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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12251–12300 of 17610 papers

TitleStatusHype
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
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme ModelsCode1
Synchromesh: Reliable code generation from pre-trained language modelsCode2
Learning To Recognize Procedural Activities with Distant SupervisionCode1
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models—0
BERTHA: Video Captioning Evaluation Via Transfer-Learned Human AssessmentCode0
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection—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
A Large and Diverse Arabic Corpus for Language Modeling—0
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring—0
Chinese Word Segmentation with Heterogeneous Graph Neural Network—0
Identifying Adversarial Attacks on Text Classifiers—0
A Comparative Study on Language Models for Task-Oriented Dialogue SystemsCode0
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
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model CapabilitiesCode0
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health RecordsCode0
PPLM Revisited: Steering and Beaming a Lumbering Mammoth to Control Text Generation—0
Deep dive into CoCon - A Self Supervised approach for Controlled Text Generation—0
Korean-Specific Dataset for Table Question AnsweringCode1
Language Model-Based Paired Variational Autoencoders for Robotic Language LearningCode0
Unintended Bias in Language Model-driven Conversational Recommendation—0
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus—0
ValCAT: Generating Variable-Length Contextualized Adversarial Transformations using Encoder-DecoderCode0
Rethinking Style Transformer by Energy-based Interpretation: Adversarial Unsupervised Style Transfer using Pretrained Model—0
Provably Confidential Language Modelling—0
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving—0
Speaker Clustering in Textual Dialogue with Utterance Correlation and Cross-corpus Dialogue Act Supervision—0
Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription—0
PNEG: Prompt-based Negative Response Generation for Robust Response Selection Model—0
On Measuring Social Biases in Prompt-Based Learning—0
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models—0
Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture—0
"my stance decides my language": Modeling of Framing and Political Stance in News Media—0
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information—0
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs—0
Causal Language Model for Zero-shot Constrained Keyphrase Generation—0
Don’t Forget About Pronouns: Removing Gender Bias in Language Models without Losing Factual Gender Information—0
Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
Exposing the Limits of Video-Text Models through Contrast Sets—0
How do QA models combine knowledge from LM and 100 passages?—0
DOCmT5: Document-Level Pre-training of Multilingual Language Models—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