5 citations
- Charles Darwin UniversityAU2 papers
- Monash UniversityAU2 papers
- United International UniversityBD2 papers
- Association for the Advancement of Artificial IntelligenceUS1 paper
- Chung-Ang UniversityKR1 paper
- GeneMatrix (South Korea)KR1 paper
- Lappeenranta-Lahti University of TechnologyFI1 paper
- RF Laboratories (United States)US1 paper
- Seoul National UniversityKR1 paper
- Universitat de ValènciaES1 paper
- University of OxfordGB1 paper
6 papers
The em-dash em-beds in Congress: A population-level rise in em-dash frequency in U.S. congressional press releases at the dawn of the large-language-model era, 2021-2025
Przemysław Czuma
Large language models (LLMs) can leave small stylistic traces in text written with their help. The most discussed is the em-dash (U+2014), especially the unspaced form word---word,…
IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering
JungMin Yun, YoungBin Kim
Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy cont…
Structural constraints to compare phenomenal experience
J. DÃaz-Boils, N. Tsuchiya, CM. Signorelli
This article defines a partial order structure to study the relationship between levels and contents of conscious subjective experience in a single mathematical set-up. We understa…
Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation
Musarrat Zeba, Abdullah Al Mamun, Kishoar Jahan Tithee +8
In healthcare, it is essential for any Large Language Model (LLM)-generated output to be reliable and accurate, particularly in cases involving decision-making and patient safety.…
A fine-grained attention and geometric correspondence model for musculoskeletal risk classification in athletes using multimodal visual and skeletal features
Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Tamanna Shermin +3
Musculoskeletal disorders pose significant risks to athletes, and early risk assessment is essential for prevention. However, most existing methods are designed for controlled sett…
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
Haengbok Chung, Jae Sung Lee
Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contributing to non-identically distrib…