Best AI papers explained

En podcast av Enoch H. Kang

550 Avsnitt

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

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Cut through the noise. We curate and break down the most important AI papers so you don’t have to.

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550 Avsnitt

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