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
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Qianli Wang, Nils Feldhus, Simon Ostermann, Luis Felipe Villa-Arenas, Sebastian Möller, Vera Schmitt
DIVE: Diversified Iterative Self-Improvement
Yiwei Qin, Yixiu Liu, Pengfei Liu
Adjoint sharding for very long context training of state space models
Xingzi Xu, Amir Tavanaei, Kavosh Asadi, Karim Bouyarmane
Labels Generated by Large Language Model Helps Measuring People's Empathy in Vitro
Md Rakibul Hasan, Yue Yao, Md Zakir Hossain, Aneesh Krishna, Imre Rudas, Shafin Rahman, Tom Gedeon
Finding Missed Code Size Optimizations in Compilers using LLMs
Davide Italiano, Chris Cummins
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
Yuqian Yuan, Hang Zhang, Wentong Li, Zesen Cheng, Boqiang Zhang, Long Li, Xin Li, Deli Zhao, Wenqiao Zhang, Yueting Zhuang, Jianke Zhu, Lidong Bing
Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation
M. Ali Bayram, Ali Arda Fincan, Ahmet Semih G"um"uş, Banu Diri, Savaş Yıldırım, "Oner Aytaş
Causal Graph Guided Steering of LLM Values via Prompts and Sparse Autoencoders
Yipeng Kang, Junqi Wang, Yexin Li, Fangwei Zhong, Xue Feng, Mengmeng Wang, Wenming Tu, Quansen Wang, Hengli Li, Zilong Zheng
AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects
Ahmad Mustapha, Hadi Al-Khansa, Hadi Al-Mubasher, Aya Mourad, Ranam Hamoud, Hasan El-Husseini, Marwah Al-Sakkaf, Mariette Awad
Monty Hall and Optimized Conformal Prediction to Improve Decision-Making with LLMs
Harit Vishwakarma, Alan Mishler, Thomas Cook, Niccolò Dalmasso, Natraj Raman, Sumitra Ganesh
Low-Rank Adaptation for Foundation Models: A Comprehensive Review
Menglin Yang, Jialin Chen, Yifei Zhang, Jiahong Liu, Jiasheng Zhang, Qiyao Ma, Harshit Verma, Qianru Zhang, Min Zhou, Irwin King, Rex Ying
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
Wanlong Liu, Junying Chen, Ke Ji, Li Zhou, Wenyu Chen, Benyou Wang
LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language Texts
Helia Hashemi, Jason Eisner, Corby Rosset, Benjamin Van Durme, Chris Kedzie
Echoes in AI: Quantifying Lack of Plot Diversity in LLM Outputs
Weijia Xu, Nebojsa Jojic, Sudha Rao, Chris Brockett, Bill Dolan
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Yichi Zhang, Zhuo Chen, Lingbing Guo, Yajing Xu, Shaokai Chen, Mengshu Sun, Binbin Hu, Zhiqiang Zhang, Lei Liang, Wen Zhang, Huajun Chen
Exploring Variability in Fine-Tuned Models for Text Classification with DistilBERT
Giuliano Lorenzoni, Ivens Portugal, Paulo Alencar, Donald Cowan
Zero-Shot Strategies for Length-Controllable Summarization
Fabian Retkowski, Alexander Waibel
Generative Emergent Communication: Large Language Model is a Collective World Model
Tadahiro Taniguchi, Ryo Ueda, Tomoaki Nakamura, Masahiro Suzuki, Akira Taniguchi
Extracting effective solutions hidden in large language models via generated comprehensive specialists: case studies in developing electronic devices
Hikari Tomita, Nobuhiro Nakamura, Shoichi Ishida, Toshio Kamiya, Kei Terayama
The Potential of LLMs in Automating Software Testing: From Generation to Reporting
Betim Sherifi, Khaled Slhoub, Fitzroy Nembhard