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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7951–8000 of 17610 papers

TitleStatusHype
Impact of Gender Debiased Word Embeddings in Language Modeling—0
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study—0
Impact of Tone-Aware Explanations in Recommender Systems—0
ImPaKT: A Dataset for Open-Schema Knowledge Base Construction—0
IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator—0
Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation—0
Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control—0
Implementation of a Generative AI Assistant in K-12 Education: The CyberScholar Initiative—0
Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: ACDH-CH System Description for the MWSA Shared Task at GlobaLex 2020—0
Implicit Bias of AdamW: _ Norm Constrained Optimization—0
Implicit causality in GPT-2: a case study—0
Implicitly Intersecting Weighted Automata using Dual Decomposition—0
Implicit Semantic Roles in a Multilingual Setting—0
Implicit Sentiment Analysis Based on Chain of Thought Prompting—0
Implicit spoken language diarization—0
Importance of Self-Attention for Sentiment Analysis—0
Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities—0
Improved Algorithms for Differentially Private Language Model Alignment—0
Improved Alignment of Modalities in Large Vision Language Models—0
Improved Arabic Dialect Classification with Social Media Data—0
Improved Chord Recognition by Combining Duration and Harmonic Language Models—0
Improved Decipherment of Homophonic Ciphers—0
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data—0
Improved Iterative Correction for Distant Spelling Errors—0
Improved Language Modeling by Decoding the Past—0
Improved Large Language Model Jailbreak Detection via Pretrained Embeddings—0
Improved Long-Form Spoken Language Translation with Large Language Models—0
Improved low-resource Somali speech recognition by semi-supervised acoustic and language model training—0
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models—0
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer—0
Improved Sentence-Level Arabic Dialect Classification—0
改良語句模型技術於節錄式語音摘要之研究 (Improved Sentence Modeling Techniques for Extractive Speech Summarization) [In Chinese]—0
Improved Spelling Error Detection and Correction for Arabic—0
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model—0
Improved Unbiased Watermark for Large Language Models—0
Improved Visual Grounding through Self-Consistent Explanations—0
Improved Word Embeddings with Implicit Structure Information—0
Improve LLM-based Automatic Essay Scoring with Linguistic Features—0
Improvements to Dependency Parsing Using Automatic Simplification of Data—0
Improvements to the Bayesian Topic N-Gram Models—0
Improvements to the Sequence Memoizer—0
Improve Statistical Machine Translation with Context-Sensitive Bilingual Semantic Embedding Model—0
Better Pre-Training by Reducing Representation Confusion—0
Improving Abstraction in Text Summarization—0
Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model—0
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion—0
Improving Agent Interactions in Virtual Environments with Language Models—0
Improving alignment of dialogue agents via targeted human judgements—0
Improving astroBERT using Semantic Textual Similarity—0
Improving Audio Codec-based Zero-Shot Text-to-Speech Synthesis with Multi-Modal Context and Large 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