In this episode of the Women in AI Research Podcast, hosts Jekaterina Novikova and Malikeh Ehghaghi engage with Abhilasha Ravichander to discuss the complexities of LLM hallucinations, the development of factuality benchmarks, and the importance of data transparency and machine unlearning in AI. The conversation also delves into personal experiences in academia and the future directions of research in responsible AI.

REFERENCES:

• Abhilasha Ravichander -- Google Scholar profile
• WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries
• HALoGEN: Fantastic LLM Hallucinations and Where to Find Them
• FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
• What's In My Big Data?
• Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models
• RESTOR: Knowledge Recovery in Machine Unlearning
• Model State Arithmetic for Machine Unlearning

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