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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7501–7550 of 17610 papers

TitleStatusHype
Getting to Production with Few-shot Natural Language Generation Models—0
GhostWriter: Using an LSTM for Automatic Rap Lyric Generation—0
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect—0
GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture—0
GiusBERTo: A Legal Language Model for Personal Data De-identification in Italian Court of Auditors Decisions—0
Giving Simulated Cells a Voice: Evolving Prompt-to-Intervention Models for Cellular Control—0
GKS: Graph-based Knowledge Selector for Task-oriented Dialog System—0
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts—0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model—0
GLM: General Language Model Pretraining with Autoregressive Blank Infilling—0
Global and Local Feature Learning for Ego-Network Analysis—0
Globalizing BERT-based Transformer Architectures for Long Document Summarization—0
Globally Coherent Text Generation with Neural Checklist Models—0
Global memory transformer for processing long documents—0
GlobalPhone: Pronunciation Dictionaries in 20 Languages—0
Global Position Aware Group Choreography using Large Language Model—0
Global-to-Local Support Spectrums for Language Model Explainability—0
GlórIA - A Generative and Open Large Language Model for Portuguese—0
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings—0
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion—0
GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters—0
GNEG: Graph-Based Negative Sampling for word2vec—0
GNN-ACLP: Graph Neural Networks based Analog Circuit Link Prediction—0
GNN: Graph Neural Network and Large Language Model for Data Discovery—0
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning—0
Go-Browse: Training Web Agents with Structured Exploration—0
Go Climb a Dependency Tree and Correct the Grammatical Errors—0
Going Wider: Recurrent Neural Network With Parallel Cells—0
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review—0
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2—0
A Language Model With Million Context Length For Raw Audio—0
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation—0
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification—0
GottBERT: a pure German Language Model—0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
GPC: Generative and General Pathology Image Classifier—0
GP-GPT: Large Language Model for Gene-Phenotype Mapping—0
GPKEX: Genetically Programmed Keyphrase Extraction from Croatian Texts—0
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion—0
GPT-2-based Human-in-the-loop Theatre Play Script Generation—0
GPT-3-driven pedagogical agents for training children's curious question-asking skills—0
Gpt-4: A Review on Advancements and Opportunities in Natural Language Processing—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
GPT4GEO: How a Language Model Sees the World's Geography—0
GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as A Plug-and-Play Transductive Model for Medical Image Analysis—0
GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation—0
GPT-4V as Traffic Assistant: An In-depth Look at Vision Language Model on Complex Traffic Events—0
GPT-4V Explorations: Mining Autonomous Driving—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