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Take a trip with me through the paper Large Language Models, A Survey, published on February 9th of 2024. All figures and tables mentioned throughout the episode can be found on the Into AI Safety podcast website.
00:36 - Intro and authors01:50 - My takes and paper structure04:40 - Getting to LLMs07:27 - Defining LLMs & emergence12:12 - Overview of PLMs15:00 - How LLMs are built18:52 - Limitations if LLMs23:06 - Uses of LLMs25:16 - Evaluations and Benchmarks28:11 - Challenges and future directions29:21 - Recap & outro
Links to all articles/papers which are mentioned throughout the episode can be found below, in order of their appearance.Large Language Models, A SurveyMeysam's LinkedIn PostClaude E. ShannonA symbolic analysis of relay and switching circuits (Master's Thesis)Communication theory of secrecy systemsA mathematical theory of communicationPrediction and entropy of printed EnglishFuture ML Systems Will Be Qualitatively DifferentMore Is DifferentSleeper Agents: Training Deceptive LLMs that Persist Through Safety TrainingAre Emergent Abilities of Large Language Models a Mirage?Are Emergent Abilities of Large Language Models just In-Context Learning?Attention is all you needDirect Preference Optimization: Your Language Model is Secretly a Reward ModelKTO: Model Alignment as Prospect Theoretic OptimizationOptimization by Simulated AnnealingMemory and new controls for ChatGPTHallucinations and related concepts—their conceptual background
Show Notes
Take a trip with me through the paper Large Language Models, A Survey, published on February 9th of 2024. All figures and tables mentioned throughout the episode can be found on the Into AI Safety podcast website.
00:36 - Intro and authors
01:50 - My takes and paper structure
04:40 - Getting to LLMs
07:27 - Defining LLMs & emergence
12:12 - Overview of PLMs
15:00 - How LLMs are built
18:52 - Limitations if LLMs
23:06 - Uses of LLMs
25:16 - Evaluations and Benchmarks
28:11 - Challenges and future directions
29:21 - Recap & outro
01:50 - My takes and paper structure
04:40 - Getting to LLMs
07:27 - Defining LLMs & emergence
12:12 - Overview of PLMs
15:00 - How LLMs are built
18:52 - Limitations if LLMs
23:06 - Uses of LLMs
25:16 - Evaluations and Benchmarks
28:11 - Challenges and future directions
29:21 - Recap & outro
Links to all articles/papers which are mentioned throughout the episode can be found below, in order of their appearance.
- Large Language Models, A Survey
- Meysam's LinkedIn Post
- Claude E. Shannon
- Future ML Systems Will Be Qualitatively Different
- More Is Different
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
- Are Emergent Abilities of Large Language Models a Mirage?
- Are Emergent Abilities of Large Language Models just In-Context Learning?
- Attention is all you need
- Direct Preference Optimization: Your Language Model is Secretly a Reward Model
- KTO: Model Alignment as Prospect Theoretic Optimization
- Optimization by Simulated Annealing
- Memory and new controls for ChatGPT
- Hallucinations and related concepts—their conceptual background
Creators & Guests
What is Into AI Safety?
The Into AI Safety podcast aims to make it easier for everyone, regardless of background, to get meaningfully involved with the conversations surrounding the rules and regulations which should govern the research, development, deployment, and use of the technologies encompassed by the term "artificial intelligence" or "AI"
For better formatted show notes, additional resources, and more, go to https://into-ai-safety.github.io
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