Revolutionizing Intent Detection with MiniLM: Tackling Out-of-Scope Challenges
A new approach using MiniLM embeddings shows promise in enhancing out-of-scope intent detection, outperforming traditional multi-class models.
Read the full articleYou might also wanna read
Breaking Quadratic Barriers: A Non-Attention LLM for Ultra-Long Context Horizons
Article URL: Comments URL: Points: 11 # Comments: 1
Ouro: Looped Language Models That Build Reasoning into Pre-Training Through Latent Space Iteration
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-tr
JavelinGuard: Low-Cost Transformer Architectures for LLM Security
We present JavelinGuard, a suite of low-cost, high-performance model architectures designed for detecting malicious intent in Large Language
Recursive Language Models: A New Approach for Processing Extremely Long Prompts Beyond Standard Context Windows
We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Re
Study reveals why in-context learning fails on complex specification-heavy tasks and how fine-tuning can help
In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations an
Serving MiniMax-M3 for efficient inference: Unlocking 1M-Token Context and Multimodality Without Regrets
How Together served MiniMax-M3 efficiently with KV-block-major sparse attention, paged MSA decode, optimized index scoring, and a Rust-based

Comments
Sign in to join the conversation.
No comments yet. Be the first.