LLM Hallucinations and Machine Unlearning, with Dr. Abhilasha Ravichander
In this episode of the Women in AI Research Podcast, hosts Jekaterina Novikova and Malikeh Ehghaghi engage with Abhilasha Ravichander https://lasharavichander.github.io/ 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.
CHAPTERS:
00:00 Episode Overview: LLM Hallucinations and Responsible AI
01:23 Introduction to Abhilasha Ravichander
03:24 Navigating Challenges in Research
16:33 Understanding LLM Hallucinations: Definitions and Implications
23:43 Factuality Benchmarks: Innovations in Evaluating LLMs
26:03 Innovative Benchmarks for Hallucination Detection
28:29 Implications of Hallucinations in LLMs
29:06 Atomic Units for Precise Verification
31:29 Classifying Hallucinations: Types A, B, and C
36:28 Mitigating Hallucinations in LLMs
39:14 Future Directions in LLM Architecture and Training
42:48 Tools for Data Control and Transparency
52:20 Machine Unlearning: Concepts and Challenges
59:02 Emerging Research Avenues in AI
01:01:11 Advice for Impactful Research
REFERENCES:
01:24 Abhilasha Ravichander -- Google Scholar profile (https://scholar.google.ca/citations?u...)
25:05 WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries (https://arxiv.org/abs/2407.17468)
25:33 HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (https://arxiv.org/abs/2501.08292)
30:02 FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (https://arxiv.org/abs/2305.14251)
34:05 What's In My Big Data? (https://arxiv.org/abs/2310.20707)
45:16 Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models (https://arxiv.org/abs/2503.12072)
54:15 RESTOR: Knowledge Recovery in Machine Unlearning (https://arxiv.org/abs/2411.00204)
54:39 Model State Arithmetic for Machine Unlearning (https://arxiv.org/abs/2506.20941)
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