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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4701–4750 of 17610 papers

TitleStatusHype
Cross-lingual Model Transfer Using Feature Representation Projection—0
Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages—0
Cross-lingual projection for class-based language models—0
Cross-lingual Pronoun Prediction for English, French and German with Maximum Entropy Classification—0
Cross-lingual Pronoun Prediction with Linguistically Informed Features—0
Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks—0
Cross-Lingual QA as a Stepping Stone for Monolingual Open QA in Icelandic—0
Cross-Lingual Speaker Identification from Weak Local Evidence—0
Cross-Lingual Speaker Verification with Domain-Balanced Hard Prototype Mining and Language-Dependent Score Normalization—0
Crosslingual Structural Priming and the Pre-Training Dynamics of Bilingual Language Models—0
Cross-lingual Subjectivity Detection for Resource Lean Languages—0
Cross-Lingual Supervision improves Large Language Models Pre-training—0
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training—0
Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages—0
Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER—0
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model—0
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources—0
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models—0
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding—0
Cross-lingual Transfer of Semantic Role Labeling Models—0
Cross-lingual Transferring of Pre-trained Contextualized Language Models—0
Cross-Lingual Transformers for Neural Automatic Post-Editing—0
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-Tuning—0
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
Cross-modality debiasing: using language to mitigate sub-population shifts in imaging—0
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning—0
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression—0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
Cross-sentence Pre-trained Model for Interactive QA matching—0
Cross-target Stance Detection by Exploiting Target Analytical Perspectives—0
CrossTune: Black-Box Few-Shot Classification with Label Enhancement—0
Cross-utterance ASR Rescoring with Graph-based Label Propagation—0
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
CRPE: Expanding The Reasoning Capability of Large Language Model for Code Generation—0
Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse—0
A Generation Framework with Strict Constraints for Crystal Materials Design—0
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials—0
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning—0
CTC-Assisted LLM-Based Contextual ASR—0
CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Models—0
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning—0
CTRL: Connect Collaborative and Language Model for CTR Prediction—0
CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generation—0
CtrlRAG: Black-box Adversarial Attacks Based on Masked Language Models in Retrieval-Augmented Language Generation—0
CubeRobot: Grounding Language in Rubik's Cube Manipulation via Vision-Language Model—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