Tag
Proposes SERAF, a multimodal retrieval-augmented framework for time series forecasting that uses both numerical similarity and self-generated textual descriptions to retrieve historical patterns, improving forecasting under non-stationarity. Experiments on seven real-world datasets show effectiveness over state-of-the-art baselines.
This paper introduces a semantic-timescale analysis pipeline to study how generic vs. specific content is distributed over time in human and AI-generated speech, revealing that autocorrelation-window measures capture temporal organization of semantics beyond static lexical distributions.
This philosophical paper argues that AI chatbot outputs are meaningful under standard theories of language, without requiring anthropomorphic assumptions about mental states or intentions.
The article explains the confusing behavior of C array types, including their decay to pointers, exceptions like sizeof and function parameters, and compares it to function types, suggesting a mental model where arrays and pointers are strictly separated.
A blog post by Ben Meyer shares new-to-me facts about the HTML <dl> element, including that multiple <dd> can follow a <dt>, grouping within <div>, ARIA labeling, and that they've been called 'description lists' since 2008.
A detailed guide to using the HTML <dl>, <dt>, and <dd> elements to semantically mark up name-value pairs, with examples.
This paper studies how LLMs interpret English degree modifiers (e.g., 'slightly', 'drastically') in a controlled resource-allocation task. It finds that the model compresses 10 intensity words into 5 distinct numeric outputs, and the interpretation is heavily state-dependent, with lexical differentiation collapsing near system capacity.