7 citations · 18 across the 6 of their papers we have counts for
8 papers
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning
Ruizhe Shi, Zaiwen Yang, Qiubo Wang +2
We study sparse and continual reinforcement learning fine-tuning of language models through attention-head ranking and partitioning. For sparse learning, we rank attention heads by…
Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks
Jihan Yao, Gantavya Bhatt, Arnav Das +16
We study LLM benchmark coreset selection: selecting a small subset of prompts over multiple benchmarks whose induced model scores and rankings approximate those obtained from the f…
How to Evaluate Reward Models for RLHF
Evan Frick, Tianle Li, Connor Chen +6
We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-s…
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick +5
The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks.…
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng +8
Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To addres…
Fairness in Serving Large Language Models
Ying Sheng, Shiyi Cao, Dacheng Li +5
High-demand LLM inference services (e.g., ChatGPT and BARD) support a wide range of requests from short chat conversations to long document reading. To ensure that all client reque…
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Ying Sheng, Shiyi Cao, Dacheng Li +9
The "pretrain-then-finetune" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often…
QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
Zhikai Li, Xiaoxuan Liu, Banghua Zhu +3
Large Language Models (LLMs) have showcased remarkable impacts across a wide spectrum of natural language processing tasks. Fine-tuning these pretrained models on downstream datase…