Tootfinder

How Well Do LLMs Imitate Human Writing Style?
Rebira Jemama, Rajesh Kumar
https://arxiv.org/abs/2509.24930 https://arxiv.org/pdf/2509.24930

How Well Do LLMs Imitate Human Writing Style?
Large language models (LLMs) can generate fluent text, but their ability to replicate the distinctive style of a specific human author remains unclear. We present a fast, training-free framework for authorship verification and style imitation analysis. The method integrates TF-IDF character n-grams with transformer embeddings and classifies text pairs through empirical distance distributions, eliminating the need for supervised training or threshold tuning. It achieves 97.5\% accuracy on academ…

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Md Afif Al Mamun, Gias Uddin, Lan Xia, Longyu Zhang
https://arxiv.org/abs/2508.16860

TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token semantics than traditional Machine Learning (ML) models that rely on statistical features (e.g., TF-IDF, bag of words). However, PLMs may still attend to less relevant tokens in a bug report, which can impact their effectiveness. In addition, the model can be sub-optimal with its recommendations when the interaction history of developers around similar bug…

Semantic Search for Information Retrieval
Kayla Farivar
https://arxiv.org/abs/2508.17694 https://arxiv.org/pdf/2508.17694

Semantic Search for Information Retrieval
Information retrieval systems have progressed notably from lexical techniques such as BM25 and TF-IDF to modern semantic retrievers. This survey provides a brief overview of the BM25 baseline, then discusses the architecture of modern state-of-the-art semantic retrievers. Advancing from BERT, we introduce dense bi-encoders (DPR), late-interaction models (ColBERT), and neural sparse retrieval (SPLADE). Finally, we examine MonoT5, a cross-encoder model. We conclude with common evaluation tactics,…

Searching the Title of Practical Work of the Informatics Engineering Bachelor Program with the Case Base Reasoning Method
Agung Sukrisna Jaya, Osvari Arsalan, Danny Matthew Saputra
https://arxiv.org/abs/2508.20442

Searching the Title of Practical Work of the Informatics Engineering Bachelor Program with the Case Base Reasoning Method
Case Base Reasoning (CBR) is a case solving technique based on experience in cases that have occurred before with the highest similarity. CBR is used to search for practical work titles. TF-IDF is applied to process the vectorization of each practical work title word and Cosine Similarity for the calculation of similarity values. This system can search either in the form of titles or keywords. The output of the system is the title of practical work and the match value of each title. Based on th…

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Mohammad Zana Majidi, Sajjad Karimi, Teng Wang, Robert Kluger, Reginald Souleyrette
https://arxiv.org/abs/2509.07845

Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
Predicting injuries and fatalities in traffic crashes plays a critical role in enhancing road safety, improving emergency response, and guiding public health interventions. This study investigates the added value of unstructured crash narratives (written by police officers at the scene) when combined with structured crash data to predict injury severity. Two widely used Natural Language Processing (NLP) techniques, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, were employed t…

The Few-shot Dilemma: Over-prompting Large Language Models
Yongjian Tang, Doruk Tuncel, Christian Koerner, Thomas Runkler
https://arxiv.org/abs/2509.13196 https://

The Few-shot Dilemma: Over-prompting Large Language Models
Over-prompting, a phenomenon where excessive examples in prompts lead to diminished performance in Large Language Models (LLMs), challenges the conventional wisdom about in-context few-shot learning. To investigate this few-shot dilemma, we outline a prompting framework that leverages three standard few-shot selection methods - random sampling, semantic embedding, and TF-IDF vectors - and evaluate these methods across multiple LLMs, including GPT-4o, GPT-3.5-turbo, DeepSeek-V3, Gemma-3, LLaMA-3…

Automated Research Article Classification and Recommendation Using NLP and ML
Shadikur Rahman, Hasibul Karim Shanto, Umme Ayman Koana, Syed Muhammad Danish
https://arxiv.org/abs/2510.05495

Automated Research Article Classification and Recommendation Using NLP and ML
In the digital era, the exponential growth of scientific publications has made it increasingly difficult for researchers to efficiently identify and access relevant work. This paper presents an automated framework for research article classification and recommendation that leverages Natural Language Processing (NLP) techniques and machine learning. Using a large-scale arXiv.org dataset spanning more than three decades, we evaluate multiple feature extraction approaches (TF--IDF, Count Vectorize…