Published:
The objective of this research is to design and develop a novel human-AI collaborative framework that can braid the Overlapping, Unique, and Conflicting information from a pair of alternative narratives into a single coherent summary.
Published:
This is an ongoing project in my lab, where we are developing a “Meta-Conversation Framework” to create dialog-based interactive laboratory experiences for middle school science students and teachers in the context of simulation-based science experiments.
Published:
This is an ongoing project where we develop conversational AI techniques by prompting Large Language Models (LLMs) to build a natural language interface between humans and the AutoML tools (e.g., Scikit-Learn), which, in turn, can facilitate acquiring new predictive skills via self-directed learning. To achieve this, we recently introduced a new prompting taxonomy called TeLER to design diverse prompts that can unleash the full potential of LLMs with proper knowledge grounding.
Published:
This ongoing project aims to develop a novel metric to quantify an author’s influence in the narrative braiding process by comparing the final braided narrative against the individual author’s contribution in terms of the Overlap-Unique-Conflict clauses extracted by the CAMPeN framework.
Published:
In this project, our main goal is to investigate how to make NLP/IR evaluation metrics more utility-centric and robust.
Published:
The objective of this research is to design and develop a novel human-AI collaborative framework that can braid the Overlapping, Unique, and Conflicting information from a pair of alternative narratives into a single coherent summary.
Published:
This is an ongoing project in my lab, where we are developing a “Meta-Conversation Framework” to create dialog-based interactive laboratory experiences for middle school science students and teachers in the context of simulation-based science experiments.
Published:
This is an ongoing project where we develop conversational AI techniques by prompting Large Language Models (LLMs) to build a natural language interface between humans and the AutoML tools (e.g., Scikit-Learn), which, in turn, can facilitate acquiring new predictive skills via self-directed learning. To achieve this, we recently introduced a new prompting taxonomy called TeLER to design diverse prompts that can unleash the full potential of LLMs with proper knowledge grounding.
Published:
This ongoing project aims to develop a novel metric to quantify an author’s influence in the narrative braiding process by comparing the final braided narrative against the individual author’s contribution in terms of the Overlap-Unique-Conflict clauses extracted by the CAMPeN framework.
Published:
In this project, our main goal is to investigate how to make NLP/IR evaluation metrics more utility-centric and robust.