The article discusses how AI detectors can mistakenly flag genuine reviews as problematic, leading to a decrease in the accuracy of sentiment models when those reviews are filtered out. It explores three methods the author tested to identify and mitigate this issue of ‘AI slop’ in training datasets.
Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why' The post Is This Slop? Detecting AI-Generated Content Without a Model appeared first on Towards Data Science .
We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews.
The article titled "The AI That Learned to Understand Long After It Stopped Trying" discusses a small, strange discovery in machine learning known as grokking. It highlights how this phenomenon involves an AI developing understanding after ceasing to actively try. The piece was originally published on Towards Data Science.
Python tutorial for fine-tuning a Mistral Small 3. 1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science .
AI does not decide who gets fired. Companies do.
arXiv:2606. 29614v1 Announce Type: cross Abstract: This study examines whether supervised fine-tuning remains necessary for Turkish sentiment analysis in the era of large language models.
The analytics career I signed up for five years ago doesn't exist anymore, and honestly, I am fine with that. The post How I’m Making Sure My Analytics Career Doesn’t Get Eaten by AI appeared first on Towards Data Science .
arXiv:2609.15369v1 Announce Type: new Abstract: Word-level detectors identify unedited AI-generated text almost perfectly, but the literature documents their brittleness under rewording, and a word-l...
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
Five scikit-learn defaults that deserve a closer look before your next model reaches production The post Your AI Assistant Wrote the Code. Who Checked the Defaults? appeared first on Towards Data Scie...
The paper introduces KESA, a knowledge‑enhanced approach for sentence‑level sentiment analysis that incorporates sentiment knowledge through two auxiliary tasks: sentiment word cloze and conditional sentiment prediction. These tasks use prior sentiment polarity to guide the selection of sentiment words and the prediction of overall sentiment, respectively, and explore label combination methods to unify multiple label types. Experiments show that KESA consistently outperforms pre‑trained models and complements existing knowledge‑enhanced post‑training methods.
The article explores how public opinion toward AI can shift when people recognize tangible benefits, and examines what occurs when such value is not perceived. It discusses the dynamics of acceptance and resistance to AI technologies based on perceived tradeoffs and benefits.