Tootfinder

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/5]:
- Highly Fast Text Segmentation With Pairwise Markov Chains
Elie Azeraf, Emmanuel Monfrini, Emmanuel Vignon, Wojciech Pieczynski

Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment
Nevan Wichers, Aram Ebtekar, Ariana Azarbal, Victor Gillioz, Christine Ye, Emil Ryd, Neil Rathi, Henry Sleight, Alex Mallen, Fabien Roger, Samuel Marks
https://arxiv.org/abs/2510.05024

Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment
Large language models are sometimes trained with imperfect oversight signals, leading to undesired behaviors such as reward hacking and sycophancy. Improving oversight quality can be expensive or infeasible, motivating methods that improve learned behavior despite an imperfect training signal. We introduce Inoculation Prompting (IP), a simple but counterintuitive technique that prevents learning of an undesired behavior by modifying training prompts to explicitly request it. For example, to ino…

Shout-out an Münsteraner Studierende: Das Münster Center for Open Science (MüCOS) sucht studentische Hilfskräfte!
Bei Interesse beim @… melden!
https://www.uni-muenster.de/MueCOS/

Deep Learning-Based Approach for Improving Relational Aggregated Search
Sara Saad Soliman, Ahmed Younes, Islam Elkabani, Ashraf Elsayed
https://arxiv.org/abs/2510.00966 https://…

Deep Learning-Based Approach for Improving Relational Aggregated Search
Due to an information explosion on the internet, there is a need for the development of aggregated search systems that can boost the retrieval and management of content in various formats. To further improve the clustering of Arabic text data in aggregated search environments, this research investigates the application of advanced natural language processing techniques, namely stacked autoencoders and AraBERT embeddings. By transcending the limitations of traditional search engines, which are i…

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Parham Khamsepour, Mark Cole, Ish Ashraf, Sandeep Puri, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2509.03463

The Impact of Critique on LLM-Based Model Generation from Natural Language: The Case of Activity Diagrams
Large Language Models (LLMs) show strong potential for automating the generation of models from natural-language descriptions. A common approach is an iterative generate-critique-refine loop, where candidate models are produced, evaluated, and updated based on detected issues. This process needs to address: (1) structural correctness - compliance with well-formedness rules - and (2) semantic alignment - accurate reflection of the intended meaning in the source text. We present LADEX (LLM-based …

#PhantastikPrompts 19.7.:
Benutzt du einen Thesaurus?
Nein. Früher kursierte es als Schreibtipp, ja niemals innerhalb {hier willkürliche Zahl zwischen 1 und 10 Normseiten einfügen} ein Wort zu wiederholen.
Das ergab gestelzte Texte voller nicht ins Setting passender Synonyme, Grammatik-Akrobatik ohne Hilfsverben und drei verschiedenen Bezeichnungen für Figuren. (Fenia…

Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Abhishek Kuber, Enrico Liscio, Ruixuan Zhang, Caroline Figueroa, Pradeep K. Murukannaiah
https://arxiv.org/abs/2508.20771 …

Signs of Struggle: Spotting Cognitive Distortions across Language and Register
Rising mental health issues among youth have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One key focus is the identification of cognitive distortions, irrational thought patterns that have a role in aggravating mental distress. Early detection of these distortions may enable timely, low-cost interventions. While prior work has focused on English clinical data, we present the first in-depth study of cross-lingual and cross-regis…

A Classification-Driven Likelihood Ratio Method for Familial DNA Testing
Akaraphon Jantaraphum, Chanagarn Laoiam, Budsaba Rerkamnuaychoke, Jittima Shotivaranon, Monchai Kooakachai
https://arxiv.org/abs/2508.15579

A Classification-Driven Likelihood Ratio Method for Familial DNA Testing
Background: Familial DNA search relies on statistical methods to infer genetic relationships, often using the likelihood ratio (LR) as the test statistic. This approach involves calculating the probability of observed DNA profiles under various scenarios, traditionally assuming a uniform genetic background for all individuals. However, this assumption is unrealistic in practice due to population substructure, where allele frequencies differ among subpopulations. To address this, modified LR-bas…