Multi-Task Language Understanding ๐Ÿ“ˆ // Composable Interventions ๐Ÿค // ARMT Sets Performance Record ๐Ÿ’ช

Multi-Task Language Understanding ๐Ÿ“ˆ // Composable Interventions ๐Ÿค // ARMT Sets Performance Record ๐Ÿ’ช

Author: Earkind July 10, 2024 Duration: 14:40

The MNLU-Pro dataset is a more robust and challenging massive multi-task language understanding dataset that's tailored to more rigorously benchmark large language models' capabilities.

The Composable Interventions framework allows researchers to study the effects of using multiple interventions on a language model, and the order in which interventions are applied can have a significant impact on their effectiveness.

The MJ-Bench benchmark evaluates the effectiveness of different types of multimodal judges in providing feedback for text-to-image generation models, and the experiments reveal that close-source VLMs generally provide better feedback.

The Associative Recurrent Memory Transformer (ARMT) is an approach that combines transformer self-attention for local context with segment-level recurrence for storage of task-specific information distributed over a long context, and it sets a new performance record in the recent BABILong multi-task long-context benchmark.

Contact:ย ย sergi@earkind.com

Timestamps:

00:34 Introduction

01:32ย MNLU-Pro Release on HuggingFace Datasets

03:48ย Extrinsic Hallucinations in LLMs

04:53ย RouteLLM

06:13 Fake sponsor

08:14ย Composable Interventions for Language Models

09:45ย MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?

11:31ย Associative Recurrent Memory Transformer

13:30 Outro


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