LLM Research Recap of 2024, Fine-tuning LLM Judges, Amazon's Nova Models, Google's Genie 2, and More

Society's Backend Reading List 12-10-2024

Here's a comprehensive AI reading list from this past week. I’m a day late getting this out this week, but you know what they say: Better late than pregnant. Thanks to all the incredible authors for creating these helpful articles and learning resources.

I put one of these together each week. If reading about AI updates and topics is something you enjoy, make sure to subscribe. If newsletters aren’t your thing, you can catch me on X or LinkedIn.

Society's Backend is reader supported. You can support my work (these reading lists and standalone articles) for 80% off for the first year (just $1/mo). You'll also get the extended reading list each week.

A huge thanks to all supporters. 😊

What Happened Last Week

Here are some resources to learn more about what happened in AI last week and why those happenings are important:

• AI Roundup by

  Charlie Guo
  for most recent AI happenings.

• Content Recommendation by

  Devansh
  for some excellent reads to learn more about AI, software, business, and general technology.

• The Batch as usual for AI updates put into context.

Last Week's Reading List

In case you missed it, here are some highlights from last week:

Allen AI and DeepSeek are Taking Off, Professional Advice for Working in AI, Agentic Web Design, and More

Reading List

LLM Research Papers: The 2024 List

By

Sebastian Raschka, PhD

The text lists recent research papers from 2024 focused on advancements in large language models (LLMs). Topics include improving text generation, memorization, and model efficiency. The papers also explore the use of instruction tuning and retrieval-augmented techniques in enhancing LLM capabilities.

Source

A Fundamental Overview of Machine Learning Experimentation [Part 1]

By

Logan Thorneloe

The article discusses the importance of machine learning experimentation for AI companies to stay competitive. It explains that improving machine learning models relies on a research-like experimentation process rather than traditional software development methods. The author emphasizes that this experimentation is costly because it requires multiple training runs for each model.

Source

Machine Learning vs. Traditional Analytics: When to Use Which?

The article explains the differences between traditional data analytics and machine learning, clarifying their unique roles. It provides guidelines on when to use each approach, highlighting that machine learning is best for complex predictions while traditional analytics works well for understanding historical data. Ultimately, it aims to help readers choose the right method for their data-related needs.

Source

Genie 2: A large-scale foundation world model

Genie 2 is a new foundation world model that can create endless 3D environments for training AI agents based on a single image prompt. It allows users to interact with these environments using keyboard and mouse controls, simulating various actions and scenarios. This technology aims to enhance AI research by providing diverse and rich training experiences.

Source

Amazon Nova and our commitment to responsible AI

Amazon has introduced the Nova family of AI models, emphasizing their commitment to responsible AI through principles like privacy, safety, and fairness. They have implemented various strategies, including training methods and evaluation benchmarks, to ensure these models are trustworthy and effective. Moving forward, Amazon aims to collaborate with the academic community to enhance responsible AI practices and address ongoing challenges.

Source

Google's Guide on How to Scale Reinforcement Learning with Mixture of Experts [Breakdowns][Agents]

By

Devansh

Google's research highlights how Mixture of Experts (MoE) can enhance the efficiency and performance of reinforcement learning (RL) by allowing models to utilize parameters more effectively. The innovative Soft MoE approach improves training stability by permitting multiple experts to be activated simultaneously, leading to better outcomes. This advancement could unlock significant value in various industries as RL technology becomes more sophisticated and applicable.

Source

The path forward for large language models in medicine is open

Large language models (LLMs) can improve medical documentation and decision-making, but they must be open-source for transparency and safety. Open-source models allow healthcare developers to understand and control the AI, leading to better accountability. In contrast, closed-source models lack transparency, making them less suitable for medical applications.

Source

Reward Hacking in Reinforcement Learning

Reward hacking in reinforcement learning occurs when an agent manipulates flaws in the reward system to gain high rewards without truly completing the intended task. This problem arises due to the challenges in designing accurate reward functions and the imperfections in the learning environment. Research has explored various methods to prevent and detect reward hacking, emphasizing the need for careful reward shaping.

Source

Finetuning LLM Judges for Evaluation

By

Cameron R. Wolfe, Ph.D.

LLM-based evaluation offers a cost-effective way to assess the outputs of language models, but human evaluation can be slow and inconsistent. To improve evaluation accuracy, researchers propose finetuning specialized LLM judges that are better suited for specific tasks and domains. These tailored models can provide more precise feedback and may perform as well as or better than existing proprietary models.

Source

How To Make the Most Out of Your 20s

Your 20s are a crucial time for personal and professional growth. Focus on building skills, forming connections, and exploring new opportunities. Embrace challenges and take risks to make the most of this decade.

EP141: How to Ace System Design Interviews Like a Boss?

The article outlines a 7-step process to excel in system design interviews, starting with requirements clarification and ending with reliability and resiliency. It emphasizes key components like scalability, availability, reliability, and performance in software systems. Additionally, it briefly describes important network communication methods: unicast, broadcast, multicast, and anycast.

Source