SOTAVerified

Language Modelling

A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.

Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.

Source: Wikipedia

Papers

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Showing 17101–17150 of 17610 papers

TitleStatusHype
Deliberation in Latent Space via Differentiable Cache Augmentation—0
DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR—0
DeLLMa: Decision Making Under Uncertainty with Large Language Models—0
DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing—0
DELPHYNE: A Pre-Trained Model for General and Financial Time Series—0
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products—0
DEMix Layers: Disentangling Domains for Modular Language Modeling—0
Diffusion Language Models Generation Can Be Halted Early—0
Democratizing Reward Design for Personal and Representative Value-Alignment—0
DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion—0
Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System—0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT—0
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering—0
De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks—0
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition—0
Dense Recurrent Neural Network with Attention Gate—0
Dependency Annotation of Ottoman Turkish with Multilingual BERT—0
Dependency-Based Bilingual Language Models for Reordering in Statistical Machine Translation—0
Dependency-Based Decipherment for Resource-Limited Machine Translation—0
Dependency-Based Word Embeddings—0
Dependency grammars as Haskell programs—0
Dependency Language Models for Sentence Completion—0
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures—0
Dependency Parsing with Graph Rewriting—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
Deploying Multi-task Online Server with Large Language Model—0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach—0
DePrompt: Desensitization and Evaluation of Personal Identifiable Information in Large Language Model Prompts—0
DEPT: Decoupled Embeddings for Pre-training Language Models—0
Depth-Gated LSTM—0
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents—0
DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room—0
Derivational Smoothing for Syntactic Distributional Semantics—0
Derivation of Document Vectors from Adaptation of LSTM Language Model—0
Deriving Adjectival Scales from Continuous Space Word Representations—0
Deriving Neural Architectures from Sequence and Graph Kernels—0
Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment—0
Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs—0
DeServe: Towards Affordable Offline LLM Inference via Decentralization—0
Designing and Evaluating Multi-Chatbot Interface for Human-AI Communication: Preliminary Findings from a Persuasion Task—0
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian—0
Design of AI-Powered Tool for Self-Regulation Support in Programming Education—0
Design of an Input Method for Taiwanese Hokkien using Unsupervized Word Segmentation for Language Modeling—0
Design principles of an open-source language modeling microservice package for AAC text-entry applications—0
Designs and Implementations in Neural Network-based Video Coding—0
DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding—0
Detailed Human-Centric Text Description-Driven Large Scene Synthesis—0
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment—0
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified