Events

Name: Vedant R Bahel
Date: April 30th, 2024
Time: 11 am  
Location: ICCS 104
Supervisor: Cristina Conati

Title: Personalizing explanations of AI hints to users’ cognitive abilities in an intelligent tutoring system 

Abstract:

We investigate personalizing the explanations that an Intelligent Tutoring System generates to justify the hints it provides to students to foster their learning. The personalization targets students with low levels of two long-term traits, the Need for Cognition and Conscientiousness, and aims to enhance these students’ engagement with the explanations, based on prior findings that these students do not naturally engage with the explanations but would benefit from them if they do. To evaluate the effectiveness of the personalization, we conducted a user study where we found that our proposed personalization significantly increases our target users’ interaction with the hint explanations, their understanding of the hints and their learning. Hence, this work provides valuable insights into effectively personalizing AI-driven explanations for cognitively demanding tasks such as learning. This design for personalization was selected from among two distinct designs initially tested against each other, with a separate discussion provided within this thesis. Furthermore, with a long-term vision of personalizing explanations based on users’ short-term states, mainly confusion, we conducted an exploratory study to capture instances of user confusion related to ACSP hints and their interaction with accessing explanations, aiming to investigate users' reliance on hint explanations amidst confusion.

Name: Swati Kanwal
Date: 25th April 2024
Time: 2 - 3 PM
Supervisor: Laks Lakshmanan
Location: Zoom
https://ubc.zoom.us/j/69333584806?pwd=c2dlR0I0TUQxTFVYcXRZaFhwL3IyZz09

Meeting ID: 693 3358 4806
Passcode: 996226

Title: Exploring the potential of LLMs for Biomedical Relation Extraction

Abstract:
Though the current state-of-the-art models for biomedical relation extraction rely on encoder-only LLMs that are pre-trained on domain-specific data and then fine-tuned using large amounts of annotated data, there is a compelling argument to explore decoder-only LLMs and alternative task formulation paradigms to investigate if similar or superior results can be achieved for this task without the need for large amounts of annotated data and computational resources.

Surprisingly, there has been limited exploration of decoder-only LLMs for biomedical relation extraction. This study aims to address this gap, presenting BioREPS, a novel method that enhances decoder-only LLMs through semantic similarity and chain-of-thought prompting, yielding promising outcomes. This study highlights the effectiveness of instruction training and self-generated chain-of-thought prompts in enhancing the reasoning abilities of decoder-only LLMs for biomedical relation extraction. Additionally, this research investigates various task formulation paradigms and the empirical advantages of domain-specific training for biomedical relation extraction through a series of experiments. The results confirm that ”general” decoder-only Language Models (LLMs) hold immense potential for the task of biomedical relation extraction and come close to state-of-the-art performance with a fractional amount of data and no additional training.