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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16651–16700 of 17610 papers

TitleStatusHype
Construct a Sentence with Multiple Specified Words—0
AutoKG: Constructing Virtual Knowledge Graphs from Unstructured Documents for Question Answering—0
Filtering Discomforting Recommendations with Large Language Models—0
Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks—0
Constructing Multimodal Datasets from Scratch for Rapid Development of a Japanese Visual Language Model—0
Construction contract risk identification based on knowledge-augmented language model—0
Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training—0
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model—0
Construction of Semantic Collocation Bank Based on Semantic Dependency Parsing—0
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection—0
Contamination Report for Multilingual Benchmarks—0
CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models—0
ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Context-Aware Clustering using Large Language Models—0
Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model—0
Context-Aware Differential Privacy for Language Modeling—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
Context-Aware Neural Machine Translation Decoding—0
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment—0
Context-aware Sentiment Word Identification: sentiword2vec—0
Context-Aware Temperature for Language Modeling—0
Context-Aware Temporal Embedding of Objects in Video Data—0
Context-Aware Text Normalisation for Historical Dialects—0
Context-based out-of-vocabulary word recovery for ASR systems in Indian languages—0
Context based Text-generation using LSTM networks—0
Context-Enhanced Video Moment Retrieval with Large Language Models—0
Context Generation Improves Open Domain Question Answering—0
Context Matters: A Strategy to Pre-train Language Model for Science Education—0
Context Parallelism for Scalable Million-Token Inference—0
Context Perception Parallel Decoder for Scene Text Recognition—0
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering—0
Context-specific Language Modeling for Human Trafficking Detection from Online Advertisements—0
Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation—0
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI—0
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations—0
Contextual BERT: Conditioning the Language Model Using a Global State—0
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems—0
Contextual Data Augmentation for Task-Oriented Dialog Systems—0
Contextual Density Ratio for Language Model Biasing of Sequence to Sequence ASR Systems—0
Contextual Emotion Estimation from Image Captions—0
Contextual Emotion Recognition using Large Vision Language Models—0
Contextual Gradient Flow Modeling for Large Language Model Generalization in Multi-Scale Feature Spaces—0
Contextualized Automatic Speech Recognition with Dynamic Vocabulary—0
Contextualized Evaluations: Taking the Guesswork Out of Language Model Evaluations—0
Contextualized French Language Models for Biomedical Named Entity Recognition—0
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures—0
Contextualized Representations Using Textual Encyclopedic Knowledge—0
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion—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