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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7101–7150 of 17610 papers

TitleStatusHype
Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders—0
Semi-supervised Word Sense Disambiguation with Neural Models—0
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation—0
Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model—0
Sense-Aware Statistical Machine Translation using Adaptive Context-Dependent Clustering—0
SenseBERT: Driving Some Sense into BERT—0
Sensing-Assisted Channel Prediction in Complex Wireless Environments: An LLM-Based Approach—0
SENTAUR: Security EnhaNced Trojan Assessment Using LLMs Against Undesirable Revisions—0
Sentence Clustering using PageRank Topic Model—0
Sentence Compression with Joint Structural Inference—0
Sentence Correction Based on Large-scale Language Modelling—0
Sentence Embedding for Neural Machine Translation Domain Adaptation—0
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!—0
Sentence-level Privacy for Document Embeddings—0
Sentence-level Privacy for Document Embeddings—0
Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming—0
Sentence-Select: Large-Scale Language Model Data Selection for Rare-Word Speech Recognition—0
Sentence Semantic Regression for Text Generation—0
Sentence Similarity Based on Contexts—0
Sentence Weighting for Neural Machine Translation Domain Adaptation—0
Sentential Paraphrasing as Black-Box Machine Translation—0
SentiCap: Generating Image Descriptions with Sentiments—0
Sentiment Analysis Across Multiple African Languages: A Current Benchmark—0
Sentiment Analysis of Homeric Text: The 1st Book of Iliad—0
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon—0
Sentiment analysis of preservice teachers' reflections using a large language model—0
Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora—0
Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach—0
Sentinels of the Stream: Unleashing Large Language Models for Dynamic Packet Classification in Software Defined Networks -- Position Paper—0
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis—0
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment—0
SEOVER: Sentence-level Emotion Orientation Vector based Conversation Emotion Recognition Model—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages—0
An Information-Theoretic Approach for Detecting Edits in AI-Generated Text—0
Seq2Mol: Automatic design of de novo molecules conditioned by the target protein sequences through deep neural networks—0
Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model—0
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model—0
Seq-GAN-BERT：Sequence Generative Adversarial Learning for Low-resource Name Entity Recognition—0
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing—0
CORM: Cache Optimization with Recent Message for Large Language Model Inference—0
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS—0
Sequence-level Large Language Model Training with Contrastive Preference Optimization—0
Sequence Model Design for Code Completion in the Modern IDE—0
Sequence Preserving Network Traffic Generation—0
Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives—0
Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion—0
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions—0
Sequence to Sequence - Video to Text—0
Sequential Attention Module for 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