A Blog post by Dharma-AI on Hugging Face
Artificial intelligences and human scientists exhibit...
We investigate the effectiveness of artificial intelligences (AI)-specifically large language models (LLMs)-relative to human scientists at high-level cognitive tasks in social science such as...
Slop-Machine Future
The arc of large language models is mediocre, and it bends toward “target procurement”.
LLM in a Flash: Efficient Large Language Model Inference with Limited Memory
Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks…
Understanding Understanding: A Pragmatic Framework Motivated by...
Motivated by the rapid ascent of Large Language Models (LLMs) and debates about the extent to which they possess human-level qualities, we propose a framework for testing whether any agent (be it...
elvis on Twitter / X
Small Language Models are the Future of Agentic AILots to gain from building agentic systems with small language models.Capabilities are increasing rapidly!AI devs should be exploring SLMs.Here are my notes: pic.twitter.com/7dhmz9V2jB— elvis (@omarsar0) July 1, 2025
A jargon-free explanation of how AI large language models work
Want to really understand large language models? Here’s a gentle primer.
Curated retrieval versus open web search in public AI information...
Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these...
Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Generative artificial intelligence (AI) and machine learning (ML) models are being adopted across a variety of domains. As these technologies develop, there is notable diversity in their levels of availability and paths of diffusion. For example, fully closed-source models may be available through chatbots and API calls, but their weights, source code, training data, and other artifacts remain hidden from view. In contrast, open models make some or all of these materials publicly available for developers and downstream users.
Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function
What makes large language models (LLMs) impressive is also what makes them hard to evaluate: their diversity of uses. To evaluate these models, we must understand the purposes they will be used for. We consider a setting where these deployment decisions are made by people, and in particular, people's beliefs about where an LLM will perform well. We model such beliefs as the consequence of a human generalization function: having seen what an LLM gets right or wrong, people generalize to where else it might succeed. We collect a dataset of 19K examples of how humans make generalizations across 79 tasks from the MMLU and BIG-Bench benchmarks. We show that the human generalization function can be predicted using NLP methods: people have consistent structured ways to generalize. We then evaluate LLM alignment with the human generalization function. Our results show that -- especially for cases where the cost of mistakes is high -- more capable models (e.g. GPT-4) can do worse on the instances people choose to use them for, exactly because they are not aligned with the human generalization function.
The Philosophy of Language Models
ABSTRACT The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human‐like linguistic and cognitive capacities, whereas others ascribe it to shallow pattern matching. These disputes stem from deep‐seated philosophical disagreements about the nature of language and cognition. We provide an opinionated survey of these disagreements across core topics in the philosophy of mind and language, including syntactic competence, compositionality, linguistic meaning, representation, attitudes, reasoning, agency, and consciousness. We contend that progress on these issues requires not only clarity about background philosophical commitments but also, in many cases, close engagement with emerging empirical evidence.
AI slop has hit the science communicators
Small Language Models are the Future of Agentic AI
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI systems is, however, ushering in a mass of applications in which language models perform a small number of specialized tasks repetitively and with little variation. Here we lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. Our argumentation is grounded in the current level of capabilities exhibited by SLMs, the common architectures of agentic systems, and the economy of LM deployment. We further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models) are the natural choice. We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm. Our position, formulated as a value statement, highlights the significance of the operational and economic impact even a partial shift from LLMs to SLMs is to have on the AI agent industry. We aim to stimulate the discussion on the effective use of AI resources and hope to advance the efforts to lower the costs of AI of the present day. Calling for both contributions to and critique of our position, we commit to publishing all such correspondence at https://research.nvidia.com/labs/lpr/slm-agents.
Augmenting Research Ideation with Data: An Empirical Investigation...
Recent advancements in large language models (LLMs) demonstrate strong potential for generating novel research ideas, yet such ideas often struggle with feasibility and effectiveness. In this...
Dria on Twitter / X
Introducing Inference Arena v2.0.An agentic experience that searches, analyzes, and delivers insights about LLM inference.When we first launched, our goal was simple: make it easier for developers to compare models, engines, and hardware without digging through scattered… pic.twitter.com/fgWgos48lW— Dria (@driaforall) September 30, 2025
