Natural Language Processing
Natural Language Processing (NLP) focuses on enabling computers to understand, interpret, and generate human language. Current research heavily emphasizes large language models (LLMs), exploring their capabilities in various tasks like question answering, text classification, and translation, while also addressing challenges such as bias, efficiency, and the need for better evaluation metrics. The field's significance lies in its potential to revolutionize numerous applications, from improving healthcare and education to enhancing information access and facilitating more effective human-computer interaction.
Papers
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression
Utkarsh Saxena, Gobinda Saha, Sakshi Choudhary, Kaushik Roy
P3: A Policy-Driven, Pace-Adaptive, and Diversity-Promoted Framework for data pruning in LLM Training
Yingxuan Yang, Huayi Wang, Muning Wen, Xiaoyun Mo, Qiuying Peng, Jun Wang, Weinan Zhang
LogogramNLP: Comparing Visual and Textual Representations of Ancient Logographic Writing Systems for NLP
Danlu Chen, Freda Shi, Aditi Agarwal, Jacobo Myerston, Taylor Berg-Kirkpatrick
BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
Yupeng Chang, Yi Chang, Yuan Wu
LaDiMo: Layer-wise Distillation Inspired MoEfier
Sungyoon Kim, Youngjun Kim, Kihyo Moon, Minsung Jang
Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity
Wrick Talukdar, Anjanava Biswas
SLIM-RAFT: A Novel Fine-Tuning Approach to Improve Cross-Linguistic Performance for Mercosur Common Nomenclature
Vinícius Di Oliveira, Yuri Façanha Bezerra, Li Weigang, Pedro Carvalho Brom, Victor Rafael R. Celestino
mucAI at WojoodNER 2024: Arabic Named Entity Recognition with Nearest Neighbor Search
Ahmed Abdou, Tasneem Mohsen
BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba
Ling Yue, Sixue Xing, Yingzhou Lu, Tianfan Fu
Long Input Benchmark for Russian Analysis
Igor Churin, Murat Apishev, Maria Tikhonova, Denis Shevelev, Aydar Bulatov, Yuri Kuratov, Sergej Averkiev, Alena Fenogenova
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
Albert Sawczyn, Katsiaryna Viarenich, Konrad Wojtasik, Aleksandra Domogała, Marcin Oleksy, Maciej Piasecki, Tomasz Kajdanowicz
Do Large Language Models Speak All Languages Equally? A Comparative Study in Low-Resource Settings
Md. Arid Hasan, Prerona Tarannum, Krishno Dey, Imran Razzak, Usman Naseem