8 papers · 1 filter
Reliable, Adaptable, and Attributable Language Models with Retrieval
Akari Asai, Zexuan Zhong, Danqi Chen +4
Parametric language models (LMs), which are trained on vast amounts of web data, exhibit remarkable flexibility and capability. However, they still face practical challenges such a…
Improving Language Understanding from Screenshots
Tianyu Gao, Zirui Wang, Adithya Bhaskar +1
An emerging family of language models (LMs), capable of processing both text and images within a single visual view, has the promise to unlock complex tasks such as chart understan…
Poisoning Retrieval Corpora by Injecting Adversarial Passages
Zexuan Zhong, Ziqing Huang, Alexander Wettig +1
Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but to what extent can they be safely deployed in real-world applications? In th…
Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation
Yangsibo Huang, Samyak Gupta, Mengzhou Xia +2
The rapid progress in open-source large language models (LLMs) is significantly advancing AI development. Extensive efforts have been made before model release to align their behav…
Privacy Implications of Retrieval-Based Language Models
Yangsibo Huang, Samyak Gupta, Zexuan Zhong +2
Retrieval-based language models (LMs) have demonstrated improved interpretability, factuality, and adaptability compared to their parametric counterparts, by incorporating retrieve…
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
Chenglei Si, Dan Friedman, Nitish Joshi +3
In-context learning (ICL) is an important paradigm for adapting large language models (LLMs) to new tasks, but the generalization behavior of ICL remains poorly understood. We inve…