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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12701–12750 of 17610 papers

TitleStatusHype
Developing Language Resources and NLP Tools for the North Korean Language—0
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification—0
A Language Model for Spell Checking of Educational Texts in Kurdish (Sorani)Code0
A Language Modelling Approach to Quality Assessment of OCR’ed Historical Text—0
gaBERT — an Irish Language Model—0
Discovering Financial Hypernyms by Prompting Masked Language Models—0
Error Correction Environment for the Polish Parliamentary Corpus—0
Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More—0
Evaluating Pretraining Strategies for Clinical BERT Models—0
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method—0
Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey—0
On the Usefulness of Embeddings, Clusters and Strings for Text Generator EvaluationCode0
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
The Contribution of Lyrics and Acoustics to Collaborative Understanding of Mood—0
E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and GenerationCode0
Automatic Short Math Answer Grading via In-context Meta-learningCode0
Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task—0
MiniDisc: Minimal Distillation Schedule for Language Model CompressionCode0
COFS: Controllable Furniture layout Synthesis—0
Urdu News Article Recommendation Model using Natural Language Processing Techniques—0
L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library—0
Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit—0
Few-shot Subgoal Planning with Language Models—0
StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and StereotypesCode0
Differentially Private Decoding in Large Language Models—0
TAGPRIME: A Unified Framework for Relational Structure ExtractionCode0
Improving CTC-based ASR Models with Gated Interlayer Collaboration—0
Fine-grained Contrastive Learning for Relation ExtractionCode0
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations—0
Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation—0
Large Language Models are Few-Shot Clinical Information Extractors—0
Low Resource Style Transfer via Domain Adaptive Meta Learning—0
Segmenting Numerical Substitution Ciphers—0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
MaskEval: Weighted MLM-Based Evaluation for Text Summarization and Simplification—0
On the Role of Bidirectionality in Language Model Pre-Training—0
Toxicity Detection with Generative Prompt-based Inference—0
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry GenerationCode0
Enhancing Continual Learning with Global Prototypes: Counteracting Negative Representation Drift—0
K-12BERT: BERT for K-12 educationCode0
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition—0
PERT: A New Solution to Pinyin to Character Conversion TaskCode0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
Chunk-based Nearest Neighbor Machine TranslationCode0
Garden-Path Traversal in GPT-2Code0
Improving Short Text Classification With Augmented Data Using GPT-3—0
Challenges in Measuring Bias via Open-Ended Language GenerationCode0
Looking for a Handsome Carpenter! Debiasing GPT-3 Job AdvertisementsCode0
RL with KL penalties is better viewed as Bayesian inference—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