Trustworthy LLMs

Section 1: Background: The journey of LLMs

1. Evolution of AI

1.1 History of artificial intelligence till neural networks

1.2 ANN to Asynchronous RNN

1.3 Asynchronous RNN to Encoder-Decoder

1.4 Elite combination of Transformers, Attention Mechanism and Transfer learning


2. Introduction to LLM

2.1 Brief of Language Model

2.2 Large Language Models

2.3 Key Characteristics and capabilities of LLM

2.4 Applications of LLM


3. Transformers: Detailed sequence of steps

3.1 Introduction to transformers

3.2 Architecture of transformers

3.3 Tokenization

3.4 Vector and Positional embedding

3.5 Attention mechanism and Feed Forward mechanism


4. ChatGPT: The Prominent application of LLM

4.1 Introduction to ChatGPT

4.2 Generative Pre-Training Phase

4.3 Supervised Fine-tuning Phase

4.4 Reinforcement Learning through Human Feedback Phase


5. The synergy between Deep Learning and LLM

5.1 Basics of Deep Learning

5.2 Exploitation of deep Learning in LLM

5.3 Real life example

Section 2: The Trust Landscape and Building Trustworthy LLMs

6. Explainable AI

6.1 Introduction to Explainable AI

6.2 Components of Explainable AI

6.3 Trustworthiness from Explainable AI


7. Foundation of Trustworthy LLM

7.1 Introduction to Trustworthy LLM

7.2 Components of Trustworthy LLM

7.3 Application areas


8. The trust Imperative: Why trust matters in AI

8.1 Safety and Reliability

8.2 Ethical Considerations

8.3 Transparency and Explainability


9. Current State of LLM Trust: Gap and challenges

9.1 Lack of Transparency and Explainability

9.2 Potential for Bias and Discrimination

9.3 Concerns Regarding Accuracy and Reliability

9.4 Regulatory and Governance Challenges


10. Dimensions of Trustworthy LLM

10.1 Multifaceted issues of LLM Trust

10.2 Trustfulness, Safety and Fairness

10.3 Machine Ethics, Explainability, and Reasoning


11. A Benchmark for Trustworthy LLM: TrustGPT

11.1 Toxicity of LLMs

11.2 Design of TrustGPT

11.3 Metrics: Toxicity, Bias, Value Alignment

Section 3: Exemplary testing for Dimensions of Trustworthiness

12. Case Study: GPT-4

12.1 Evolution of GPT-4

12.2 Testing for Trustworthiness


13. Case Study: OPT-1.3B

13.1 Evolution of OPT-1.3B

13.2 Testing for Trustworthiness

Section 4: The Future of Trustworthy LLMs

14. Key Challenges in Trustworthy LLM

14.1 Language Bias

14.2 Prompt Sensitivity

14.3 Instruction Following


15. A Vision for a Trustworthy AI Future

15.1 Technical Advancements

15.2 Ethical Guidelines and Regulations

15.3 Education and Awareness

15.4 Collaboration and Partnerships