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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8851–8900 of 17610 papers

TitleStatusHype
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation—0
Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation—0
PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment—0
Watermarking Language Models for Many Adaptive Users—0
Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering—0
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving—0
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot—0
Enriching a Model's Notion of Belief using a Persistent Memory—0
Enriching Cold Start Personalized Language Model Using Social Network Information—0
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification—0
Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—0
Enriching Vulnerability Reports Through Automated and Augmented Description Summarization—0
Enriching Word Sense Embeddings with Translational Context—0
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation—0
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation—0
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning—0
Ensembling Sparse Autoencoders—0
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles—0
Ensuring Fair LLM Serving Amid Diverse Applications—0
LLMs Plagiarize: Ensuring Responsible Sourcing of Large Language Model Training Data Through Knowledge Graph Comparison—0
Enterprise Large Language Model Evaluation Benchmark—0
Entity and Evidence Guided Document-Level Relation Extraction—0
Entity and Evidence Guided Relation Extraction for DocRED—0
Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation—0
Entity-Aware Language Model as an Unsupervised Reranker—0
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web—0
Entity Decisions in Neural Language Modelling: Approaches and Problems—0
Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction—0
Entity Retrieval via Entity Factoid Hierarchy—0
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions—0
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices—0
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference—0
Entropy-based Exploration Conduction for Multi-step Reasoning—0
Entropy-based Pruning for Phrase-based Machine Translation—0
Entropy-Based Subword Mining with an Application to Word Embeddings—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
EntropyRank: Unsupervised Keyphrase Extraction via Side-Information Optimization for Language Model-based Text Compression—0
Entropy Rate Estimation for Markov Chains with Large State Space—0
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models—0
EO-VLM: VLM-Guided Energy Overload Attacks on Vision Models—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction—0
Epigenomic language models powered by Cerebras—0
EpilepsyLLM: Domain-Specific Large Language Model Fine-tuned with Epilepsy Medical Knowledge—0
Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience—0
Equipping Educational Applications with Domain Knowledge—0
Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance—0
ER3: A Unified Framework for Event Retrieval, Recognition and Recounting—0
ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning—0
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing—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