Tag
This paper investigates the use of large language models (LLMs) and supervised classifiers for depression detection from social media text, proposing a prompt-based embedding method that enhances interpretability. Experiments on multiple datasets show that zero-shot LLMs perform well for binary classification but struggle with fine-grained severity, while supervised models on LLM summary embeddings achieve more consistent performance across multi-class and ordinal tasks.
Proposes TempoWave, a plug-and-play temporal wavelet digit interface that maps time series observations into digit-wise embeddings from multi-wavelet coefficients, improving LLM-based time series forecasting and achieving state-of-the-art on multiple benchmarks.
This paper presents a methodology for delineating concepts and training linear probes to detect them in LLM embeddings, using four example concepts across three models. The work aims to enable scalable monitoring of LLM internal representations.