7 papers
Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling
Jannek Ulm, Kevin Du, Vésteinn Snæbjarnarson
Large language models (LLMs) are trained on huge amounts of textual data, and concerns have been raised that the limits of such data may soon be reached. A potential solution is to…
How Persuasive is Your Context?
Tu Nguyen, Kevin Du, Alexander Miserlis Hoyle +1
Two central capabilities of language models (LMs) are: (i) drawing on prior knowledge about entities, which allows them to answer queries such as "What's the official language of A…
Controllable Context Sensitivity and the Knob Behind It
Julian Minder, Kevin Du, Niklas Stoehr +4
When making predictions, a language model must trade off how much it relies on its context vs. its prior knowledge. Choosing how sensitive the model is to its context is a fundamen…
Taxonomy-Aware Evaluation of Vision-Language Models
Vésteinn Snæbjarnarson, Kevin Du, Niklas Stoehr +4
When a vision-language model (VLM) is prompted to identify an entity depicted in an image, it may answer 'I see a conifer,' rather than the specific label 'norway spruce'. This rai…
The Causal Influence of Grammatical Gender on Distributional Semantics
Karolina StaÅczak, Kevin Du, Adina Williams +2
How much meaning influences gender assignment across languages is an active area of research in linguistics and cognitive science. We can view current approaches as aiming to deter…
Efficiently Computing Susceptibility to Context in Language Models
Tianyu Liu, Kevin Du, Mrinmaya Sachan +1
One strength of modern language models is their ability to incorporate information from a user-input context when answering queries. However, they are not equally sensitive to the…