Best AI papers explained

Podcast autorstwa Enoch H. Kang

523 Odcinki

Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models
Opublikowany: 24.05.2025
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Opublikowany: 24.05.2025
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Opublikowany: 24.05.2025
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch
Opublikowany: 24.05.2025
Automated Social Science: A Structural Causal Model-Based Approach
Opublikowany: 24.05.2025
Causal Interpretation of Transformer Self-Attention
Opublikowany: 24.05.2025
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Opublikowany: 24.05.2025
Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs
Opublikowany: 24.05.2025
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
Opublikowany: 24.05.2025
Prompts from Reinforcement Learning (PRL)
Opublikowany: 24.05.2025
Logits are All We Need to Adapt Closed Models
Opublikowany: 24.05.2025
Large Language Models Are (Bayesian) Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Opublikowany: 23.05.2025
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
Opublikowany: 23.05.2025
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Opublikowany: 23.05.2025
LLM In-Context Learning as Kernel Regression
Opublikowany: 23.05.2025
Personalizing LLMs via Decode-Time Human Preference Optimization
Opublikowany: 23.05.2025
Almost Surely Safe LLM Inference-Time Alignment
Opublikowany: 23.05.2025
Survey of In-Context Learning Interpretation and Analysis
Opublikowany: 23.05.2025
From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
Opublikowany: 23.05.2025
LLM In-Context Learning as Kernel Regression
Opublikowany: 23.05.2025

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523 Odcinki

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

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