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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10501–10550 of 17610 papers

TitleStatusHype
Dodo: Dynamic Contextual Compression for Decoder-only LMs—0
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains—0
Numerically Grounded Language Models for Semantic Error Correction—0
Numerical Optimizations for Weighted Low-rank Estimation on Language Model—0
NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning—0
NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance—0
Nutri-bullets Hybrid: Consensual Multi-document Summarization—0
NuwaTS: a Foundation Model Mending Every Incomplete Time Series—0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching—0
OAEI-LLM-T: A TBox Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching—0
OASIS: Order-Augmented Strategy for Improved Code Search—0
OBJ2TEXT: Generating Visually Descriptive Language from Object Layouts—0
Object-Centric Instruction Augmentation for Robotic Manipulation—0
Object Counts! Bringing Explicit Detections Back into Image Captioning—0
Object Relational Graph with Teacher-Recommended Learning for Video Captioning—0
Objects in Semantic Topology—0
Observations on LLMs for Telecom Domain: Capabilities and Limitations—0
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step—0
Occlusion-Aware 3D Motion Interpretation for Abnormal Behavior Detection—0
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning—0
OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects—0
OCR Error Correction Using Character Correction and Feature-Based Word Classification—0
OCR Language Models with Custom Vocabularies—0
Octo-planner: On-device Language Model for Planner-Action Agents—0
Octopus: On-device language model for function calling of software APIs—0
Octopus v2: On-device language model for super agent—0
Oddballness: universal anomaly detection with language models—0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
ODIST: Open World Classification via Distributionally Shifted Instances—0
OD-Stega: LLM-Based Near-Imperceptible Steganography via Optimized Distributions—0
O-Edit: Orthogonal Subspace Editing for Language Model Sequential Editing—0
Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Offline Reinforcement Learning for Large Scale Language Action Spaces—0
Off-the-shelf ChatGPT is a Good Few-shot Human Motion Predictor—0
OLaLa: Ontology Matching with Large Language Models—0
OLMES: A Standard for Language Model Evaluations—0
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens—0
OLoRA: Orthonormal Low-Rank Adaptation of Large Language Models—0
OmniACT: A Dataset and Benchmark for Enabling Multimodal Generalist Autonomous Agents for Desktop and Web—0
OmniEvalKit: A Modular, Lightweight Toolbox for Evaluating Large Language Model and its Omni-Extensions—0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering—0
Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks—0
OmniVLM: A Token-Compressed, Sub-Billion-Parameter Vision-Language Model for Efficient On-Device Inference—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding—0
OMoS-QA: A Dataset for Cross-Lingual Extractive Question Answering in a German Migration Context—0
On a Benefit of Masked Language Model Pretraining: Robustness to Simplicity Bias—0
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias—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