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MINISODE: "LLMs, a Survey"
Manage episode 403264357 series 3536907
Jacob Haimes에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Jacob Haimes 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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
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
19 에피소드
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