Entity Recognition from Colloquial Text

Extraction of concepts and entities of interest from non-formal texts such as social media posts and informal communication is an important capability for decision support systems in many domains, including healthcare, customer relationship management, and others. Despite the recent advances in training large language models for a variety of natural language processing tasks, the developed models and techniques have mainly focused on formal texts and do not perform as well on colloquial data, which is characterized by a number of distinct challenges. In our research, we focus on the healthcare domain and investigate the problem of symptom recognition from colloquial texts by designing and evaluating several training strategies for BERT-based model fine-tuning. These strategies are distinguished by the choice of the base model, the training corpora, and application of term perturbations in the training data. The best-performing models trained using these strategies outperform the state-of-the-art specialized symptom recognizer by a large margin. Through a series of experiments, we have found specific patterns of model behavior associated with the training strategies we designed. We present design principles for training strategies for effective entity recognition in colloquial texts based on our findings.

covLLM: Large Language Models for COVID-19 Biomedical Literature

The COVID-19 pandemic led to 1.1 million deaths in the United States, despite the explosion of coronavirus research. These new findings are slow to translate to clinical interventions, leading to poorer patient outcomes and unnecessary deaths. One reason is that clinicians, overwhelmed by patients, struggle to keep pace with the rate of new coronavirus literature. A potential solution is developing a tool for evaluating coronavirus literature using large language models (LLMs) -- neural networks that are deployed for natural language processing. LLMs can be used to summarize and extract user-specified information. The greater availability and advancement of LLMs and pre-processed coronavirus literature databases provide the opportunity to assist clinicians in evaluating coronavirus literature through a coronavirus literature specific LLM (covLLM), a tool that directly takes an inputted research article and a user query to return an answer. Using the COVID-19 Open Research Dataset (CORD-19), we produced two datasets: (1) synCovid, which uses a combination of handwritten prompts and synthetic prompts generated using OpenAI, and (2) real abstracts, which contains abstract and title pairs. covLLM was trained with LLaMA 7B as a baseline model to produce three models trained on (1) the Alpaca and synCovid datasets, (2) the synCovid dataset, and (3) the synCovid and real abstract datasets. These models were evaluated by two human evaluators and ChatGPT. Results demonstrate that training covLLM on the synCovid and abstract pairs datasets performs competitively with ChatGPT and outperforms covLLM trained primarily using the Alpaca dataset.