2 citations · 3 across the 3 of their papers we have counts for
3 papers
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
Alberto Mario Ceballos Arroyo, Monica Munnangi, Jiuding Sun +4
Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are p…
On-the-fly Definition Augmentation of LLMs for Biomedical NER
Monica Munnangi, Sergey Feldman, Byron C Wallace +3
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In th…
Expanding Subjective Lexicons for Social Media Mining with Embedding Subspaces
Silvio Amir, Rámon Astudillo, Wang Ling +2
Recent approaches for sentiment lexicon induction have capitalized on pre-trained word embeddings that capture latent semantic properties. However, embeddings obtained by optimizin…