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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11801–11850 of 17610 papers

TitleStatusHype
Are Prompt-based Models Clueless?—0
Automatic Spoken Language Identification using a Time-Delay Neural Network—0
Foundation Posteriors for Approximate Probabilistic Inference—0
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models—0
GPoeT-2: A GPT-2 Based Poem GeneratorCode0
Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator—0
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems—0
AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D AvatarsCode3
Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT—0
TiBERT: Tibetan Pre-trained Language Model—0
PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain—0
Bootstrapping Text Anonymization Models with Distant SupervisionCode0
Controlling Translation Formality Using Pre-trained Multilingual Language Models—0
Weakly Supervised Text Classification using Supervision Signals from a Language ModelCode1
TIE: Topological Information Enhanced Structural Reading Comprehension on Web PagesCode1
SimCPSR: Simple Contrastive Learning for Paper Submission Recommendation SystemCode0
Localized Vision-Language Matching for Open-vocabulary Object DetectionCode1
Efficient and Training-Free Control of Language Generation—0
A Generalist AgentCode2
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language ModelingCode1
Towards the Generation of Musical Explanations with GPT-3Code0
Towards Unified Prompt Tuning for Few-shot Text Classification—0
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With TransformersCode0
Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification—0
Human Language ModelingCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
Extracting Latent Steering Vectors from Pretrained Language ModelsCode1
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More EffectiveCode1
Sentence-level Privacy for Document Embeddings—0
The Importance of Context in Very Low Resource Language Modeling—0
Symphony Generation with Permutation Invariant Language ModelCode2
LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents—0
Multi-segment preserving sampling for deep manifold sampler—0
Multimodal Semi-Supervised Learning for Text RecognitionCode1
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text CorrespondenceCode0
Vietnamese Automatic Speech Recognition using Wav2vec 2.0Code1
Towards a Progression-Aware Autonomous Dialogue Agent—0
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney InjuryCode0
A Data Cartography based MixUp for Pre-trained Language ModelsCode0
The Unreliability of Explanations in Few-shot Prompting for Textual ReasoningCode1
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Prompt Distribution Learning—0
Robust Conversational Agents against Imperceptible Toxicity TriggersCode0
Assistive Recipe Editing through Critiquing—0
A Simple Contrastive Learning Objective for Alleviating Neural Text DegenerationCode1
Declaration-based Prompt Tuning for Visual Question AnsweringCode1
Implicit N-grams Induced by RecurrenceCode0
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging FrameworkCode1
TLMOTE: A Topic-based Language Modelling Approach for Text OversamplingCode0
Provably Confidential Language ModellingCode0
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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