Generative AI Weekly Research Highlights | Jan'24 Part 3

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1. The What, Why, and How of Context Length Extension Techniques in Large Language Models – A Detailed Survey

[Link](https://arxiv.org/pdf/2401.07872.pdf)

2. AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models

[Link](https://arxiv.org/pdf/2401.09002.pdf)

3. Large Language Models are Null-Shot Learners

[Link](https://arxiv.org/pdf/2401.08273.pdf)

4. Gender Bias in Machine Translation and The Era of Large Language Models

[Link](https://arxiv.org/pdf/2401.10016.pdf)

5. Leveraging Biases in Large Language Models: “Bias-kNN” for Effective Few-Shot Learning

[Link](https://arxiv.org/pdf/2401.09783.pdf)

6. Are Self-Explanations from Large Language Models Faithful?

[Link](https://arxiv.org/pdf/2401.07927.pdf)

7. Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models

[Link](https://arxiv.org/pdf/2312.09669.pdf)

8. Watch Your Language: Investigating Content Moderation with Large Language Models

[Link](https://arxiv.org/pdf/2309.14517.pdf)

00:00 Intro

00:20 Extending LLM Context Length

00:43 Assessing Jailbreak Attacks on LLMs

01:08 Null Shot Prompting: A New Technique

01:27 Tackling Gender Bias in Translation

01:50 BiasKAndN: Leveraging LLM Biases

02:12 Self-Explanations in LLMs

02:30 Silent Guardian: Protecting Text Data

02:53 LLMs in Content Moderation

03:22 End

#generativeai,#promptengineering,#largelanguagemodels,#openai,#chatgpt,#gpt4,#ai,#abcp,#prompt,#responsibleai,#promptengineer,