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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13051–13100 of 17610 papers

TitleStatusHype
Neural-FST Class Language Model for End-to-End Speech Recognition—0
Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired ClassifiersCode0
Protum: A New Method For Prompt Tuning Based on "[MASK]"—0
Schema-Free Dependency Parsing via Sequence Generation—0
Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based Encoder-decoder ASR—0
An Assessment of the Impact of OCR Noise on Language Models—0
DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators—0
On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR—0
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language ModelCode0
FiNCAT: Financial Numeral Claim Analysis ToolCode0
Impact of representation matching with neural machine translationCode0
BERTHA: Video Captioning Evaluation Via Transfer-Learned Human AssessmentCode0
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection—0
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models—0
Multimodal data matters: language model pre-training over structured and unstructured electronic health recordsCode0
Relational Memory Augmented Language Models—0
Emotion-based Modeling of Mental Disorders on Social Media—0
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring—0
A Large and Diverse Arabic Corpus for Language Modeling—0
Chinese Word Segmentation with Heterogeneous Graph Neural Network—0
A Comparative Study on Language Models for Task-Oriented Dialogue SystemsCode0
Identifying Adversarial Attacks on Text Classifiers—0
Nearest Class-Center Simplification through Intermediate Layers—0
Text Style Transfer for Bias Mitigation using Masked Language Modeling—0
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-trainingCode0
LaMDA: Language Models for Dialog Applications—0
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees—0
TourBERT: A pretrained language model for the tourism industry—0
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health RecordsCode0
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model CapabilitiesCode0
Deep dive into CoCon - A Self Supervised approach for Controlled Text Generation—0
Language Model-Based Paired Variational Autoencoders for Robotic Language LearningCode0
PPLM Revisited: Steering and Beaming a Lumbering Mammoth to Control Text Generation—0
Unintended Bias in Language Model-driven Conversational Recommendation—0
ValCAT: Generating Variable-Length Contextualized Adversarial Transformations using Encoder-DecoderCode0
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
Hardness Masking via Auto-Regressive Language Model—0
Data Augmentation for Biomedical Factoid Question Answering—0
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs—0
Breaking Character: Are Subwords Good Enough for MRLs After All?—0
Discourse-Aware Prompt Design for Text Generation—0
Hierarchical Transformers Are More Efficient Language Models—0
CL-ReKD: Cross-lingual Knowledge Distillation for Multilingual Retrieval Question Answering—0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding—0
Efficient Machine Translation Domain Adaptation—0
Disaggregating Hops: Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at each Hop?—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
How do QA models combine knowledge from LM and 100 passages?—0
A Masked Segmental Language Model for Unsupervised Natural Language Segmentation—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