46 citations · 54 across the 5 of their papers we have counts for
5 papers
SOCIALITE-LLAMA: An Instruction-Tuned Model for Social Scientific Tasks
Gourab Dey, Adithya V Ganesan, Yash Kumar Lal +6
Social science NLP tasks, such as emotion or humor detection, are required to capture the semantics along with the implicit pragmatics from text, often with limited amounts of trai…
Human Language Modeling
Nikita Soni, Matthew Matero, Niranjan Balasubramanian +1
Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM),…
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment
Matthew Matero, Albert Hung, H. Andrew Schwartz
Recent works have demonstrated ability to assess aspects of mental health from personal discourse. At the same time, pre-trained contextual word embedding models have grown to domi…
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
Matthew Matero, Nikita Soni, Niranjan Balasubramanian +1
Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. Ho…
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula +2
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of e…