This paper introduces a practical pipeline to convert text corpora into quantitative semantic signals, employing embeddings, logprob-based evaluation, and noise reduction. The case study applies six semantic dimensions to Portuguese news articles about AI, supporting AI engineering tasks such as corpus inspection and monitoring.
Sem-Detect is a novel method for distinguishing between human-written and AI-generated peer reviews, combining textual features with claim-level semantic analysis. It leverages the observation that AI models tend to converge on similar points, while human reviewers introduce more unique ideas, enabling the detection of fully AI reviews and human reviews refined by LLMs.
Este artigo propõe uma estrutura de refinamento baseada em raciocínio que utiliza LLMs como juízes semânticos para validar e reestruturar os resultados de algoritmos de agrupamento de texto não supervisionados. A estrutura inclui verificação de coerência, adjudicação de redundância e fundamentação de rótulos, visando melhorar a qualidade dos clusters sem dados rotulados.
This work investigates the persistent effect of lexical overlap, rather than semantic content, on representations extracted from large language models (LLMs) and its implications. The authors find that lexical influence extends across model depths, architectures, and training regimes, even in models trained for semantic similarity.
This paper introduces the Weighted Syntactic and Semantic Context Assessment Summary (wSSAS), a deterministic framework to optimize text categorization using LLMs. It addresses LLM limitations by organizing text hierarchically and employing a Signal-to-Noise Ratio (SNR) to focus on high-value semantic features.