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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7651–7675 of 17610 papers

TitleStatusHype
HiLight: Technical Report on the Motern AI Video Language Model—0
Enhancing Low-Resource NMT with a Multilingual Encoder and Knowledge Distillation: A Case StudyCode0
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
AutoTask: Task Aware Multi-Faceted Single Model for Multi-Task Ads Relevance—0
Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation—0
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training—0
LETS-C: Leveraging Text Embedding for Time Series Classification—0
VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving—0
Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions—0
Using Grammar Masking to Ensure Syntactic Validity in LLM-based Modeling Tasks—0
Variational Best-of-N Alignment—0
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing—0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
GenFollower: Enhancing Car-Following Prediction with Large Language Models—0
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory—0
Artificial Intuition: Efficient Classification of Scientific Abstracts—0
Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning—0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian—0
MST5 -- Multilingual Question Answering over Knowledge GraphsCode0
Large Language Model Recall Uncertainty is Modulated by the Fan EffectCode0
OneDiff: A Generalist Model for Image Difference Captioning—0
Large Language Models for Judicial Entity Extraction: A Comparative Study—0
On Speeding Up Language Model Evaluation—0
E^2CFD: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via 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