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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13501–13550 of 17610 papers

TitleStatusHype
Generative Question Answering: Learning to Answer the Whole Question—0
Generative Recommendation with Continuous-Token Diffusion—0
Generative Regression Based Watch Time Prediction for Short-Video Recommendation—0
Generative Relevance Feedback with Large Language Models—0
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design—0
Generative Sentiment Transfer via Adaptive Masking—0
Generative Spoken Dialogue Language Modeling—0
Generative Spoken Language Model based on continuous word-sized audio tokens—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
Generative Text Steganography with Large Language Model—0
Generative Timelines for Instructed Visual Assembly—0
GeneSUM: Large Language Model-based Gene Summary Extraction—0
GeNet: A Multimodal LLM-Based Co-Pilot for Network Topology and Configuration—0
GenFollower: Enhancing Car-Following Prediction with Large Language Models—0
Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise—0
GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling—0
GenSpectrum Chat: Data Exploration in Public Health Using Large Language Models—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
GenTorrent: Scaling Large Language Model Serving with An Overley Network—0
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers—0
GenX: Mastering Code and Test Generation with Execution Feedback—0
Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions—0
GeoCode-GPT: A Large Language Model for Geospatial Code Generation Tasks—0
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder—0
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing—0
Geometry Informed Tokenization of Molecules for Language Model Generation—0
Geometry is All You Need: A Unified Taxonomy of Matrix and Tensor Factorization for Compression of Generative Language Models—0
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing—0
GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing—0
German and English Treebanks and Lexica for Tree-Adjoining Grammars—0
German BERT Model for Legal Named Entity Recognition—0
German FinBERT: A German Pre-trained Language Model—0
GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning—0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders—0
Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs—0
Get the gist? Using large language models for few-shot decontextualization—0
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
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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