The hottest Language Models Substack posts right now

And their main takeaways
Category
Top Technology Topics

GPT-4 is "WEIRD"—what should we do about it?

The Counterfactual • 59 implied HN points • 12 Feb 24
🕹 Technology AI Ethics Machine Learning Data Bias Language Models Cognitive Science
  1. Large Language Models (LLMs) like GPT-4 often reflect the views of people from Western, educated, industrialized, rich, and democratic (WEIRD) cultures. This means they may not accurately represent other cultures or perspectives.
  2. When using LLMs for research, it's important to consider who they are modeling. We should check if the data they were trained on includes a variety of cultures, not just a narrow subset.
  3. To improve LLMs and make them more representative, researchers should focus on creating models that include diverse languages and cultural contexts, and be clear about their limitations.

Neologisms

johan’s substack • 19 implied HN points • 02 Jun 24
🕹 Technology AI Language Models Speculation Machine Learning
  1. Exploring neologisms can reveal insights into AI models and their inner workings.
  2. Speculative neologisms can provide a framework for understanding how AI processes information and feelings.
  3. Using neologisms can help simulate and investigate complex behaviors in AI models and uncover hidden structures.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

A Short History Of RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 22 Mar 24
🕹 Technology AI Machine Learning Language Models Software Development Data processing
  1. Retrieval Augmented Generation (RAG) helps improve how language models work by adding context to their responses. This means they can give more accurate answers based on the information provided.
  2. Language models can show surprising abilities, called emergent capabilities, but these usually depend on the context they receive. If they get the right context, they can solve problems and adapt better.
  3. To get the best results from language models, it's important to provide them with the right information at the right time. This makes their answers more relevant and helps them understand what’s being asked.

LLM Links, 2/15

In My Tribe • 182 implied HN points • 15 Feb 24
🕹 Technology AI Robots Software Language Models
  1. Bill Gates supports building general-purpose humanoid robots capable of multiple tasks, modeling them after people.
  2. Mark McNeilly predicts that AI will seduce humans rather than destroy us, leading to a decline in human interaction.
  3. There is potential to use large language models for tasks like contract reviews in legal and financial sectors, but resistance to fully relying on AI in certain professions may persist.

What Noam Chomsky gets wrong about AI

ML Powered • 98 implied HN points • 10 Mar 23
🕹 Technology AI Language Models Intelligence Machine Learning
  1. Machine learning models like ChatGPT can be as efficient or even more efficient than the human brain in certain tasks.
  2. Measuring intelligence of machine learning models based solely on the ability to apply the scientific method is unrealistic.
  3. Modern language models like ChatGPT can understand and parse phrases with ease, contradicting claims of their failure in understanding language.

Can we do better than ChatGPT for translation?

Dubverse Black • 98 implied HN points • 05 Jul 23
🕹 Technology Machine Translation Language Models Evaluation Metrics Open Source
  1. The ChatGPT-powered translations are still performing better than other models for most translations.
  2. COMET is an important metric for evaluating translations, focusing on fluency, adequacy, and meaning conveyed.
  3. Open source LLMs like IndicTrans2 and NLLB may be inferior to GCP and GPT, but they can be fine-tuned for better performance.

LLM links

In My Tribe • 136 implied HN points • 06 Mar 24
🕹 Technology AI Robotics Customer Service Language Models Artificial Intelligence
  1. Chatbots like Gemini can reflect biases based on data sources - having diverse datasets can prevent skewed outcomes.
  2. Human brains and Large Language Models (LLMs) share similarities in predicting and processing information.
  3. AI assistants like Klarna's are proving effective in handling customer service inquiries, improving efficiency, and customer experience.

A Must for Indic Language Models

Sector 6 | The Newsletter of AIM • 39 implied HN points • 09 Feb 24
🕹 Technology AI Machine Learning Data science Language Models Benchmarks
  1. There is a big need for benchmarks specifically for Indian languages. This helps assess how well language models perform in those languages.
  2. Upcoming models like Tamil Llama and Odia Llama are pushing for the creation of these benchmarks. They could lead to better evaluations for these Indic language models.
  3. Having a leaderboard for Indic language models is vital. It will spotlight advancements and improvements within India's language technology space.

Catechizing the Bots, Part 2: Reinforcement Learning and Fine-Tuning With RLHF

jonstokes.com • 206 implied HN points • 10 Jun 23
🕹 Technology AI Machine Learning Neural Networks Reinforcement Learning Language Models
  1. Reinforcement Learning is a technique that helps models learn from experiencing pleasure and pain in their environment over time.
  2. Human feedback plays a crucial role in fine-tuning language models by providing ratings that indicate how a model's output impacts users' feelings.
  3. To train models effectively, a preference model can be used to emulate human responses and provide feedback without the need for extensive human involvement.

Will AI replace 'knowledge workers'?

Nick Merrill • 78 implied HN points • 12 May 23
🕹 Technology AI Language Models Automation Future of work
  1. AI may replicate work of 'knowledge workers' but many of these jobs may never have been necessary in the first place
  2. Uncertainty about AI replacing jobs is at the core of the discussion, and it's linked to broader societal structures
  3. There could be a possible third path towards liberation for people among the discourse around AI and knowledge work

How LLMs can self-improve on instruction-following

TechTalks • 39 implied HN points • 29 Jan 24
🕹 Technology AI Machine Learning Language Models Technology Tools
  1. A new technique called Self-Rewarding Language Models helps LLMs improve on instruction-following tasks by creating and evaluating their own training data.
  2. SRLM starts with a base model and seed dataset for fine-tuning instructions, generates new examples and responses, and ranks them using a special prompt.
  3. Experiments show that SRLM enhances model performance in instruction-following and outperforms some existing models on the AlpacaEval benchmark.

Edge 364: About COSP and USP: Two New LLM Reasoning Methods Built by Google Research

TheSequence • 133 implied HN points • 25 Jan 24
🕹 Technology AI Language Models Research Machine Learning Data science
  1. Two new LLM reasoning methods, COSP and USP, have been developed by Google Research to enhance common sense reasoning capabilities in language models.
  2. Prompt generation is crucial for LLM-based applications, and techniques like few-shot setup have reduced the need for large amounts of data to fine-tune models.
  3. Models with robust zero-shot performance can eliminate the need for manual prompt generation, but may have less potent results due to operating without specific guidance.

Are LLMs less sophisticated versions of human brains?

ailogblog • 39 implied HN points • 05 Jan 24
🕹 Technology AI Machine Learning Language Models
  1. Language is only meaningful in a social context. Large Language Models (LLMs) do not understand context, so they do not reason or think in ways similar to humans.
  2. Human brains are embodied, while LLMs are not. This difference is crucial because it affects how language and information processing occur.
  3. The complexity of the human brain far surpasses that of LLMs in terms of size and dimensionality, making direct comparison between the two a category error.

LLM agents and integration dead-ends

Democratizing Automation • 146 implied HN points • 12 Jul 23
🕹 Technology AI Integration ML Generative AI Language Models
  1. The biggest immediate roadblock in generative AI unlocking economic value is the barrier of enabling direct integration of language models
  2. Many are exploring the use of large language models (LLMs) for various business tasks through LLM agents, which are facing challenges of integration and broad scope
  3. The successful commercial viability of LLM agents depends on trust, reliability, management of failure modes, and understanding of feedback dynamics

LLMs are machines. Are you one too?

70 Years Old. WTF! • 58 implied HN points • 19 Feb 23
🕹 Technology AI Machine Learning Language Models Text generation
  1. LLMs are Large Language Models, which are computer systems trained to generate language based on patterns.
  2. LLMs can write better than most humans, but they lack the freedom of expression that humans have.
  3. The difference between how a human writes and how a machine like ChatGPT generates text is the ability to freely use explicit language.

Stanford CRFM suggests moving to workflows rather than tasks for evaluation!

MLOps Newsletter • 58 implied HN points • 04 Sep 23
🕹 Technology Language Models Quantization AI Tools
  1. Stanford CRFM recommends shifting ML validation from task-centric to workflow-centric for better evaluation
  2. Google introduces Ro-ViT for pre-training vision transformers, improving on object detection tasks
  3. Google AI presents Retrieval-VLP for pre-training vision-language models, emphasizing retrieval to enhance performance

📝 Guest Post: Caching LLM Queries for Improved Performance and Cost Savings*

TheSequence • 217 implied HN points • 10 Apr 23
🕹 Technology Language Models APIs
  1. Using a semantic cache can improve LLM application performance by reducing retrieval times and API call expenses.
  2. Caching LLM responses can enhance scalability by reducing the load on the LLM service and improving user experience by reducing network latency.
  3. GPTCache is an open-source semantic cache designed for storing LLM responses efficiently and offers various customization options.

TinyLlama Is An Open-Source Small Language Model

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 15 Mar 24
🕹 Technology AI Software Open Source Mobile Language Models
  1. TinyLlama is a small but powerful language model that's open-source. It can be used on mobile devices and is great for trying out new ideas in language processing.
  2. This model is trained on a huge amount of text, around 1 trillion tokens, which helps it do a good job with various tasks. It performs better than other similar models.
  3. TinyLlama aims to keep getting better and more useful by adding new features and improving its performance in different applications.

Inside Alpaca: The Language Model from Stanford University that can Follow Instructions and Match GPT-3.5

TheSequence • 203 implied HN points • 06 Apr 23
🕹 Technology Language Models Artificial Intelligence Academic Research
  1. Alpaca is a language model from Stanford University that can follow instructions and is smaller than GPT-3.5.
  2. Instruction-following models like GPT-3.5 have issues with false information, social stereotypes, and toxic language.
  3. Academic research on instruction-following models is challenging due to limited availability of models similar to closed-source ones like OpenAI's text-davinci-003.

Self-Reflective Retrieval-Augmented Generation (SELF-RAG)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 04 Mar 24
🕹 Technology AI Machine Learning Generative AI Language Models Data science
  1. SELF-RAG is designed to improve the quality and accuracy of responses from generative AI by allowing the AI to reflect on its own outputs and decide if it needs to retrieve additional information.
  2. The process involves generating special tokens that help the AI evaluate its answers and determine whether to get more information or stick with its original response.
  3. Balancing efficiency and accuracy is crucial; too much focus on speed can lead to wrong answers, while aiming for perfect accuracy can slow down the system.

Modifying readability with large language models (pt. 1)

The Counterfactual • 19 implied HN points • 29 Feb 24
🕹 Technology AI Readability Language Models Human-computer interaction Data Analysis
  1. Large language models can change text to make it easier or harder to read. It's important to check if these changes actually help with understanding.
  2. By comparing modified texts to their original versions, it's clear that 'Easy' texts are generally simpler than 'Hard' texts. However, it can be harder to make texts significantly simpler than they originally are.
  3. Despite the usefulness of these models, they might sometimes lose important information when simplifying texts. Future studies should involve human judgments to see if the changes maintain the original meaning.

Solves maths Olympiad problems, doesn’t grasp place value: What kind of intelligence is GPT-4?

Augmented • 39 implied HN points • 05 Apr 23
🕹 Technology Artificial Intelligence Education Assessment Intelligence Language Models
  1. GPT-4 can solve complex problems but struggles with basic math concepts.
  2. Large language models like GPT-4 excel in certain areas but show limitations in understanding.
  3. The standards used to measure intelligence need to be reevaluated based on the capabilities of AI like GPT-4.

Three key developments

Prompt Engineering • 39 implied HN points • 22 May 23
🕹 Technology AI Language Models
  1. AI is rapidly advancing, especially in the medical field.
  2. New technology like ImageBind can link different types of data with images as a common basis.
  3. Fine-tuning language models with a small number of prompts can significantly improve performance.

The Counterfactual's poll #2

The Counterfactual • 19 implied HN points • 05 Feb 24
🕹 Technology AI Data science Research Communication Language Models
  1. Subscribers can vote each month on research topics. This helps decide what the writer will explore next based on community interest.
  2. The upcoming projects mostly focus on how Large Language Models (LLMs) can measure or modify readability. Some topics might take more than a month to research thoroughly.
  3. One of the suggested studies looks at whether AI responses vary by month, testing if it seems 'lazier' in December compared to other months.

Links #7

Splitting Infinity • 19 implied HN points • 02 Feb 24
🕹 Technology Community AI Language Models Blockchain Scientific research
  1. In a post-scarcity society, communities of hobbyists can lead to significant innovations driven by leisure time and interest rather than necessity.
  2. Drug discovery challenges stem from a lack of understanding of diseases and biology, proposing an alternative approach focusing on experimental drug use and patient data collection.
  3. Language models are scaling down for efficient inference, suggesting that combinations of smaller models may outperform training larger ones.

Adding Noise Improves RAG Performance

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 02 Feb 24
🕹 Technology AI Research Information Retrieval Machine Learning Language Models Data Analysis
  1. Adding irrelevant documents can actually improve accuracy in Retrieval-Augmented Generation systems. This goes against the common belief that only relevant documents are useful.
  2. In some cases, having unrelated information can help the model find the right answer, even better than using only related documents.
  3. It's important to carefully place both relevant and irrelevant documents when building RAG systems to make them work more effectively.