24 citations · 46 across the 13 of their papers we have counts for
6 papers · 1 filter
Forcing Diffuse Distributions out of Language Models
Yiming Zhang, Avi Schwarzschild, Nicholas Carlini +2
Despite being trained specifically to follow user instructions, today's instructiontuned language models perform poorly when instructed to produce random outputs. For example, when…
Effective Prompt Extraction from Language Models
Yiming Zhang, Nicholas Carlini, Daphne Ippolito
The text generated by large language models is commonly controlled by prompting, where a prompt prepended to a user's query guides the model's output. The prompts used by companies…
FLamE: Few-shot Learning from Natural Language Explanations
Yangqiaoyu Zhou, Yiming Zhang, Chenhao Tan
Natural language explanations have the potential to provide rich information that in principle guides model reasoning. Yet, recent work by Lampinen et al. (2022) has shown limited…
BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biases
Yiming Zhang, Sravani Nanduri, Liwei Jiang +2
Toxicity annotators and content moderators often default to mental shortcuts when making decisions. This can lead to subtle toxicity being missed, and seemingly toxic but harmless…
Active Example Selection for In-Context Learning
Yiming Zhang, Shi Feng, Chenhao Tan
With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-t…
Learning to Ignore Adversarial Attacks
Yiming Zhang, Yangqiaoyu Zhou, Samuel Carton +1
Despite the strong performance of current NLP models, they can be brittle against adversarial attacks. To enable effective learning against adversarial inputs, we introduce the use…