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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7751–7800 of 17610 papers

TitleStatusHype
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model—0
HIINT: Historical, Intra- and Inter- personal Dynamics Modeling with Cross-person Memory Transformer—0
HiLight: Technical Report on the Motern AI Video Language Model—0
Hindi-English Code-Switching Speech Corpus—0
HindiLLM: Large Language Model for Hindi—0
"Hinglish" Language -- Modeling a Messy Code-Mixed Language—0
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA—0
Historical German Text Normalization Using Type- and Token-Based Language Modeling—0
History Based Unsupervised Data Oriented Parsing—0
History, Development, and Principles of Large Language Models-An Introductory Survey—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information—0
HIT at SemEval-2022 Task 2: Pre-trained Language Model for Idioms Detection—0
HIT-SCIR at SemEval-2020 Task 5: Training Pre-trained Language Model with Pseudo-labeling Data for Counterfactuals Detection—0
HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10—0
HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield Pooling—0
HM3: Heterogeneous Multi-Class Model Merging—0
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides—0
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems—0
HMoE: Heterogeneous Mixture of Experts for Language Modeling—0
HOIGPT: Learning Long-Sequence Hand-Object Interaction with Language Models—0
Holaaa!! writin like u talk is kewl but kinda hard 4 NLP—0
HoliSafe: Holistic Safety Benchmarking and Modeling with Safety Meta Token for Vision-Language Model—0
Homonym normalisation by word sense clustering: a case in Japanese—0
Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition—0
Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model—0
HoneyGPT: Breaking the Trilemma in Terminal Honeypots with Large Language Model—0
HOP+: History-enhanced and Order-aware Pre-training for Vision-and-Language Navigation—0
HouseLLM: LLM-Assisted Two-Phase Text-to-Floorplan Generation—0
HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset—0
HouYi: An open-source large language model specially designed for renewable energy and carbon neutrality field—0
How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment—0
How and where does CLIP process negation?—0
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse—0
How Chinese are Chinese Language Models? The Puzzling Lack of Language Policy in China's LLMs—0
How Context Affects Language Models' Factual Predictions—0
How Does Adversarial Fine-Tuning Benefit BERT?—0
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers—0
How Does Code Pretraining Affect Language Model Task Performance?—0
How does the pre-training objective affect what large language models learn about linguistic properties?—0
How do Hyenas deal with Human Speech? Speech Recognition and Translation with ConfHyena—0
How Do Large Language Monkeys Get Their Power (Laws)?—0
How do QA models combine knowledge from LM and 100 passages?—0
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance—0
How do Transformers perform In-Context Autoregressive Learning?—0
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites—0
How few is too few? Determining the minimum acceptable number of LSA dimensions to visualise text cohesion with Lex—0
How fine can fine-tuning be? Learning efficient language models—0
How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics—0
How Good are Commercial Large Language Models on African Languages?—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