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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12451–12500 of 17610 papers

TitleStatusHype
Causal Distillation for Language ModelsCode1
Representation Learning for Conversational Data using Discourse Mutual Information Maximization—0
Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation—0
Controllable Response Generation for Assistive Use-cases—0
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts—0
Multitask Finetuning for Improving Neural Machine Translation in Indian Languages—0
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach—0
Probing Linguistic Information For Logical Inference In Pre-trained Language ModelsCode0
Prompt-free and Efficient Language Model Fine-Tuning—0
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding—0
InfoLM: A New Metric to Evaluate Summarization & Data2Text GenerationCode1
DenseCLIP: Language-Guided Dense Prediction with Context-Aware PromptingCode1
An End-to-End Speech Recognition for the Nepali Language—0
An Investigation of Hybrid architectures for Low Resource Multilingual Speech Recognition system in Indian context—0
MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)—0
Cross-Domain Language Modeling: An Empirical Investigation—0
Interactive Model with Structural Loss for Language-based Abductive Reasoning—0
Building astroBERT, a language model for Astronomy & Astrophysics—0
DPRK-BERT: The Supreme Language Model—0
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging—0
Searching for Efficient Transformers for Language Modeling—0
Multilingual Pre-training with Universal Dependency Learning—0
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network ModelsCode1
Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding—0
A Simple Long-Tailed Recognition Baseline via Vision-Language ModelCode1
Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat—0
PSG: Prompt-based Sequence Generation for Acronym Extraction—0
Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question MatchingCode0
ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic ArithmeticCode1
FastTrees: Parallel Latent Tree-Induction for Faster Sequence EncodingCode0
Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer ModelsCode0
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions—0
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language ModelCode1
Transformer-based Korean Pretrained Language Models: A Survey on Three Years of Progress—0
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect—0
Evaluating the Robustness of Retrieval Pipelines with Query Variation GeneratorsCode1
Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets—0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart HomesCode0
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
Romanian Speech Recognition Experiments from the ROBIN ProjectCode1
Enhancing Multilingual Language Model with Massive Multilingual Knowledge TriplesCode1
Modelling Direct Messaging Networks with Multiple Recipients for Cyber Deception—0
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning—0
DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding SharingCode2
ClipCap: CLIP Prefix for Image CaptioningCode2
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model—0
RoBERTuito: a pre-trained language model for social media text in SpanishCode1
Linking-Enhanced Pre-Training for Table Semantic Parsing—0
Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application—0
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding MatchingCode1
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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