In this episode, Vishnu and Alex reflect on Wisecube’s 8-year journey and over 15 years of experience in AI and NLP. From pioneering search engines using TF-IDF to building knowledge graphs (Orpheus) and addressing LLM reliability with Pythia, they explore key milestones in AI development and the evolution of NLP. The conversation includes insights on medical coding (CAC), drug discovery, the ELIZA effect, and real-world applications in healthcare and research. They also discuss Wisecube’s recent acquisition by John Snow Labs and its implications for the future of NLP and AI in healthcare.

Available on:

• ⁠⁠YouTube⁠⁠: https://youtube.com/@WisecubeAI/podcasts

• ⁠⁠Apple Podcast⁠⁠: https://apple.co/4kPMxZf

• ⁠⁠Spotify⁠⁠: https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55

• ⁠⁠Amazon Music⁠⁠: https://bit.ly/4izpdO2

#AI #NLP #LLM #MachineLearning #KnowledgeGraphs #ArtificialIntelligence #DataScience #HealthcareAI #StartupJourney

In this episode of Gradient Descent, we explore two competing approaches to fine-tuning LLMs: Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). Dive into the mechanics of RLHF, its computational challenges, and how DPO simplifies the process by eliminating the need for a separate reward model. We also discuss supervised fine-tuning, emerging methods like Identity Preference Optimization (IPO) and Kahneman-Tversky Optimization (KTO), and their real-world applications in models like Llama 3 and Mistral. Learn practical LLM optimization strategies, including task modularization to boost performance without extensive fine-tuning.

Timestamps:

Intro - 0:00

Overview of LLM Fine-Tuning - 00:48

Deep Dive into RLHF - 02:46

Supervised Fine-Tuning vs. RLHF - 10:38

DPO and Other RLHF Alternatives - 14:43

Real-World Applications in Frontier Models - 22:23

Practical Tips for LLM Optimization - 25:18

Closing Thoughts - 36:05

References:

[1] Training language models to follow instructions with human feedback https://arxiv.org/abs/2203.02155

[2] Direct Preference Optimization: Your Language Model is Secretly a Reward Model https://arxiv.org/abs/2305.18290

[3] Hugging Face Blog on DPO: Simplifying Alignment: From RLHF to Direct Preference Optimization (DPO) https://huggingface.co/blog/ariG23498/rlhf-to-dpo

[4] Comparative Analysis: RLHF and DPO Compared https://crowdworks.blog/en/rlhf-and-dpo-compared/

[5] YouTube Explanation: How to fine-tune LLMs directly without reinforcement learning https://www.youtube.com/watch?v=k2pD3k1485A

Listen on:

• Apple Podcasts:

https://podcasts.apple.com/us/podcast/gradient-descent-podcast-about-ai-and-data/id1801323847

• Spotify:

https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55

• Amazon Music:

https://music.amazon.com/podcasts/79f6ed45-ef49-4919-bebc-e746e0afe94c/gradient-descent---podcast-about-ai-and-data

• YouTube: https://youtube.com/@WisecubeAI/podcasts

Our solutions:

- https://askpythia.ai/ - LLM Hallucination Detection Tool

- https://www.wisecube.ai - Wisecube AI platform for large-scale biomedical knowledge analysis

Follow us:

- Pythia Website: https://askpythia.ai/

- Wisecube Website: https://www.wisecube.ai

- LinkedIn: https://www.linkedin.com/company/wisecube/

- Facebook: https://www.facebook.com/wisecubeai

- Twitter: https://x.com/wisecubeai

- Reddit: https://www.reddit.com/r/pythia/

- GitHub: https://github.com/wisecubeai

#FineTuning #LLM #RLHF #AI #MachineLearning #AIDevelopment

Explore the evolution of prompt engineering in this episode of Gradient Descent. Manual prompt tuning — slow, brittle, and hard to scale — is giving way to DSPy, a framework that turns LLM prompting into a structured, programmable, and optimizable process.

Learn how DSPy’s modular approach — with Signatures, Modules, and Optimizers — enables LLMs to tackle complex tasks like multi-hop reasoning and math problem solving, achieving accuracy comparable to much larger models. We also dive into real-world examples, optimization strategies, and why the future of prompting looks a lot more like programming.

Listen to our podcast on these platforms:

• YouTube: https://youtube.com/@WisecubeAI/podcasts

• Apple Podcasts: https://apple.co/4kPMxZf

• Spotify: https://open.spotify.com/show/1nG58pwg2Dv6oAhCTzab55

• Amazon Music: https://bit.ly/4izpdO2

Mentioned Materials:

• DSPy Paper - https://arxiv.org/abs/2310.03714

• DSPy official site - https://dspy.ai/

• DSPy GitHub - https://github.com/stanfordnlp/dspy

• LLM abstractions guide - https://www.twosigma.com/articles/a-guide-to-large-language-model-abstractions/

Our solutions:

- https://askpythia.ai/ - LLM Hallucination Detection Tool

- https://www.wisecube.ai - Wisecube AI platform for large-scale biomedical knowledge analysis

Follow us:

- Pythia Website: https://askpythia.ai/

- Wisecube Website: https://www.wisecube.ai

- LinkedIn: https://www.linkedin.com/company/wisecube/

- Facebook: https://www.facebook.com/wisecubeai

- Twitter: https://x.com/wisecubeai

- Reddit: https://www.reddit.com/r/pythia/

- GitHub: https://github.com/wisecubeai

#AI #PromptEngineering #DSPy #MachineLearning #LLM #ArtificialIntelligence #AIdevelopment