Generative AI Weekly Research Highlights | Mar'24 Part 2

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Summary of Research Papers:

1. Bias and Fairness in Large Language Models: A Survey [https://arxiv.org/pdf/2309.00770.pdf]

2. HILL: A Hallucination Identifier for Large Language Models [https://arxiv.org/pdf/2403.06710.pdf]

3. Instruction Tuning for Large Language Models: A Survey [https://arxiv.org/pdf/2308.10792.pdf]

4. Review of Generative AI Methods in Cybersecurity [https://arxiv.org/pdf/2403.08701.pdf]

5. Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code [https://arxiv.org/pdf/2403.07506.pdf]

6. Tell me the truth: A system to measure the trustworthiness of Large Language Models [https://arxiv.org/ftp/arxiv/papers/24...]

7. Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity [https://arxiv.org/pdf/2401.07348.pdf]

8. MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models [https://arxiv.org/pdf/2311.17600.pdf]

00:00 Intro

00:21 Bias and Fairness in LLMs

00:54 Identifying Hallucinations in LLMs

01:19 Instruction Tuning for LLMs

01:44 Generative AI in Cybersecurity

02:09 LLMs for Code

02:33 Measuring LLM Trustworthiness

02:54 Legal and Regulatory Implications of Generative AI in the EU

03:18 Evaluating Safety in Multimodal LLMs

03:49 End

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