5 papers
Causally Evaluating the Learnability of Formal Language Tasks
Vésteinn Snæbjarnarson, Anej Svete, Josef Valvoda +3
Language models, as multi-task learners, acquire a wide range of abilities during training. A fundamental question is how much task-specific data is needed to learn a given task. A…
Training Neural Networks as Recognizers of Formal Languages
Alexandra Butoi, Ghazal Khalighinejad, Anej Svete +3
Characterizing the computational power of neural network architectures in terms of formal language theory remains a crucial line of research, as it describes lower and upper bounds…
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages
Nadav Borenstein, Anej Svete, Robin Chan +5
What can large language models learn? By definition, language models (LM) are distributions over strings. Therefore, an intuitive way of addressing the above question is to formali…
A Probability--Quality Trade-off in Aligned Language Models and its Relation to Sampling Adaptors
Naaman Tan, Josef Valvoda, Tianyu Liu +4
The relationship between the quality of a string, as judged by a human reader, and its probability, under a language model undergirds the development of better…
HR-Agent: A Task-Oriented Dialogue (TOD) LLM Agent Tailored for HR Applications
Weijie Xu, Jay Desai, Fanyou Wu +2
Recent LLM (Large Language Models) advancements benefit many fields such as education and finance, but HR has hundreds of repetitive processes, such as access requests, medical cla…