@_akhaliq - AI Dynamics - Page 140 of 145

Fine-Grained Human Feedback Improves Language Model Reward Training

By

–

05 June 2023 5h41

Fine-Grained Human Feedback Gives Better Rewards for Language Model Training paper page: https://
huggingface.co/papers/2306.01
693
… use fine-grained human feedback (e.g., which sentence is false, which sub-sentence is irrelevant) as an explicit training signal. We introduce Fine-Grained RLHF, a

→ View original post on X — @_akhaliq

5 June 2023

Large Language Models for Private Synthetic Text Generation

By

@_akhaliq

–

05 June 2023 5h19

Harnessing large-language models to generate private synthetic text paper page: https://
huggingface.co/papers/2306.01
684
… Differentially private (DP) training methods like DP-SGD can protect sensitive training data by ensuring that ML models will not reveal private information. An alternative

→ View original post on X — @_akhaliq

5 June 2023

Responsible Task Automation Empowering Large Language Models

By

@_akhaliq

–

05 June 2023 5h11

Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators paper page: https://
huggingface.co/papers/2306.01
242
… The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a

→ View original post on X — @_akhaliq

5 June 2023

Faster Causal Attention for Long Sequences with Sparse Flash Attention

By

@_akhaliq

–

05 June 2023 5h06

Faster Causal Attention Over Large Sequences Through Sparse Flash Attention paper page: https://
huggingface.co/papers/2306.01
160
… Transformer-based language models have found many diverse applications requiring them to process sequences of increasing length. For these applications, the causal

→ View original post on X — @_akhaliq

5 June 2023

RefinedWeb Dataset: High-Quality Web Data for Falcon LLM Training

By

@_akhaliq

–

05 June 2023 5h04

The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only paper page: https://
huggingface.co/papers/2306.01
116
… Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media

→ View original post on X — @_akhaliq

5 June 2023

Retrieval Augmented Language Models: Reality Check and Comparison

By

@_akhaliq

–

05 June 2023 5h02

Reimagining Retrieval Augmented Language Models for Answering Queries paper page: https://
huggingface.co/papers/2306.01
061
… present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric,

→ View original post on X — @_akhaliq

5 June 2023

CheckMate: Interactive Platform for Evaluating Language Models in Mathematics

By

@_akhaliq

–

05 June 2023 3h49

Evaluating Language Models for Mathematics through Interactions paper page: https://
huggingface.co/papers/2306.01
694
… introduce CheckMate, an adaptable prototype platform for humans to interact with and evaluate LLMs. We conduct a study with CheckMate to evaluate three language

→ View original post on X — @_akhaliq

5 June 2023

Empirical Study on GPT-4 Performance in Challenging Math Problem Solving

By

@_akhaliq

–

05 June 2023 3h43

An Empirical Study on Challenging Math Problem Solving with GPT-4 paper page: https://
huggingface.co/papers/2306.01
337
… Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural

→ View original post on X — @_akhaliq

5 June 2023