DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models (decodingtrust.github.io)
DecodingTrust is the Adversarial GLUE Benchmark. DecodingTrust aims at providing a thorough assessment of trustworthiness in GPT models....
Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code (www.microsoft.com)
Here's some preliminary work from Microsoft from 2022 that incorporates OpenAI's Codex model to make NPCs that can interact with the player using natural language instructions. It works by defining an API of functions the bot can use, then having Codex generate function calls in response to the player's instructions....
r/MachineLearning finally received a warning from u/ModCodeOfConduct
The Curse of Recursion: Training on Generated Data Makes Models Forget (arxiv.org)
Stable Diffusion revolutionised image creation from descriptive text. GPT-2, GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of language tasks. ChatGPT introduced such language models to the general public. It is now clear that large language models (LLMs) are here to stay, and will bring about drastic...
Neural Dynamic Image-Based Rendering (kbin.social)
Crosspost from m/ArtificialIntelligence
OC "When modeling a system, it should be as minimally detailed as required to be maximally useful for its intended purpose." (medium.com)
This is an article I wrote in 2019 about modeling and entropy. It's something I consider daily as I work on modeling in the drug discovery space.
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings (arxiv.org)
In several practical applications of federated learning (FL), the clients are highly heterogeneous in terms of both their data and compute resources, and therefore enforcing the same model architecture for each client is very limiting. Moreover, the need for uncertainty quantification and data privacy constraints are often...
Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks (arxiv.org)
Large language models (LLMs) are remarkable data annotators. They can be used to generate high-fidelity supervised training data, as well as survey and experimental data. With the widespread adoption of LLMs, human gold--standard annotations are key to understanding the capabilities of LLMs and the validity of their results....
Meta Releases Open Source MusicGen AI (ai.honu.io)
Code, paper, and online demos available
RWKV: Reinventing RNNs for the Transformer Era (Paper Explained) (kbin.social)
Crosspost from m/ai
[R] Paving the way to efficient architectures: StripedHyena-7B, open source models offering a glimpse into a world beyond Transformers (www.reddit.com)
[R] QuIP#: SOTA 2 bit LLMs (www.reddit.com)
We're pleased to introduce QuIP#, a new SOTA LLM quantization method that uses incoherence processing from QuIP (the paper) & lattices to achieve 2 bit LLMs with near-fp16 performance! Now you can run LLaMA 2 70B on a 24G GPU w/out offloading!...
[R] Chain of Code: Reasoning with a Language Model-Augmented Code Emulator (www.reddit.com)
arXiv: https://arxiv.org/abs/2312.04474...
[R] Gated Linear Attention Transformers with Hardware-Efficient Training (www.reddit.com)
Paper: https://arxiv.org/abs/2312.06635...
[D] Can someone describe how the SSM in Mamba is much different than the concepts in a GRU / LSTM Cell? (www.reddit.com)
[D] Is DPO all you need in RLHF? (www.reddit.com)
[D] Length Generalizability of Transformers. (www.reddit.com)
[R] VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation (www.reddit.com)
Arxiv...
Theoretical Foundations of Graph Neural Networks - Seminar (www.youtube.com)
cross-posted from: slrpnk.net/post/3892266...
