Papers
5,479 papers found
Scissorhands: Exploiting the Persistence of Importance Hypothesis for LLM KV Cache Compression at Test Time
Zichang Liu, Aditya Desai, Fangshuo Liao et al.
SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs
Lijun Yu, Yong Cheng, Zhiruo Wang et al.
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan et al.
Joint Prompt Optimization of Stacked LLMs using Variational Inference
Alessandro Sordoni, Eric Yuan, Marc-Alexandre Côté et al.
Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations
Lifan Yuan, Yangyi Chen, Ganqu Cui et al.
To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis
Fuzhao Xue, Yao Fu, Wangchunshu Zhou et al.
The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data Only
Guilherme Penedo, Quentin Malartic, Daniel Hesslow et al.
Jailbroken: How Does LLM Safety Training Fail?
Alexander Wei, Nika Haghtalab, Jacob Steinhardt
Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
Xuan Zhang, Chao Du, Tianyu Pang et al.
AutoManual: Constructing Instruction Manuals by LLM Agents via Interactive Environmental Learning
Minghao Chen, Yihang Li, Yanting Yang et al.
Open LLMs are Necessary for Current Private Adaptations and Outperform their Closed Alternatives
Vincent Hanke, Tom Blanchard, Franziska Boenisch et al.
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh et al.
Efficient Adversarial Training in LLMs with Continuous Attacks
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann et al.
D-LLM: A Token Adaptive Computing Resource Allocation Strategy for Large Language Models
Yikun Jiang, Huanyu Wang, Lei Xie et al.
SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization
Wanhua Li, Zibin Meng, Jiawei Zhou et al.
ReMoDetect: Reward Models Recognize Aligned LLM's Generations
Hyunseok Lee, Jihoon Tack, Jinwoo Shin
QBB: Quantization with Binary Bases for LLMs
Adrian Bulat, Yassine Ouali, Georgios Tzimiropoulos
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
Hang Zhou, Yehui Tang, Haochen Qin et al.
Building on Efficient Foundations: Effective Training of LLMs with Structured Feedforward Layers
Xiuying Wei, Skander Moalla, Razvan Pascanu et al.
IRCAN: Mitigating Knowledge Conflicts in LLM Generation via Identifying and Reweighting Context-Aware Neurons
Dan Shi, Renren Jin, Tianhao Shen et al.
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression
Vladimir Malinovskii, Denis Mazur, Ivan Ilin et al.
AGILE: A Novel Reinforcement Learning Framework of LLM Agents
Peiyuan Feng, Yichen He, Guanhua Huang et al.
WildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs
Seungju Han, Kavel Rao, Allyson Ettinger et al.
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
Jinda Jia, Cong Xie, Hanlin Lu et al.
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
Alexander Nikitin, Jannik Kossen, Yarin Gal et al.