Recurrent neural networks (RNNs) have achieved state-of-the-art performances in many natural language processing tasks, such as language modeling and machine translation. However, when the vocabulary is large, the RNN model will become very big (e.g., possibly beyond the memory capacity of a GPU device) and its training will become very inefficient. In this work, we propose a novel technique to tackle this challenge. The key idea is to use 2-Component (2C) shared embedding for word representations. We allocate every word in the vocabulary into a table, each row of which is associated with a vector, and each column associated with another vector.

Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space.

We focus on categorical attributes of language. Examples of such attributes include sentiment, language complexity, tense, voice, honorifics, mood, etc. Our approach draws inspiration from styletransfer methods inthevision andlanguage literature.

The AI search startup once predicted advertising would be a massive business. Perplexity is abandoning plans to put ads in its AI search product as the industry looks for sustainable business models that won't hurt user trust. The changes are part of a larger strategic shift for the company, which has long focused on disrupting Google Search's business. Google is changing to be like Perplexity more than Perplexity is trying to take on Google, said a Perplexity executive at a press briefing on Tuesday. Executives spoke to the press on the condition of anonymity.

Large Language Models (LLMs) have long held sway in the realm s of artificial intelligence research. Numerous efficient techniques, inc luding weight pruning, quantization, and distillation, have been embraced to comp ress LLMs, targeting memory reduction and inference acceleration, which unders core the redundancy in LLMs. However, most model compression techniques concen trate on weight optimization, overlooking the exploration of optimal arch itectures. Besides, traditional architecture search methods, limited by the eleva ted complexity with extensive parameters, struggle to demonstrate their effecti veness on LLMs. In this paper, we propose a training-free architecture search fram ework to identify optimal subnets that preserve the fundamental strengths of the o riginal LLMs while achieving inference acceleration. Furthermore, after gen erating subnets that inherit specific weights from the original LLMs, we introduce a reformation algorithm that utilizes the omitted weights to rectify the inher ited weights with a small amount of calibration data. Compared with SOT A training-fr ee structured pruning works that can generate smaller networks, our method dem onstrates superior performance across standard benchmarks. Furthermore, our generated subnets can directly reduce the usage of GPU memory and achieve infer ence acceleration.

This renders them impractical to use in a wide range of use-cases, limiting who can benefit from them to a handful of big corporations. As an attempt to mitigate this issue, Touvron et al.

Many applications today provide users with multiple auto-complete drafts as they type, including GitHub's code completion, Gmail's smart compose, and Apple's messaging auto-suggestions.