It is now understood that the attention mechanism in large language models (LLMs) serves multiple functions. By analyzing attention, we gain insight into why LLMs succeed at in-context learning and chain-of-thought—and, consequently, why LLMs sometimes succeed at extrapolation. In this article, we aim to unpack this question by observing various types of attention mechanisms. Basic […]
Attention in LLMs and Extrapolation Read Post »
Most existing machine learning models aim to maximize predictive accuracy, but in this article, I will introduce classifiers that prioritize interestingness. What Does It Mean to Prioritize Interestingness? For example, let us consider the task of classifying whether a user is an adult based on their profile.If the profile contains an age feature, then the
Interestingness First Classifiers Read Post »
As the capabilities of AI continue to grow, it is becoming increasingly difficult for humans to supervise them. In this article, we discuss this pressing issue through the lens of a recent paper presented at ICLR 2025 by a group from Anthropic: Language Models Learn to Mislead Humans via RLHF. This paper investigates an intriguing
Deceptive and Lazy AIs: When Language Models Learn to Mislead Read Post »
In the field of Natural Language Processing (NLP), a central theme has always been “how to make computers understand the meaning of words.” One fundamental technique for this is “Word Embedding.” This technique converts words into numerical vectors (lists of numbers), with methods like Word2Vec and GloVe being well-known. Using these vectors allows for calculations
Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem Read Post »
“Recommended for You” YouTube, Amazon, Netflix, X, news sites… We are surrounded by services displaying recommendations. While convenient, have you ever felt, “I keep getting recommended the same kinds of things,” or “I wish I could get recommendations from a different perspective”? Or perhaps wondered, “Are these recommendations biased?” The truth is, these “recommendations” (recommender
Hack Your Feed: Take Control of Your Recommendations Read Post »
I came across an intriguing post while browsing X during New Year’s holidays. Historically, humanity has pursued the idea that truth is inherently simple, exemplified by equations like \(E = mc^2\) or \(F = ma\). However, the idea presented was that such truths may represent only a small subset of the entire truth, suggesting the
The Hidden Information AI Understands but Humans Miss Read Post »
