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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13601–13650 of 17610 papers

TitleStatusHype
GP-VLS: A general-purpose vision language model for surgery—0
Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach—0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration—0
Gradable ChatGPT Translation Evaluation—0
GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection—0
Gradient-based inference of abstract task representations for generalization in neural networks—0
Gradient-Based Language Model Red Teaming—0
Gradients of Counterfactuals—0
GradPower: Powering Gradients for Faster Language Model Pre-Training—0
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model—0
Grafting Pre-trained Models for Multimodal Headline Generation—0
Grammatical Error Correction as Multiclass Classification with Single Model—0
Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses—0
Grammatical error correction using hybrid systems and type filtering—0
Grammatical error correction using neural machine translation—0
Grammatical Error Feedback: An Implicit Evaluation Approach—0
Grammaticality and Language Modelling—0
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes—0
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications—0
Graph-based Coherence Modeling For Assessing Readability—0
Graph-Based Collective Lexical Selection for Statistical Machine Translation—0
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models—0
Graph-Based Unsupervised Learning of Word Similarities Using Heterogeneous Feature Types—0
GraphCodeBERT: Pre-training Code Representations with Data Flow—0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model—0
Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering—0
Graph Databases for Designing High-Performance Speech Recognition Grammars—0
Graph database while computationally efficient filters out quickly the ESG integrated equities in investment management—0
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework—0
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs—0
GraphicBench: A Planning Benchmark for Graphic Design with Language Agents—0
Graph Language Model (GLM): A new graph-based approach to detect social instabilities—0
Graph-Level Embedding for Time-Evolving Graphs—0
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding—0
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs—0
Graphologue: Exploring Large Language Model Responses with Interactive Diagrams—0
Graph Propagation for Paraphrasing Out-of-Vocabulary Words in Statistical Machine Translation—0
Graph-Structured Speculative Decoding—0
GraphVL: Graph-Enhanced Semantic Modeling via Vision-Language Models for Generalized Class Discovery—0
GraspCorrect: Robotic Grasp Correction via Vision-Language Model-Guided Feedback—0
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models—0
Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training—0
Green CWS: Extreme Distillation and Efficient Decode Method Towards Industrial Application—0
Green Runner: A tool for efficient model selection from model repositories—0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—0
Grid-LOGAT: Grid Based Local and Global Area Transcription for Video Question Answering—0
Grid Search Hyperparameter Benchmarking of BERT, ALBERT, and LongFormer on DuoRC—0
GENEVA: GENErating and Visualizing branching narratives using LLMs—0
GRIN: GRadient-INformed MoE—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