Tootfinder

This https://arxiv.org/abs/2402.08015 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Walia-LLM: Enhancing Amharic-LLaMA by Integrating Task-Specific and Generative Datasets
Large language models (LLMs) have received a lot of attention in natural language processing (NLP) research because of their exceptional performance in understanding and generating human languages. However, low-resource languages are left behind due to the unavailability of resources. In this work, we focus on enhancing the LLaMA-2-Amharic model by integrating task-specific and generative datasets to improve language model performance for Amharic. We compile an Amharic instruction fine-tuning d…

An Optimised Brushless DC Motor Control Scheme for Robotics Applications
Nilabha Das, Laxman Rao S. Paragond, Balkrushna H. Waghmare
https://arxiv.org/abs/2404.17367

An Optimised Brushless DC Motor Control Scheme for Robotics Applications
This work aims to develop an integrated control strategy for Brushless Direct Current Motors for a wide range of applications in robotics systems. The controller is suited for both high torque - low speed and high-speed control of the motors. Hardware validation is done by developing a custom BLDC drive system, and the circuit elements are optimised for power efficiency.

This https://arxiv.org/abs/2308.06013 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Large Language Models for Telecom: Forthcoming Impact on the Industry
Large Language Models (LLMs), AI-driven models that can achieve general-purpose language understanding and generation, have emerged as a transformative force, revolutionizing fields well beyond Natural Language Processing (NLP) and garnering unprecedented attention. As LLM technology continues to progress, the telecom industry is facing the prospect of its impact on its landscape. To elucidate these implications, we delve into the inner workings of LLMs, providing insights into their current ca…

DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition
Kenneth Enevoldsen, Emil Trenckner Jessen, Rebekah Baglini
https://arxiv.org/abs/2402.18209

DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition
Named entity recognition is one of the cornerstones of Danish NLP, essential for language technology applications within both industry and research. However, Danish NER is inhibited by a lack of available datasets. As a consequence, no current models are capable of fine-grained named entity recognition, nor have they been evaluated for potential generalizability issues across datasets and domains. To alleviate these limitations, this paper introduces: 1) DANSK: a named entity dataset providing …

5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2024) is co-organized by my colleagues from @…, co-located with SIGIR 2024.
paper deadline: April 25, 2024
website: http://

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This https://arxiv.org/abs/2302.14534 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face
We present Spacerini, a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face to enable the seamless construction and deployment of interactive search engines. Spacerini makes state-of-the-art sparse and dense retrieval models more accessible to non-IR practitioners while minimizing deployment effort. This is useful for NLP researchers who want to better understand and validate their research by performing qualitative analyses of training co…

This https://arxiv.org/abs/2401.16979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Re3val: Reinforced and Reranked Generative Retrieval
Generative retrieval models encode pointers to information in a corpus as an index within the model's parameters. These models serve as part of a larger pipeline, where retrieved information conditions generation for knowledge-intensive NLP tasks. However, we identify two limitations: the generative retrieval does not account for contextual information. Secondly, the retrieval can't be tuned for the downstream readers as decoding the page title is a non-differentiable operation. This paper intr…

Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction
Mohan Raj Chanthran, Lay-Ki Soon, Huey Fang Ong, Bhawani Selvaretnam
https://arxiv.org/abs/2402.14521

Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction
Standard English and Malaysian English exhibit notable differences, posing challenges for natural language processing (NLP) tasks on Malaysian English. Unfortunately, most of the existing datasets are mainly based on standard English and therefore inadequate for improving NLP tasks in Malaysian English. An experiment using state-of-the-art Named Entity Recognition (NER) solutions on Malaysian English news articles highlights that they cannot handle morphosyntactic variations in Malaysian Englis…

This https://arxiv.org/abs/2312.06499 has been replaced.
link: https://scholar.google.com/scholar?q=a

TaCo: Targeted Concept Removal in Output Embeddings for NLP via Information Theory and Explainability
The fairness of Natural Language Processing (NLP) models has emerged as a crucial concern. Information theory indicates that to achieve fairness, a model should not be able to predict sensitive variables, such as gender, ethnicity, and age. However, information related to these variables often appears implicitly in language, posing a challenge in identifying and mitigating biases effectively. To tackle this issue, we present a novel approach that operates at the embedding level of an NLP model,…