Best AI papers explained

A podcast by Enoch H. Kang

526 Episodes

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning
Published: 25/05/2025
Understanding Best-of-N Language Model Alignment
Published: 25/05/2025
Maximizing Acquisition Functions for Bayesian Optimization - and its relation to Gradient Descent
Published: 24/05/2025
Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models
Published: 24/05/2025
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Published: 24/05/2025
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Published: 24/05/2025
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Published: 24/05/2025
Automated Social Science: A Structural Causal Model-Based Approach
Published: 24/05/2025
Causal Interpretation of Transformer Self-Attention
Published: 24/05/2025
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Published: 24/05/2025
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Published: 24/05/2025
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Published: 24/05/2025
Prompts from Reinforcement Learning (PRL)
Published: 24/05/2025
Logits are All We Need to Adapt Closed Models
Published: 24/05/2025
Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Published: 23/05/2025
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Published: 23/05/2025
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Published: 23/05/2025
LLM In-Context Learning as Kernel Regression
Published: 23/05/2025
Personalizing LLMs via Decode-Time Human Preference Optimization
Published: 23/05/2025
Almost Surely Safe LLM Inference-Time Alignment
Published: 23/05/2025

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526 Episodes

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning

Understanding Best-of-N Language Model Alignment

Maximizing Acquisition Functions for Bayesian Optimization - and its relation to Gradient Descent

Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation

The Parallel Knowledge Gradient Method for Batch Bayesian Optimization

FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch

Automated Social Science: A Structural Causal Model-Based Approach

Causal Interpretation of Transformer Self-Attention

A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation

Prompts from Reinforcement Learning (PRL)

Logits are All We Need to Adapt Closed Models

Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

Inference-Time Intervention: Eliciting Truthful Answers from a Language Model

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