10 papers
Diffusion Language Models are Super Data Learners
Jinjie Ni, Qian Liu, Longxu Dou +5
Under strictly controlled pre-training settings, we observe a Crossover: when unique data is limited, diffusion language models (DLMs) consistently surpass autoregressive (AR) mode…
Training Optimal Large Diffusion Language Models
Jinjie Ni, Qian Liu, Chao Du +5
We introduce Quokka, the first systematic scaling law for diffusion language models (DLMs), encompassing both compute-constrained and data-constrained regimes, and studying the key…
Improving Your Model Ranking on Chatbot Arena by Vote Rigging
Rui Min, Tianyu Pang, Chao Du +3
Chatbot Arena is a popular platform for evaluating LLMs by pairwise battles, where users vote for their preferred response from two randomly sampled anonymous models. While Chatbot…
Bootstrapping Language Models with DPO Implicit Rewards
Changyu Chen, Zichen Liu, Chao Du +5
Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the proces…
When Attention Sink Emerges in Language Models: An Empirical View
Xiangming Gu, Tianyu Pang, Chao Du +5
Language Models (LMs) assign significant attention to the first token, even if it is not semantically important, which is known as attention sink. This phenomenon has been widely a…
Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates
Xiaosen Zheng, Tianyu Pang, Chao Du +3
Automatic LLM benchmarks, such as AlpacaEval 2.0, Arena-Hard-Auto, and MT-Bench, have become popular for evaluating language models due to their cost-effectiveness and scalability…