Llms

Abstract This study traces the lyrical evolution of the Beatles across four albums — Rubber Soul (1965), Revolver (1966), Sgt. Pepper’s Lonely Hearts Club Band (1967) and Abbey Road (1969) — using a pipeline of LLM-driven topic modeling (BERTopic), a vector database (OpenAI text-embedding-3-large indexed in ChromaDB), embedding geometry, and graph theory. The study puts a falsifiable question to the canonical critical narrative — that the Beatles moved through four discrete stylistic eras — by testing whether the albums occupy separable territories in lyrical-semantic space. ...

A song can change language while continuing an image, a joke, or an argument. It can also stay in one language while changing subject. Aquamosh, by Plastilina Mosh, offers a setting for asking how a similarity-based representation handles those possibilities. The question is narrower than whether a model understands the album: how do thresholded embedding similarities vary with language transitions, and where do they disagree with another model’s reading? What changed in this revision The previous title claimed a falsification of the distributional hypothesis. Its interpretation also treated GPT-4o-mini as equivalent to a human reference and generalized the results to commercial recommendation, moderation, and support systems. Those claims exceed the evidence available from this experiment and are withdrawn. ...

An album can revisit an idea through different words, voices, and images. It can also repeat the same words while changing what those words mean. That makes musical narrative an interesting setting for a measurement question: what does similarity between lyric embeddings actually tell us? The original analysis compared lyrics from Abbey Road and The Dark Side of the Moon. I expected a representation of sustained themes to reflect my reading of Pink Floyd’s album. That expectation is an interpretation to test, not a ground truth against which every different model result must be declared a failure. ...

Post Objective Data cleaning and preliminary analysis process Understanding the emotional charge or plot development of texts through semantic archaeology based on PCAs Understanding the connections and most representative ideas within the document set Intention Understanding a story’s behavior at the level of its variance is a challenge addressed by attentional engineering. Therefore, using lesser-known methods such as the vector narrative arc combined with a literary map constitutes an interesting route to address increasingly common problems. ...

September 2026 update: This article is retained as an earlier overview. Its evaluation and judge snippets do not reflect the current GenAI API. For current examples and the prompt lifecycle, read MLflow for AI Engineering: Prompts Are Release Artifacts. MLflow for Generative AI Systems I’ll start this post by recalling what Hayen said in her book Designing Machine Learning Systems (2022): ‘Systems are meant to learn’. This statement reflects a simple fact: today, LLMs and to a lesser extent vision language models are winning in the Data Science world. But how do we measure this learning? RLHF work is always a good indicator that perplexity will improve, but let’s return to a key point: LLMs must work as a system, therefore debugging is important, and that’s where the necessary tool for every Data Scientist, AI Engineer, ML Engineer, and MLOps Engineer comes in: MLflow. ...