Physics • Machine Learning • Curiosity
Welcome to Tensors & Quarks
Exploring the cosmos of physics and the depths of machine learning with hands-on experiments, notes, and essays.
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From “Why” to “How”: ReAct’s Unified Reasoning-Acting Paradigm
Large language models (LLMs) have reshaped natural language processing by demonstrating impressive capabilities in text generation, summarization, and translation. Yet, as powerful as they are, these models often struggle when asked to perform complex, multi-step tasks that require deliberate planning and interaction with external information sources. Traditional chain-of-thought (CoT) prompting enables LLMs to articulate intermediate reasoning steps, but it remains confined to the model’s internal knowledge and inference capabilities. Conversely, action-based approaches have allowed models to execute external operations—such as querying an API or navigating an environment—but lack explicit internal reasoning, leading to unexplainable or brittle behavior. The ReAct framework addresses this gap by synergizing reasoning and acting in a unified prompt-based paradigm that interleaves “thoughts” and “actions” to solve complex tasks more effectively and transparently.
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From Facts to Insight: Bridging the Compositionality Gap in Language Models
Large language models (LLMs) such as GPT-3 have transformed natural language understanding by memorizing vast amounts of text. Yet, when faced with questions that require combining multiple pieces of knowledge—so-called compositional reasoning—even the biggest models stumble. In their paper Measuring and Narrowing the Compositionality Gap in Language Models, Press et al. introduce a new metric for this shortfall, show that it persists despite model scale, and propose practical prompting techniques to close it.
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LoRA: A Breakthrough in Efficient Fine-Tuning of Large Language Models
As large language models (LLMs) like GPT-3, LLaMA, and BERT continue to grow in size and influence, one challenge becomes increasingly apparent: while these models offer exceptional capabilities, adapting them for new tasks remains expensive and resource-intensive. Fine-tuning a model with billions of parameters typically requires large datasets, massive compute power, and hours or even days of training time — luxuries not everyone can afford.
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Fine-Tuning Language Models: Welcome to the Nerdy Playground of LLMs
From LoRA to RLHF — and all the acronyms in between
So, you’ve got your hands on a fancy pre-trained language model. Great. It’s read more text than any human ever will, speaks in Shakespearean iambic pentameter and Python, and can tell you the capital of Burkina Faso at 3 AM.
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Welcome to Tensors & Quarks
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Here I’ll share ideas in physics, AI, and their cosmic overlaps.