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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10651–10700 of 17610 papers

TitleStatusHype
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise—0
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems—0
Define, Evaluate, and Improve Task-Oriented Cognitive Capabilities for Instruction Generation Models—0
Crowd Score: A Method for the Evaluation of Jokes using Large Language Model AI Voters as JudgesCode0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction—0
Critic-Guided Decoding for Controlled Text GenerationCode1
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 SurprisalCode0
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning—0
SERENGETI: Massively Multilingual Language Models for AfricaCode0
Resolving Indirect Referring Expressions for Entity SelectionCode0
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-shot In-Context Learners—0
SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning—0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models—0
Training language models to summarize narratives improves brain alignmentCode1
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis—0
Zero-shot Triplet Extraction by Template InfillingCode1
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models—0
KronA: Parameter Efficient Tuning with Kronecker Adapter—0
Parsel: Algorithmic Reasoning with Language Models by Composing DecompositionsCode2
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities—0
Toward Human-Like Evaluation for Natural Language Generation with Error AnalysisCode1
Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMCCode1
Language Modeling with Latent Situations—0
Is GPT-3 a Good Data Annotator?Code0
Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data—0
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in EnglishCode1
Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters—0
Precise Zero-Shot Dense Retrieval without Relevance LabelsCode2
Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis—0
Self-Instruct: Aligning Language Models with Self-Generated InstructionsCode5
DePlot: One-shot visual language reasoning by plot-to-table translation—0
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?—0
Does CLIP Bind Concepts? Probing Compositionality in Large Image ModelsCode1
Are Deep Neural Networks SMARTer than Second Graders?Code1
DISCO: Distilling Counterfactuals with Large Language ModelsCode1
ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language ModelsCode1
EIT: Enhanced Interactive TransformerCode0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
A Length-Extrapolatable TransformerCode2
AnyTOD: A Programmable Task-Oriented Dialog System—0
Identifying and Manipulating the Personality Traits of Language Models—0
Controllable Text Generation with Language Constraints—0
A Measure-Theoretic Characterization of Tight Language Models—0
Optimizing Prompts for Text-to-Image GenerationCode4
An overview of open source Deep Learning-based libraries for Neuroscience—0
Evaluating Human-Language Model InteractionCode1
Python Code Generation by Asking Clarification QuestionsCode1
Improved Long-Form Spoken Language Translation with Large Language Models—0
Explanation Regeneration via Information BottleneckCode0
Visconde: Multi-document QA with GPT-3 and Neural RerankingCode1
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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