REVIEW 1 cited by
Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Pretrained language models have been used in various natural language processing applications. In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions. Social posts, e.g., on Reddit, are usually long documents. However, there are no domain-specific pretrained models for long-sequence modeling in the mental health domain. This paper conducts domain-specific continued pretraining to capture the long context for mental health. Specifically, we train and release MentalXLNet and MentalLongformer based on XLNet and Longformer. We evaluate the mental health classification performance and the long-range ability of these two domain-specific pretrained models. Our models are released in HuggingFace.
Forward citations
Cited by 1 Pith paper
-
Fluent but Unfeeling: The Emotional Blind Spots of Language Models
On EXPRESS, best LLMs reach only ~31-36% lexical accuracy and ~39-44% basic-emotion-vector accuracy in predicting self-disclosed emotions, with chain-of-thought prompting hurting.
Discussion (0). Sign in to comment.