The hottest Datasets Substack posts right now

The post shares a categorized list of resources for learning about Reinforcement Learning from Human Feedback (RLHF) in 2024.
The resources include videos, research talks, code, models, datasets, evaluations, blog posts, and other related materials.
The aim is to provide a variety of learning tools for individuals with different learning styles interested in going deeper into RLHF.

Reward models provide a unique way to assess language models without relying on traditional prompting and computation limits.
Constructing comparisons with reward models helps identify biases and viewpoints, aiding in understanding language model representations.
Generative reward models offer a simple way to classify preferences in tasks like LLM evaluation, providing clarity and performance benefits in the RL setting.

Multi-modal models are advancing to complement information processing capabilities by incorporating diverse inputs and outputs.
Unified IO 2 introduces a novel autoregressive multimodal model capable of generating and understanding images, text, audio, and action through shared semantic space processing.
LLaVA-RLHF explores new factually augmented RLHF techniques and datasets to bridge misalignment between different modalities and enhance multimodal models.

Well-made datasets are considered works of art
Current datasets are often exploitative of artists for creative work
Artist datasets provide new opportunities for creativity and income, while offering a more intentional and ethical approach to AI art

LLaMA models of size 13B or above might be better than random chance at evaluating credit card approvals.
Smaller LLaMA models (7B) didn't show improvement over random chance.
Instruction-finetuning didn't significantly enhance model performance.

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OpenGPT is a framework for producing domain-specific language models.
NHS-LLM is a conversational model for healthcare created using OpenGPT.
Creating instruction-based datasets and fine-tuning models are crucial steps in building large language models for healthcare.

Benchmark datasets are crucial in ML literature, providing a standard for evaluating new methods and influencing research directions.
In learning-to-rank, the Yahoo and Microsoft datasets are prominent, with Yahoo dataset being widely used in notable papers.
When writing a paper using benchmark datasets, researchers must choose ML algorithms, consider user behavior, generate initial rankings, and evaluate performance with metrics like NDCG.

The talk about the performance of Domain-Wall Encoding for Quantum Annealing provides free access to datasets and code through a downloadable link.
Those interested in course certification are encouraged to explore the paper and possibly challenge themselves by using it for practical work.
Attendees of the session have the opportunity to ask questions directly to Nick, enhancing the learning experience and understanding of the topic.