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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13851–13900 of 17610 papers

TitleStatusHype
Representation and Bias in Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling—0
On the use of linguistic similarities to improve Neural Machine Translation for African Languages—0
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing—0
Syntactic Relevance XLNet Word Embedding Generation in Low-Resource Machine Translation—0
Not All Memories are Created Equal: Learning to ExpireCode1
Universal Sentence Representations Learning with Conditional Masked Language Model—0
Transformer protein language models are unsupervised structure learners—0
DACT-BERT: Increasing the efficiency and interpretability of BERT by using adaptive computation time.—0
The Pile: An 800GB Dataset of Diverse Text for Language ModelingCode2
Unified Mandarin TTS Front-end Based on Distilled BERT ModelCode1
Verb Knowledge Injection for Multilingual Event Processing—0
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders—0
Directed Beam Search: Plug-and-Play Lexically Constrained Language GenerationCode0
ERNIE-Doc: A Retrospective Long-Document Modeling TransformerCode0
AraGPT2: Pre-Trained Transformer for Arabic Language GenerationCode1
CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse RelationsCode0
AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingCode1
Shortformer: Better Language Modeling using Shorter InputsCode1
Studying Strategically: Learning to Mask for Closed-book QA—0
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration—0
SemGloVe: Semantic Co-occurrences for GloVe from BERTCode0
Reservoir Transformers—0
Can Sequence-to-Sequence Models Crack Substitution Ciphers?—0
ECONET: Effective Continual Pretraining of Language Models for Event Temporal ReasoningCode1
Enhancing Pre-trained Language Model with Lexical Simplification—0
Generating Query Focused Summaries from Query-Free ResourcesCode1
Generating Adversarial Examples in Chinese Texts Using Sentence-Pieces—0
CMV-BERT: Contrastive multi-vocab pretraining of BERT—0
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document UnderstandingCode0
Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning—0
Universal Sentence Representation Learning with Conditional Masked Language Model—0
General Mechanism of Evolution Shared by Proteins and WordsCode0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Contextual Temperature for Language Modeling—0
A Context Aware Approach for Generating Natural Language AttacksCode0
Cross-lingual Universal Dependency Parsing Only from One Monolingual Treebank—0
SubICap: Towards Subword-informed Image Captioning—0
Code Switching Language Model Using Monolingual Training Data—0
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
Pre-Training a Language Model Without Human Language—0
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition—0
RealFormer: Transformer Likes Residual AttentionCode1
An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations—0
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Lexically-constrained Text Generation through Commonsense Knowledge Extraction and Injection—0
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-TrainingCode1
BERT Goes Shopping: Comparing Distributional Models for Product RepresentationsCode1
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration—0
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
A review of on-device fully neural end-to-end automatic speech recognition algorithms—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