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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14051–14100 of 17610 papers

TitleStatusHype
Twitter Translation using Translation-Based Cross-Lingual Retrieval—0
TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus—0
Two Discourse Driven Language Models for Semantics—0
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment—0
Two Improvements to Left-to-Right Decoding for Hierarchical Phrase-based Machine Translation—0
Two-in-One: A Model Hijacking Attack Against Text Generation Models—0
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
Two-Phase Cross-Lingual Language Model Fine-Tuning for Machine Translation Quality Estimation—0
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia—0
Two-Step Machine Translation with Lattices—0
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions—0
Tx-LLM: A Large Language Model for Therapeutics—0
Typhoon: Towards an Effective Task-Specific Masking Strategy for Pre-trained Language Models—0
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model—0
UBC-NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models—0
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus—0
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training—0
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles—0
UCCIX: Irish-eXcellence Large Language Model—0
uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers—0
UDapter: Typology-based Language Adapters for Multilingual Dependency Parsing and Sequence Labeling—0
UdS-(retrain|distributional|surface): Improving POS Tagging for OOV Words in German CMC and Web Data—0
UEdin: Translating L1 Phrases in L2 Context using Context-Sensitive SMT—0
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning—0
Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval—0
UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection—0
UHH Submission to the WMT17 Quality Estimation Shared Task—0
UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification—0
ULMRec: User-centric Large Language Model for Sequential Recommendation—0
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language—0
UM-Checker: A Hybrid System for English Grammatical Error Correction—0
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs—0
Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines—0
Unbiasing Truncated Backpropagation Through Time—0
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language—0
Unbounded: A Generative Infinite Game of Character Life Simulation—0
Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security—0
Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text—0
Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition—0
Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models—0
Uncertainty-aware Language Modeling for Selective Question Answering—0
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis—0
Uncertainty Aware Learning for Language Model Alignment—0
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings—0
UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference—0
Unconditional Truthfulness: Learning Conditional Dependency for Uncertainty Quantification of Large Language Models—0
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks—0
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization—0
Uncovering Factor Level Preferences to Improve Human-Model Alignment—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