Merge pull request Hannibal046#113 from Nathancgy/main

Added conference for milestone papers
emanbuc · Mar 18, 2024 · b9bfa24 · b9bfa24
2 parents 3bfb29f + 259e37e
commit b9bfa24
Showing 1 changed file with 5 additions and 6 deletions.
diff --git a/README.md b/README.md
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 | 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)                                                                                              |![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)|
+| 2022-05 |         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)        |
@@ -71,23 +71,22 @@
 | 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)                                                               |![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)|
+| 2023-01 | 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)|![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 | 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)|![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 | 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%5D%5D%3E)|<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)|
 
 
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 ## 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: