ffields%3DcitationCount&query=%24.citationCount&label=citation)|
| 2024-03 | Jamba | AI21 Labs | [Jamba: A Hybrid Transformer-Mamba Language Model](https://arxiv.org/pdf/2403.19887) |![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.semanticscholar.org%2Fgraph%2Fv1%2Fpaper%2Fcbaf689fd9ea9bc939510019d90535d6249b3367%3Ffields%3DcitationCount&query=%24.citationCount&label=citation) |

