{"id":31688386,"url":"https://github.com/rraghavkaushik/nlp-learning-resources","last_synced_at":"2025-10-08T10:55:41.076Z","repository":{"id":315757277,"uuid":"1007056411","full_name":"rraghavkaushik/NLP-Learning-Resources","owner":"rraghavkaushik","description":"List of latest papers and blogs for NLP","archived":false,"fork":false,"pushed_at":"2025-10-05T09:20:48.000Z","size":14,"stargazers_count":7,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-05T11:32:17.282Z","etag":null,"topics":["llm-papers","llms","mechanistic-interpretability","mlsys","natural-language-processing","nlp-learning-resources","nlp-papers","reinforcement-learning","rlhf","scaling-laws","transformers"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rraghavkaushik.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-23T11:53:24.000Z","updated_at":"2025-10-05T09:22:01.000Z","dependencies_parsed_at":"2025-09-20T15:49:47.821Z","dependency_job_id":null,"html_url":"https://github.com/rraghavkaushik/NLP-Learning-Resources","commit_stats":null,"previous_names":["rraghavkaushik/nlp-learning-resources"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rraghavkaushik/NLP-Learning-Resources","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rraghavkaushik%2FNLP-Learning-Resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rraghavkaushik%2FNLP-Learning-Resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rraghavkaushik%2FNLP-Learning-Resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rraghavkaushik%2FNLP-Learning-Resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rraghavkaushik","download_url":"https://codeload.github.com/rraghavkaushik/NLP-Learning-Resources/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rraghavkaushik%2FNLP-Learning-Resources/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278931662,"owners_count":26070788,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["llm-papers","llms","mechanistic-interpretability","mlsys","natural-language-processing","nlp-learning-resources","nlp-papers","reinforcement-learning","rlhf","scaling-laws","transformers"],"created_at":"2025-10-08T10:55:38.801Z","updated_at":"2025-10-08T10:55:41.070Z","avatar_url":"https://github.com/rraghavkaushik.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Learning-Resources\n\nA compilation of resources for keeping up with the latest trends in NLP.\n\n\u003e **Note:** This resource list is a work in progress. More papers and topics will be added regularly. Contributions and suggestions are welcome!\n\n## Some Fundamental Transformers\n\n1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding - https://arxiv.org/abs/1810.04805\n2. GPT1 - https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf\n3. GPT2 - https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf\n4. T5: https://arxiv.org/abs/1910.10683\n5. XLNet - https://arxiv.org/pdf/1906.08237\n6. RoBERTa: https://arxiv.org/abs/1907.11692\n7. ALBERT: https://arxiv.org/abs/1909.11942\n8. LongFormer - https://arxiv.org/abs/2004.05150\n\n## Papers for understanding fundamentals\n\n1. Attention is all you need - https://arxiv.org/pdf/1706.03762\n2. Memory Is All You Need - https://arxiv.org/pdf/2406.08413\n3. Language Models are Few-Shot Learners - https://arxiv.org/abs/2005.14165\n\n## Reinforcement Learning for LLMs\n\nBasics of RL - OpenAI - https://spinningup.openai.com/en/latest/spinningup/rl_intro.html\n\nReinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism - https://arxiv.org/abs/2305.18438\n\nInstructGPT - https://arxiv.org/abs/2203.02155\n\nDPO:\n\n1. DPO paper: https://arxiv.org/pdf/2305.18290\n2. Blog - Math behind DPO - https://www.tylerromero.com/posts/2024-04-dpo/\n\nPPO:\n\n1. Proximal Policy Optimization Algorithms - https://arxiv.org/pdf/1707.06347\n2. PPO Docs OpenAI - https://spinningup.openai.com/en/latest/algorithms/ppo.html\n\nGRPO: \n\n1. DeepSeekMath - https://arxiv.org/abs/2402.03300\n2. Blog - GRPO Explained - https://aipapersacademy.com/deepseekmath-grpo/\n3. DeepSeek-R1 - https://arxiv.org/pdf/2501.12948\n\n## Mechanistic Interpretability\n\n1. Basic Mech Interp Essay - https://www.transformer-circuits.pub/2022/mech-interp-essay\n2. Toy Neural Nets with low dimensional inputs - https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/\n3. Mechanistic Interpretability for AI Safety Review - https://arxiv.org/abs/2404.14082\n4. A Mathematical Framework for Transformer Circuits - https://transformer-circuits.pub/2021/framework/index.html\n5. Circuit Tracing: Revealing Computational Graphs in Language Models - https://transformer-circuits.pub/2025/attribution-graphs/methods.html#evaluating-model\n\n## Scaling Laws\n\n1. Scaling Laws for Neural Language Models - https://arxiv.org/pdf/2001.08361\n2. Scaling Laws for Autoregressive Generative Modeling - https://arxiv.org/pdf/2010.14701\n\n## MLSys\n\n1. Matrix multiplication - Nvidia Blog - https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html\n2. Understanding GPU Performance - Nvidia Blog - https://docs.nvidia.com/deeplearning/performance/dl-performance-gpu-background/index.html#gpu-arch__fig2\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frraghavkaushik%2Fnlp-learning-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frraghavkaushik%2Fnlp-learning-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frraghavkaushik%2Fnlp-learning-resources/lists"}