139 citations · 242 across the 41 of their papers we have counts for
17 papers · 1 filter
Do Androids Know They're Only Dreaming of Electric Sheep?
Sky CH-Wang, Benjamin Van Durme, Jason Eisner +1
We design probes trained on the internal representations of a transformer language model to predict its hallucinatory behavior on three grounded generation tasks. To train the prob…
Toucan: Token-Aware Character Level Language Modeling
William Fleshman, Benjamin Van Durme
Character-level language models obviate the need for separately trained tokenizers, but efficiency suffers from longer sequence lengths. Learning to combine character representatio…
FAMuS: Frames Across Multiple Sources
Siddharth Vashishtha, Alexander Martin, William Gantt +2
Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information a…
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Weiting Tan, Haoran Xu, Lingfeng Shen +5
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…
Interpreting User Requests in the Context of Natural Language Standing Instructions
Nikita Moghe, Patrick Xia, Jacob Andreas +3
Users of natural language interfaces, generally powered by Large Language Models (LLMs),often must repeat their preferences each time they make a similar request. We describe an ap…
BLT: Can Large Language Models Handle Basic Legal Text?
Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme
We find that the best publicly available LLMs like GPT-4 and Claude currently perform poorly on basic legal text handling. This motivates the creation of a benchmark consisting of…