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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10351–10400 of 17610 papers

TitleStatusHype
Making a Computational Attorney—0
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset—0
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language ModelingCode5
Stylometric Detection of AI-Generated Text in Twitter TimelinesCode1
Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corporaCode0
ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification—0
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?—0
Data Portraits: Recording Foundation Model Training Data—0
FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model—0
OpenICL: An Open-Source Framework for In-context LearningCode2
Spelling convention sensitivity in neural language models—0
PaLM-E: An Embodied Multimodal Language ModelCode2
Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance—0
A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text ClassificationCode1
Could a Large Language Model be Conscious?—0
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion TasksCode1
Prismer: A Vision-Language Model with Multi-Task ExpertsCode1
End-to-End Speech Recognition: A Survey—0
Will Affective Computing Emerge from Foundation Models and General AI? A First Evaluation on ChatGPT—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question AnsweringCode2
Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMCode1
Exploiting Language Relatedness in Machine Translation Through Domain Adaptation Techniques—0
BenchDirect: A Directed Language Model for Compiler Benchmarks—0
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning—0
Open-World Object Manipulation using Pre-trained Vision-Language Models—0
Semiparametric Language Models Are Scalable Continual Learners—0
How will Language Modelers like ChatGPT Affect Occupations and Industries?—0
AI and the FCI: Can ChatGPT Project an Understanding of Introductory Physics?—0
Variance-reduced Clipping for Non-convex OptimizationCode0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space—0
Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents—0
Almanac: Retrieval-Augmented Language Models for Clinical Medicine—0
Domain-adapted large language models for classifying nuclear medicine reports—0
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks—0
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-trainingCode0
Weighted Sampling for Masked Language Modeling—0
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue GenerationCode1
Efficient Masked Autoencoders with Self-Consistency—0
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and ContextCode0
Language Is Not All You Need: Aligning Perception with Language Models—0
Reward Design with Language ModelsCode2
Pretraining De-Biased Language Model with Large-scale Click Logs for Document RankingCode1
A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic SegmentationCode0
Finding Support Examples for In-Context Learning—0
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video CaptioningCode2
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural NetworksCode2
Show:102550
← PrevPage 208 of 353Next →
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