Large Language Model
Large language models (LLMs) are sophisticated AI systems designed to process and generate human-like text, aiming to improve various natural language processing tasks. Current research focuses on enhancing LLM safety, efficiency (through techniques like quantization and optimized decoding), and fairness, as well as improving their ability to perform complex reasoning and handle diverse instructions. These advancements are significant because they address critical limitations in current LLMs and pave the way for broader applications across diverse fields, including healthcare, legal tech, and autonomous systems.
Papers
Benchmarking large language models for materials synthesis: the case of atomic layer deposition
Angel Yanguas-Gil, Matthew T. Dearing, Jeffrey W. Elam, Jessica C. Jones, Sungjoon Kim, Adnan Mohammad, Chi Thang Nguyen, Bratin Sengupta
Low-Rank Adaptation with Task-Relevant Feature Enhancement for Fine-tuning Language Models
Changqun Li, Chaofan Ding, Kexin Luan, Xinhan Di
Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning
Melanie Sclar, Jane Yu, Maryam Fazel-Zarandi, Yulia Tsvetkov, Yonatan Bisk, Yejin Choi, Asli Celikyilmaz
GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang, Yusen Zhang, Caiming Xiong, Rui Zhang
Does Representation Matter? Exploring Intermediate Layers in Large Language Models
Oscar Skean, Md Rifat Arefin, Yann LeCun, Ravid Shwartz-Ziv
Foundational Large Language Models for Materials Research
Vaibhav Mishra, Somaditya Singh, Dhruv Ahlawat, Mohd Zaki, Vaibhav Bihani, Hargun Singh Grover, Biswajit Mishra, Santiago Miret, Mausam, N. M. Anoop Krishnan
Systematic Analysis of LLM Contributions to Planning: Solver, Verifier, Heuristic
Haoming Li, Zhaoliang Chen, Songyuan Liu, Yiming Lu, Fei Liu
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low
The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspective
Javier de la Rosa, Vladislav Mikhailov, Lemei Zhang, Freddy Wetjen, David Samuel, Peng Liu, Rolv-Arild Braaten, Petter Mæhlum, Magnus Breder Birkenes, Andrey Kutuzov, Tita Enstad, Svein Arne Brygfjeld, Jon Atle Gulla, Stephan Oepen, Erik Velldal, Wilfred Østgulen, Liljia Øvrelid, Aslak Sira Myhre
From Intention To Implementation: Automating Biomedical Research via LLMs
Yi Luo, Linghang Shi, Yihao Li, Aobo Zhuang, Yeyun Gong, Ling Liu, Lin Chen
AI Predicts AGI: Leveraging AGI Forecasting and Peer Review to Explore LLMs' Complex Reasoning Capabilities
Fabrizio Davide, Pietro Torre, Andrea Gaggioli
CRVQ: Channel-relaxed Vector Quantization for Extreme Compression of LLMs
Yuzhuang Xu, Shiyu Ji, Qingfu Zhu, Wanxiang Che
GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning
Abdessalam Ed-dib, Zhanibek Datbayev, Amine Mohamed Aboussalah
When Text Embedding Meets Large Language Model: A Comprehensive Survey
Zhijie Nie, Zhangchi Feng, Mingxin Li, Cunwang Zhang, Yanzhao Zhang, Dingkun Long, Richong Zhang
Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning
Wenna Lai, Haoran Xie, Guandong Xu, Qing Li
ZigZagkv: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty
Meizhi Zhong, Xikai Liu, Chen Zhang, Yikun Lei, Yan Gao, Yao Hu, Kehai Chen, Min Zhang
RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios
Ruiwen Zhou, Wenyue Hua, Liangming Pan, Sitao Cheng, Xiaobao Wu, En Yu, William Yang Wang
Reasoning-Aware Query-Focused Summarization over Multi-Table Data
Xiaochuan Lin, Xiangyong Chen
Align, Generate, Learn: A Novel Closed-Loop Framework for Cross-Lingual In-Context Learning
Mateo Alejandro Rojas, Rafael Carranza
CareBot: A Pioneering Full-Process Open-Source Medical Language Model
Lulu Zhao, Weihao Zeng, Xiaofeng Shi, Hua Zhou