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Found 19 papers, 11 papers with code
Autoregressive + Chain of Thought (CoT) $\simeq$ Recurrent: Recurrence's Role in Language Models and a Revist of Recurrent Transformer
Moreover, we revisit recent recurrent-based Transformer model designs, focusing on their computational abilities through our proposed concept of ``recurrence-completeness" and identify key theoretical limitations in models like Linear Transformer and RWKV.
Benchmarking Spectral Graph Neural Networks: A Comprehensive Study on Effectiveness and Efficiency
With the recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their specialty in capturing graph signals in the frequency domain, demonstrating promising capability in specific tasks.
Predicting Cascading Failures with a Hyperparametric Diffusion Model
In this paper, we study cascading failures in power grids through the lens of information diffusion models.
OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification Inference
Image classification is a fundamental building block for a majority of computer vision applications.
Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size, while smaller models that can be deployed on lower cost (e. g., edge) devices, tend to lag behind in terms of response quality.
GreenLLaMA: A Framework for Detoxification with Explanations
We then train a suite of detoxification models with our cross-platform corpus.
LLM Performance Predictors are good initializers for Architecture Search
In this work, we utilize Large Language Models (LLMs) for a novel use case: constructing Performance Predictors (PP) that estimate the performance of specific deep neural network architectures on downstream tasks.
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation
Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning.
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
In NLP tasks like machine translation and pre-trained language modeling, there is a significant performance gap between supernet and training from scratch for the same model architecture, necessitating retraining post optimal architecture identification.
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning
The prevalence of abusive language on different online platforms has been a major concern that raises the need for automated cross-platform abusive language detection.
A Benchmark Study of Contrastive Learning for Arabic Social Meaning
Contrastive learning (CL) brought significant progress to various NLP tasks.
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation
Furthermore, existing MoE works do not consider computational constraints (e. g., FLOPs, latency) to guide their design.
Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints
In this work, we study the more challenging open-domain setting consisting of low frequency user prompt patterns (or broad prompts, e. g., prompt about 93rd academy awards) and demonstrate the effectiveness of character-based language models.
Automatic Detection of Entity-Manipulated Text using Factual Knowledge
We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article.
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing
We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs.
Maximizing Social Welfare in a Competitive Diffusion Model
Competitive IM focuses on the propagation of competing items in the network.
Automatic Detection of Machine Generated Text: A Critical Survey
Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs.
Refutations on "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study"
In a recent SIGMOD paper titled "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", Arora et al. [1] undertake a performance benchmarking study of several well-known algorithms for influence maximization.
Social and Information Networks
