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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9926–9950 of 17610 papers

TitleStatusHype
Minimally-Supervised Relation Induction from Pre-trained Language Model—0
Minimally-Supervised Speech Synthesis with Conditional Diffusion Model and Language Model: A Comparative Study of Semantic Coding—0
Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator—0
Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition—0
Minimum Translation Modeling with Recurrent Neural Networks—0
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition—0
Mining and discovering biographical information in Difangzhi with a language-model-based approach—0
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry—0
Mining Local Gazetteers of Literary Chinese with CRF and Pattern based Methods for Biographical Information in Chinese History—0
Mining Logical Event Schemas From Pre-Trained Language Models—0
MiniVLM: A Smaller and Faster Vision-Language Model—0
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction—0
Minor DPO reject penalty to increase training robustness—0
Minority Positive Sampling for Switching Points - an Anecdote for the Code-Mixing Language Modeling—0
Minor SFT loss for LLM fine-tune to increase performance and reduce model deviation—0
MirasText: An Automatically Generated Text Corpus for Persian—0
MisinfoEval: Generative AI in the Era of "Alternative Facts"—0
Misinformation Detection in Social Media Video Posts—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
Misspelling Correction with Pre-trained Contextual Language Model—0
MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection—0
Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language—0
Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering—0
Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing—0
Mitigating Gender Bias in Contextual Word Embeddings—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