AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
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LongLoRA
An efficient LoRA-based fine-tuning recipe for extending LLM context windows without expensive full fine-tuning.
Struc-Bench (LLMs for Structured Data)
Studies how LLMs handle complex structured-data generation and proposes a structure-aware fine-tuning method.
Textbooks Are All You Need II (phi-1.5)
Microsoft's phi-1.5 demonstrates that a 1.3B model trained on "textbook-quality" synthetic data rivals much larger models on reasoning.
Radiology-Llama 2
A Llama 2-based LLM specialized for radiology report generation.
Transformers as Support Vector Machines
A theoretical paper establishing a formal connection between self-attention optimization and hard-margin SVM problems.
Explaining Grokking
DeepMind advances our understanding of grokking, predicting and confirming two novel phenomena that test their theory.
FLM-101B
A 101B parameter open LLM trainable on a $100K budget through a growth-based training strategy.
SAM-Med2D
Adapts the Segment Anything Model (SAM) to 2D medical imaging through large-scale medical fine-tuning.
Survey on Instruction Tuning for LLMs
A comprehensive survey of instruction tuning covering methodology, dataset construction, and applications.
Prompt2Model
CMU's Prompt2Model automates the path from a natural-language task description to a deployable small special-purpose model.
Platypus
Platypus is a family of fine-tuned and merged LLMs that topped the Open LLM Leaderboard in August 2023.
Teach LLMs to Personalize
A multitask-learning approach for personalized text generation without relying on predefined user attributes.
Synthetic Data Reduces Sycophancy
Google shows that fine-tuning on simple synthetic data can significantly reduce LLM sycophancy.
AutoRobotics-Zero
Discovers zero-shot adaptable robot policies from scratch, including the automatic discovery of Python control code.
Foundation Models in Vision
A comprehensive survey on foundational models for computer vision and their open research directions.
LoraHub
Enables efficient cross-task generalization via dynamic LoRA composition.
Llama 2
Meta's open-weight foundation model family with chat-tuned variants ranging from 7B to 70B parameters.
CM3Leon
Meta's retrieval-augmented multi-modal language model that generates both text and images.
Generative Pretraining in Multimodality (Emu)
A transformer-based multimodal foundation model for generating images and text.
CodeGen2.5
Salesforce's new 7B code LLM trained on 1.5T tokens and optimized for fast sampling.
Extending Context Window of LLMs (PI)
Position Interpolation extends LLaMA's context to 32K with minimal fine-tuning (within 1000 steps).
Visual Navigation Transformer (ViNT)
A foundation model for vision-based robotic navigation built on flexible Transformers.
Textbooks Are All You Need (phi-1)
Introduces a 1.3B parameter code LLM trained on textbook-quality data.
ClinicalGPT
A language model optimized through extensive and diverse medical data and multi-turn dialogue.