5/ Augmenting LLMs with Databases – combines an LLM with a set of SQL databases, enabling a symbolic memory framework; completes tasks via LLM generating SQL instructions that manipulate the DB autonomously.
@dair_ai
-
Sparse-Quantized Representation enables 4.75-bit LLM inference
By
–
3/ Sparse-Quantized Representation – a new compressed format and quantization technique that enables near-lossless compression of LLMs across model scales; “allows LLM inference at 4.75 bits with a 15% speedup”.
-
AlphaDev Discovers Faster Sorting Algorithms via Reinforcement Learning
By
–
2/ AlphaDev – a deep reinforcement learning agent which discovers faster sorting algorithms from scratch; the algorithms outperform previously known human benchmarks and have been integrated into the LLVM C++ library.
-
Dense Motion Estimation Method Tracks Pixels Across Full Videos
By
–
1/ Tracking Everything Everywhere All at Once – propose a test-time optimization method for estimating dense and long-range motion; enables accurate, full-length motion estimation of every pixel in a video.https://t.co/7O3Z0Em7wE
— DAIR.AI (@dair_ai) 11 juin 20231/ Tracking Everything Everywhere All at Once – propose a test-time optimization method for estimating dense and long-range motion; enables accurate, full-length motion estimation of every pixel in a video.
-
Top ML Papers Week: AlphaDev, MusicGen, RLHF Advances
By
–
Top ML Papers of the Week (June 5-11): – AlphaDev
– MusicGen
– Fine-Grained RLHF
– Humor in ChatGPT
– Concept Scrubbing in LLM
– Augmenting LLMs with Databases
… -
CodeTF: Open-Source Transformer Library for Code LLMs
By
–
10/ CodeTF – an open-source Transformer library for state-of-the-art code LLMs; supports pretrained code LLMs and popular code benchmarks, including standard methods to train and serve code LLMs efficiently.
-
SQL-PaLM: LLM-based Text-to-SQL Achieves State-of-the-Art
By
–
9/ SQL-PaLM – an LLM-based Text-to-SQL adopted from PaLM-2; achieves SoTA in both in-context learning and fine-tuning settings; the few-shot model outperforms the previous fine-tuned SoTA by 3.8% on the Spider benchmark.
-
Direct Preference Optimization: Training LLMs Without RLHF
By
–
8/ Direct Preference Optimization – while helpful to train safe & useful LLMs, RLHF can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.https://t.co/DZ0GSarfuT
— DAIR.AI (@dair_ai) 4 juin 20238/ Direct Preference Optimization – while helpful to train safe & useful LLMs, RLHF can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.
-
MERT: Self-Supervised Acoustic Music Understanding Model
By
–
6/ MERT – an acoustic music understanding model with large-scale self-supervised training; it incorporates a superior combination of teacher models to outperform conventional speech and audio approaches.
-
Bytes Are All You Need: Direct File Classification with Transformers
By
–
7/ Bytes Are All You Need – investigates performing classification directly on file bytes, without needing to decode files at inference time; achieves ImageNet Top-1 accuracy of 77.33% using a transformer backbone;