Sections

Project List

Understanding Tool Invocation in Tool-Augmented LLMs: Investigate how LLMs decide which external tool or agent to invoke using counterfactual perturbations.
RAG vs. Recall in Fact-Checking: Analyze when fact-checking LLMs rely more on retrieved evidence versus pretrained knowledge to assess the role and effect of evidence
LLMs in robotics for autonomous planning, trajectory optimization, efficient locomotion, and generalization using reinforcement learning (RL). Key areas include language-guided decision-making, hierarchical planning, policy generalization, and adaptive control to enhance robotic autonomy and efficiency. See for example https://deepmind.google/discover/blog/shaping-the-future-of-advanced-robotics/
Fine-tune LLMs for autonomous crowdsourcing tasks, enabling AI agents to independently perform data collection and complex task execution on crowdsourcing platforms, with a focus on improving collaboration and efficiency in data acquisition workflows.
Develop real-time fact-checking systems for video content, leveraging Whisper for speech-to-text transcription and LLMs augmented with Retrieval-Augmented Generation (RAG) techniques for automatic fact verification and misinformation detection.
Use OpenAI Gymnasium to design environments for studying LLM behavior and decision-making in planning tasks, analyzing strategies and performance. cfr. https://dl.acm.org/doi/abs/10.1145/3586183.3606763.
Adapt AlphaGo Zero-style models to tackle LLM challenges, focusing on improving complex reasoning paths and automatic prompt optimization using self-play and reinforcement learning. cfr https://arxiv.org/pdf/1712.01815, https://www.nature.com/articles/nature16961, https://www.nature.com/articles/s41586-021-03819-2.
Apply LLMs to Information Retrieval (IR) problems, evaluating their effectiveness in query understanding, document ranking, and relevance prediction to improve retrieval performance and user satisfaction.
Use LLMs and optimization techniques to generate novel problems, emphasizing creativity and diversity in formulations for applications in combinatorial optimization.
Engineer LLM-based real-time conversational agents for healthcare applications, incorporating sentiment analysis and adaptive tone adjustments to enhance patient interactions and support.