Language Modeling Reading List (to Start Your Paper Club)

[ llm learning ] · 6 min read

Some friends and I started a weekly paper club to read and discuss fundamental papers in language modeling. By pooling together our shared knowledge, experience, and questions, we learned more as a group than we could have individually. To encourage others to do the same, here’s the list of papers we covered, and a one-sentence summary for each. I’ll update this list with new papers as we discuss them. (Also, why and how to read papers .)

See an error or have a paper to suggest? Raise a PR or issue here!

• • •

  1. Attention Is All You Need: Query, Key, and Value are all you need* (*Also position embeddings, multiple heads, feed-forward layers, skip-connections, etc.)

  2. GPT: Improving Language Understanding by Generative Pre-Training: Decoder is all you need* (*Also, pre-training + finetuning)

  3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding: Encoder is all you need*. Left-to-right language modeling is NOT all you need. (*Also, pre-training + finetuning)

  4. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer: Encoder-only or decoder-only is NOT all you need, though text-to-text is all you need* (*Also, pre-training + finetuning)

  5. GPT2: Language Models are Unsupervised Multitask Learners: Unsupervised pre-training is all you need?!

  6. GPT3: Language Models are Few-Shot Learners: Unsupervised pre-training + a few* examples is all you need. (*From 5 examples, in Conversational QA, to 50 examples in Winogrande, PhysicalQA, and TriviaQA)

  7. Scaling Laws for Neural Language Models: Larger models trained on lesser data* are what you you need. (*10x more compute should be spent on 5.5x larger model and 1.8x more tokens)

  8. Chinchilla: Training Compute-Optimal Large Language Models: Smaller models trained on more data* are what you need. (*10x more compute should be spent on 3.2x larger model and 3.2x more tokens)

  9. LLaMA: Open and Efficient Foundation Language Models: Smoler models trained longer—on public data—is all you need

  10. InstructGPT: Training language models to follow instructions with human feedback: 40 labelers are all you need* (*Plus supervised fine-tuning, reward modeling, and PPO)

  11. LoRA: Low-Rank Adaptation of Large Language Models: One rank is all you need

  12. QLoRA: Efficient Finetuning of Quantized LLMs: 4-bit is all you need* (*Plus double quantization and paged optimizers)

  13. DPR: Dense Passage Retrieval for Open-Domain Question Answering: Dense embeddings are all you need* (*Also, high precision retrieval)

  14. RAG: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks: Semi-parametric models* are all you need (*Dense vector retrieval as non-parametric component; pre-trained LLM as parametric component)

  15. RETRO: Improving language models by retrieving from trillions of tokens: Retrieving based on input chunks and chunked cross attention are all you need

  16. Internet-augmented language models through few-shot prompting for open-domain question answering: Google Search as retrieval is all you need

  17. HyDE: Precise Zero-Shot Dense Retrieval without Relevance Labels: LLM-generated, hypothetical documents are all you need

  18. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness: For-loops in SRAM are all you need

  19. ALiBi; Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation: Constant bias on the query-key dot-product is all you need* (*Also hyperparameter m and cached Q, K, V representations)

  20. Codex: Evaluating Large Language Models Trained on Code: Finetuning on code is all you need

  21. Layer Normalization: Consistent mean and variance at each layer is all you need

  22. On Layer Normalization in the Transformer Architecture: Pre-layer norm, instead of post-layer norm, is all you need

  23. PPO: Proximal Policy Optimization Algorithms: Clipping your surrogate function is all you need

  24. WizardCoder: Empowering Code Large Language Models with Evol-Instruct: Asking the model to make the question harder is all you need* (*Where do they get the responses to these harder questions though?!)

  25. Llama 2: Open Foundation and Fine-Tuned Chat Models: Iterative finetuning, PPO, rejection sampling, and ghost attention is all you need* (*Also, 27,540 SFT annotations and more than 1 million binary comparison preference data)

  26. RWKV: Reinventing RNNs for the Transformer Era: Linear attention during inference, via RNNs, is what you need

  27. RLAIF - Constitutional AI: Harmlessness from AI Feedback: A natural language constitution* and model feedback on harmlessness is all you need (*16 different variants of harmlessness principles)

  28. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer: Noise in your softmax and expert regularization are all you need

  29. CLIP: Learning Transferable Visual Models From Natural Language Supervision: *A projection layer between text and image embeddings is all you need (*Also, 400 million image-text pairs)

  30. ViT; An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: Flattened 2D patches are all you need

  31. Generative Agents: Interactive Simulacra of Human Behavior: Reflection, memory, and retrieval are all you need

  32. Out-of-Domain Finetuning to Bootstrap Hallucination Detection: Open-source, permissive-use data is what you need

  33. DPO; Direct Preference Optimization: Your Language Model is Secretly a Reward Model: A separate reward model is NOT what you need

  34. Consistency Models: Mapping to how diffusion adds gaussian noise to images is all you need

  35. LCM; Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference: Consistency modeling in latent space is all you need* (*Also, a diffusion model to distill from)

  36. LCM-LoRA: A Universal Stable-Diffusion Acceleration Module: Combining LoRAs is all you need

  37. Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models: Asking the LLM to reflect on retrieved documents is all you need

  38. Emergent Abilities of Large Language Models: The Bitter Lesson is all you need

  39. Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions: The Bellman equation and replay buffers are all you need

  40. Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations: Classification guidelines and the multiple-choice response are all you need

  41. \(\text{REST}^{EM}\); Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models: Synthetic data and a reward function are all you need

  42. Mixture of Experts Explained: Conditional computation and sparsity are all you need

  43. SPIN: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models: Generator and discriminator are all you need.

  44. Self-Instruct: Aligning Language Models with Self-Generated Instructions: 54% valid instruction-input-output tuples is all you need.

  45. Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling: Well documented, publicly available model checkpoints are all you need.

  46. Self-Rewarding Language Models: Asking the model to evaluate itself is all you need.


If you found this useful, please cite this write-up as:

Yan, Ziyou. (Jan 2024). Language Modeling Reading List (to Start Your Paper Club). eugeneyan.com. https://eugeneyan.com/writing/llm-reading-list/.

or

@article{yan2024default,
  title   = {Language Modeling Reading List (to Start Your Paper Club)},
  author  = {Yan, Ziyou},
  journal = {eugeneyan.com},
  year    = {2024},
  month   = {Jan},
  url     = {https://eugeneyan.com/writing/llm-reading-list/}
}

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