NLP Project: Beyond Words: Enhancing Reasoning in Entity Tracking
Researched and wrote a paper evaluating the impact of fine-tuning reasoning models on entity tracking, using a T5-base model with a focus on mathematical and computational reasoning tasks. The study compares performance across various datasets: general knowledge, coding, and math. Results indicate that models trained on coding datasets excel in entity tracking, while those on math datasets struggle with unfamiliar symbols.