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Publications about AI methods and applications.
Research paper by Vignesh Chinthakuntla and collaborators, published 2026 in Research Square. Research area: Topic Modeling.
Research paper by Achintya Patil and collaborators, published 2026 in Zenodo (CERN European Organization for Nuclear Research). Research area: Advanced Text Analysis Techniques.
Research paper by Haolun Tang and collaborators, published 2026 in Research Square. Research area: Topic Modeling.
Research paper by Ashbahul Danna Yunas and collaborators, published 2026 in Jurnal Pengembangan Teknologi dan Ilmu Komputer. Research area: Sentiment Analysis and Opinion Mining.
Research paper by Bowen Tan and collaborators. Published 2023-11-12. Topics: large language models, multi-tasking, instruction-following paradigm, parameter-efficient, prompt tuning.
Research paper by Assignee Research, published 2026 in Zenodo (CERN European Organization for Nuclear Research). Research area: Topic Modeling.
This paper explores the impact of context selection on the efficiency of Large Language Models (LLMs) in generating Artificial Intelligence (AI) research leaderboards, a task defined as the extraction of (Task, Dataset, Metric, Score) quadruples from scholarly articles. By framing this challenge as a text generation objective and employing instruction finetuning with the FLAN-T5 collection, we introduce a novel method that surpasses traditional Natural Language Inference (NLI) approaches in adapting to new developments without a predefined taxonomy. Through experimentation with three distinct context types of varying selectivity and length, o
Research paper by Abdur Rasool and collaborators. arXiv record dated 2025-03-11. Research area: Language technology.
Research paper by Chandra Prakash Singh, published 2026 in IEEE International Conference on Control System, Computing and Engineering. Research area: AI in Service Interactions.
Recently, Instruction fine-tuning has risen to prominence as a potential method for enhancing the zero-shot capabilities of Large Language Models (LLMs) on novel tasks. This technique has shown an exceptional ability to boost the performance of moderately sized LLMs, sometimes even reaching performance levels comparable to those of much larger model variants. The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks. We conducted an exploration of six models including Alpaca, Vicuna, WizardLM, and Traditional Task-oriented Models(Flan-T5-XL/XXL, T0++) using real-world relation extraction datasets as case studies. We ca
This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies. To test the zero-shot performance of Flan-T5-XXL and GPT-3.5-turbo, we leverage challenging subsets from two equivalence matching datasets of the OAEI Bio-ML track, taking into account concept labels and structural contexts. Preliminary findings suggest that LLMs have the potential to outperform existing ontology alignment systems like BERTMap, given careful framework and prompt design.
Research paper by Noor Ul Amin, published 2026 in Scientific Societal & Behavioral Research Journal. Research area: Sentiment Analysis and Opinion Mining.
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