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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8751–8800 of 17610 papers

TitleStatusHype
LABOR-LLM: Language-Based Occupational Representations with Large Language Models—0
LACoS-BLOOM: Low-rank Adaptation with Contrastive objective on 8 bits Siamese-BLOOM—0
Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription—0
Lacuna Reconstruction: Self-supervised Pre-training for Low-Resource Historical Document Transcription—0
LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling—0
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching—0
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections—0
LakotaBERT: A Transformer-based Model for Low Resource Lakota Language—0
PALM: Predicting Actions through Language Models—0
LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits—0
LAMBO: Large AI Model Empowered Edge Intelligence—0
LaMDA: Language Models for Dialog Applications—0
LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner—0
LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition—0
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction—0
LANGALIGN: Enhancing Non-English Language Models via Cross-Lingual Embedding Alignment—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
langid.py for better language modelling—0
LangNav: Language as a Perceptual Representation for Navigation—0
LangPert: Detecting and Handling Task-level Perturbations for Robust Object Rearrangement—0
Language Alignment via Nash-learning and Adaptive feedback—0
Language and Dialect Discrimination Using Compression-Inspired Language Models—0
Language as a Latent Variable: Discrete Generative Models for Sentence Compression—0
Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI—0
Language-assisted Vision Model Debugger: A Sample-Free Approach to Finding and Fixing Bugs—0
Language-aware Multiple Datasets Detection Pretraining for DETRs—0
Language-Based Depth Hints for Monocular Depth Estimation—0
Language Clustering for Multilingual Named Entity Recognition—0
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking—0
LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl—0
Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation—0
Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model—0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey—0
Language-Grounded Hierarchical Planning and Execution with Multi-Robot 3D Scene Graphs—0
Language Guided Exploration for RL Agents in Text Environments—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
Language-Guided Music Recommendation for Video via Prompt Analogies—0
Language-Guided Traffic Simulation via Scene-Level Diffusion—0
Language Identification and Modeling in Specialized Hardware—0
Language-independent hybrid MT with PRESEMT—0
Language Informed Modeling of Code-Switched Text—0
Language Is Not All You Need: Aligning Perception with Language Models—0
Language model acceptability judgements are not always robust to context—0
Language Model Adaptation for Language and Dialect Identification of Text—0
Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web—0
Language Model Analysis for Ontology Subsumption Inference—0
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation—0
Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos—0
Language Model Augmented Monotonic Attention for Simultaneous Translation—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