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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14151–14200 of 17610 papers

TitleStatusHype
Improving training time and GPU utilization in geo-distributed language model training—0
Improving Unsupervised Sentence Simplification Using Fine-Tuned Masked Language Models—0
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder—0
Improving Uyghur ASR systems with decoders using morpheme-based language models—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT—0
Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text—0
Improving Word Alignment Using Linguistic Code Switching Data—0
Improving Word Representations via Global Context and Multiple Word Prototypes—0
Improving Word Translation Disambiguation by Capturing Multiword Expressions with Dictionaries—0
Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models—0
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence—0
Inaugural MOASEI Competition at AAMAS'2025: A Technical Report—0
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning—0
In-Context Demonstration Selection with Cross Entropy Difference—0
In-Context Former: Lightning-fast Compressing Context for Large Language Model—0
In-context Language Learning for Endangered Languages in Speech Recognition—0
In-Context Learning can distort the relationship between sequence likelihoods and biological fitness—0
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models—0
In-Context Learning of Energy Functions—0
Explaining Emergent In-Context Learning as Kernel Regression—0
In-Context Learning with Reinforcement Learning for Incomplete Utterance Rewriting—0
Large Language Models can Implement Policy Iteration—0
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model—0
Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer—0
Incorporating Context into Language Encoding Models for fMRI—0
Incorporating Domain Knowledge To Improve Topic Segmentation Of Long MOOC Lecture Videos—0
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis—0
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition—0
Incorporating Linguistic Knowledge in Statistical Machine Translation: Translating Prepositions—0
Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation—0
Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis—0
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design—0
Incorporating Relation Knowledge into Commonsense Reading Comprehension with Multi-task Learning—0
Incorporating Semantic Attention in Video Description Generation—0
Incorporating Side Information into Recurrent Neural Network Language Models—0
Incorporating Stylistic Lexical Preferences in Generative Language Models—0
Incorporating Symbolic Sequential Modeling for Speech Enhancement—0
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring—0
Increasing the Accessibility of Causal Domain Knowledge via Causal Information Extraction Methods: A Case Study in the Semiconductor Manufacturing Industry—0
Incremental Adaptation Strategies for Neural Network Language Models—0
Incremental and Data-Efficient Concept Formation to Support Masked Word Prediction—0
Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs—0
Incrementally Updating the SMT Reordering Model—0
Incremental Monoidal Grammars—0
Incremental Object Detection with CLIP—0
Incremental Query Generation—0
Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training—0
Incremental Semantic Role Labeling with Tree Adjoining Grammar—0
Incremental Sequence Classification with Temporal Consistency—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