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← All papersIssue 52 of 176

The week of Mar 26 – Mar 31, 2024

10 papers, hand-picked and summarised.

DBRX

DBRX

Databricks releases DBRX, a 132B-total / 36B-active open Mixture-of-Experts LLM that beats established open models on MMLU, HumanEval, and GSM8K while delivering 2x faster inference than LLaMA2-70B.

01Architecture
Grok-1.5

Grok-1.5

xAI's Grok-1.5 is the successor to the open-weight Grok-1, emphasizing long-context understanding and substantially stronger math, code, and reasoning performance.

02Memory
SEEDS

SEEDS

Google's Scalable Ensemble Envelope Diffusion Sampler (SEEDS) uses diffusion models to generate very large, physically plausible weather-forecast ensembles conditioned on only one or two operational forecasts.

03Multimodal
LLMs on University-Level Physics Coding

LLMs on University-Level Physics Coding

A controlled study pits ChatGPT variants against University of Durham physics students on Python coding assignments, finding that humans still outperform even the strongest prompt-engineered GPT-4.

04Training
Mini-Gemini

Mini-Gemini

Mini-Gemini enhances vision-language models by adding a second high-resolution visual encoder that refines details without increasing the number of visual tokens consumed by the LLM.

05Multimodal
Long-form factuality in LLMs

Long-form factuality in LLMs

Google DeepMind introduces LongFact and SAFE, a prompt set and automated evaluator for judging whether the long-form answers of modern LLMs are actually factual.

06Evaluation
Agent Lumos

Agent Lumos

Lumos is a unified recipe for training open-source LLM agents that separates high-level planning from low-level grounding so each module can be supervised and improved independently.

07Agents
AIOS

AIOS

AIOS treats the LLM as the "brain" of an operating-system kernel for agents, providing scheduling, memory, storage, tool, and access-control services so agent apps can share resources safely.

08Agents
FollowIR

FollowIR

FollowIR is both a benchmark and a training set for teaching retrieval models to follow real-world, instruction-style queries rather than just match keywords.

09Evaluation
LLM2LLM

LLM2LLM

LLM2LLM is an iterative data augmentation scheme where a strong teacher LLM generates new training examples targeted at the specific mistakes a student model makes during fine-tuning.

10Training
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