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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8601–8650 of 17610 papers

TitleStatusHype
Efficient Language Modeling with Sparse all-MLP—0
Efficient End-to-end Language Model Fine-tuning on Graphs—0
Efficient Large Scale Language Modeling with Mixtures of Experts—0
Efficient Left-to-Right Hierarchical Phrase-Based Translation with Improved Reordering—0
Efficient, Lexicon-Free OCR using Deep Learning—0
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking—0
Efficient Indirect LLM Jailbreak via Multimodal-LLM Jailbreak—0
Efficient Long Context Language Model Retrieval with Compression—0
Efficient long-distance relation extraction with DG-SpanBERT—0
Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling—0
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer—0
Efficiently and Thoroughly Anonymizing a Transformer Language Model for Dutch Electronic Health Records: a Two-Step Method—0
Efficiently Building a Domain-Specific Large Language Model from Scratch: A Case Study of a Classical Chinese Large Language Model—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Efficient Machine Translation Domain Adaptation—0
Efficient Masked Autoencoders with Self-Consistency—0
Efficient MDI Adaptation for n-gram Language Models—0
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search—0
Efficient Neural Query Auto Completion—0
EfficientOCR: An Extensible, Open-Source Package for Efficiently Digitizing World Knowledge—0
Efficient Parallel Audio Generation using Group Masked Language Modeling—0
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents—0
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion—0
Toward Adaptive Large Language Models Structured Pruning via Hybrid-grained Weight Importance Assessment—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient RLHF: Reducing the Memory Usage of PPO—0
Efficient Sequence Learning with Group Recurrent Networks—0
Efficient Sparsely Activated Transformers—0
Efficient Standardization of Clinical Notes using Large Language Models—0
Efficient Training of Language Models with Compact and Consistent Next Token Distributions—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Efficient Unsupervised NMT for Related Languages with Cross-Lingual Language Models and Fidelity Objectives—0
Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search—0
Effort and Size Estimation in Software Projects with Large Language Model-based Intelligent Interfaces—0
Effort of Genre Variation and Prediction of System Performance—0
EgoLM: Multi-Modal Language Model of Egocentric Motions—0
EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios—0
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015—0
EHRTutor: Enhancing Patient Understanding of Discharge Instructions—0
EiCi: A New Method of Dynamic Embedding Incorporating Contextual Information in Chinese NER—0
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization—0
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification—0
Eir: Thai Medical Large Language Models—0
Elastic Weight Consolidation for Full-Parameter Continual Pre-Training of Gemma2—0
ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation—0
ELDeR: Getting Efficient LLMs through Data-Driven Regularized Layer-wise Pruning—0
ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents—0
Electoral Agitation Dataset: The Use Case of the Polish Election—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