Best AI papers explained

Podcast készítő Enoch H. Kang

550 Epizód

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Közzétéve: 2025. 05. 23.
LLM In-Context Learning as Kernel Regression
Közzétéve: 2025. 05. 23.
Personalizing LLMs via Decode-Time Human Preference Optimization
Közzétéve: 2025. 05. 23.
Almost Surely Safe LLM Inference-Time Alignment
Közzétéve: 2025. 05. 23.
Survey of In-Context Learning Interpretation and Analysis
Közzétéve: 2025. 05. 23.
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Közzétéve: 2025. 05. 23.
LLM In-Context Learning as Kernel Regression
Közzétéve: 2025. 05. 23.
Where does In-context Learning Happen in Large Language Models?
Közzétéve: 2025. 05. 23.
Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting
Közzétéve: 2025. 05. 22.
metaTextGrad: Learning to learn with language models as optimizers
Közzétéve: 2025. 05. 22.
Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
Közzétéve: 2025. 05. 22.
Isolated Causal Effects of Language
Közzétéve: 2025. 05. 22.
Sleep-time Compute: Beyond Inference Scaling at Test-time
Közzétéve: 2025. 05. 22.
J1: Incentivizing Thinking in LLM-as-a-Judge
Közzétéve: 2025. 05. 22.
ShiQ: Bringing back Bellman to LLMs
Közzétéve: 2025. 05. 22.
Policy Learning with a Natural Language Action Space: A Causal Approach
Közzétéve: 2025. 05. 22.
Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
Közzétéve: 2025. 05. 22.
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Közzétéve: 2025. 05. 21.
TEXTGRAD: Automatic Differentiation via Text
Közzétéve: 2025. 05. 21.
Steering off Course: Reliability Challenges in Steering Language Models
Közzétéve: 2025. 05. 20.

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550 Epizód

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

LLM In-Context Learning as Kernel Regression

Personalizing LLMs via Decode-Time Human Preference Optimization

Almost Surely Safe LLM Inference-Time Alignment

Survey of In-Context Learning Interpretation and Analysis

From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models

LLM In-Context Learning as Kernel Regression

Where does In-context Learning Happen in Large Language Models?

Auto-Differentiating Any LLM Workflow: A Farewell to Manual Prompting

metaTextGrad: Learning to learn with language models as optimizers

Semantic Operators: A Declarative Model for Rich, AI-based Data Processing

Isolated Causal Effects of Language

Sleep-time Compute: Beyond Inference Scaling at Test-time

J1: Incentivizing Thinking in LLM-as-a-Judge

ShiQ: Bringing back Bellman to LLMs

Policy Learning with a Natural Language Action Space: A Causal Approach

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

TEXTGRAD: Automatic Differentiation via Text

Steering off Course: Reliability Challenges in Steering Language Models