9 citations · 16 across the 13 of their papers we have counts for
Showing 2023 · cs.CLShow all
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cs.CL2023
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
Jaehyung Kim, Yuning Mao, Rui Hou +7
Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversa…
cs.CL2023★ 4 cited
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Suyu Ge, Chunting Zhou, Rui Hou +5
Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressin…
cs.CL2023★ 9 cited
Effective Long-Context Scaling of Foundation Models
Wenhan Xiong, Jingyu Liu, Igor Molybog +18
We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with l…