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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12301–12350 of 17610 papers

TitleStatusHype
Hierarchical Transformers Are More Efficient Language Models—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Feasibility of BERT Embeddings For Domain-Specific Knowledge Mining—0
Cross-Lingual Speaker Identification from Weak Local Evidence—0
Discourse-Aware Prompt Design for Text Generation—0
Causal Distillation for Language Models—0
Efficient Machine Translation Domain Adaptation—0
Efficient Hierarchical Domain Adaptation for Pretrained Language Models—0
Improving Coherence of Language Model Generation with Latent Semantic State—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings—0
Data Augmentation for Biomedical Factoid Question Answering—0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering—0
Breaking Character: Are Subwords Good Enough for MRLs After All?—0
Hardness Masking via Auto-Regressive Language Model—0
EiCi: A New Method of Dynamic Embedding Incorporating Contextual Information in Chinese NER—0
Applying SoftTriple Loss for Supervised Language Model Fine Tuning—0
CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code—0
A Masked Segmental Language Model for Unsupervised Natural Language Segmentation—0
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints—0
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes—0
When More is not Necessary Better: Multilingual Auxiliary Tasks for Zero-Shot Cross-Lingual Transfer of Hate Speech Detection Models—0
Transferring Knowledge from Structure-aware Self-attention Language Model to Sequence-to-Sequence Semantic Parsing—0
Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation—0
MetaICL: Learning to Learn In Context—0
Learning Cross-Lingual IR from an English Retriever—0
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Retrieving Visual Facts For Few-Shot Visual Question Answering—0
Seq-GAN-BERT：Sequence Generative Adversarial Learning for Low-resource Name Entity Recognition—0
ProcessBERT: Towards Equivalence Judgment of Variable Definitions among Multiple Engineering Documents—0
Representation Learning for Conversational Data using Discourse Mutual Information Maximization—0
Learning To Retrieve Prompts for In-Context Learning—0
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks—0
Towards Interactive Language Modeling—0
STT: Soft Template Tuning for Few-Shot Learning—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
Progressive Class Semantic Matching for Semi-supervised Text Classification—0
Multi-Stage Pre-Training for Math-Understanding: ^2(AL)BERT—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS Decoding—0
A Dual Prompt Learning Framework for Few-Shot Dialogue State Tracking—0
Kformer: Knowledge Injection in Transformer Feed-Forward LayersCode1
A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition—0
The Dark Side of the Language: Pre-trained Transformers in the DarkNet—0
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation—0
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
Datasheet for the PileCode3
Accurate identification of bacteriophages from metagenomic data using TransformerCode1
Multi-task Pre-training Language Model for Semantic Network CompletionCode0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training—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