Recent Readings for Basic Topics of Foundation Models (since 2022) (Index of Posts):

No. Read Date Title and Information We Read @
1 2022, Dec, 3 RLHF + InstructGPT 2022-W6
2 2022, Dec, 1 Stable diffusion + DreamBooth + LoRA 2022-W5
3 2022, Oct, 1 Emergent Abilities of LLM + ICLR 2022-W4

Here is a detailed list of posts!

[1]: RLHF + InstructGPT

RL AGI language model Human Alignment
Papers Paper URL Abstract
Training language models to follow instructions with human feedback URL “further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT.”
Deep reinforcement learning from human preferences URL “explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function”

[2]: Stable diffusion + DreamBooth + LoRA

Diffusion Image synthesis Efficiency

Stable diffusion

  • URL
  • “High-Resolution Image Synthesis with Latent Diffusion Models”

DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

  • URL
  • “personalization” of text-to-image diffusion models. Given as input just a few images of a subject, we fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. .”

LoRA: Low-Rank Adaptation of Large Language Models

  • URL
  • “propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times.”

An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

  • Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, Daniel Cohen-Or
  • Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new “words” in the embedding space of a frozen text-to-image model. These “words” can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts. We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks.

[3]: Emergent Abilities of LLM + ICLR

language model

Emergent Abilities of Large Language Models

  • URL
  • “an ability to be emergent if it is not present in smaller models but is present in larger models. Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models.”

Language Models are Few-Shot Learners

  • URL
  • “GPT-3, 175B autoregerssive LLM; show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.”

On the Opportunities and Risks of Foundation Models

  • URL
  • ” a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations).”

The Power of Scale for Parameter-Efficient Prompt Tuning

  • Brian Lester, Rami Al-Rfou, Noah Constant
  • In this work, we explore “prompt tuning”, a simple yet effective mechanism for learning “soft prompts” to condition frozen language models to perform specific downstream tasks. Unlike the discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signal from any number of labeled examples. Our end-to-end learned approach outperforms GPT-3’s “few-shot” learning by a large margin. More remarkably, through ablations on model size using T5, we show that prompt tuning becomes more competitive with scale: as models exceed billions of parameters, our method “closes the gap” and matches the strong performance of model tuning (where all model weights are tuned). This finding is especially relevant in that large models are costly to share and serve, and the ability to reuse one frozen model for multiple downstream tasks can ease this burden. Our method can be seen as a simplification of the recently proposed “prefix tuning” of Li and Liang (2021), and we provide a comparison to this and other similar approaches. Finally, we show that conditioning a frozen model with soft prompts confers benefits in robustness to domain transfer, as compared to full model tuning.

Here is a name list of posts!

RLHF + InstructGPT

less than 1 minute read

Papers Paper URL Abstract Training language models to follow instructions with human feedback URL ...

Emergent Abilities of LLM + ICLR

1 minute read

Emergent Abilities of Large Language Models URL “an ability to be emergent if it is not present in smaller models but is present in larger models. Thus...