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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12851–12900 of 17610 papers

TitleStatusHype
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction—0
NTPP: Generative Speech Language Modeling for Dual-Channel Spoken Dialogue via Next-Token-Pair Prediction—0
NTULM: Enriching Social Media Text Representations with Non-Textual Units—0
NTU Speechlab LLM-Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025—0
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
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task—0
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task—0
SKDBERT: Compressing BERT via Stochastic Knowledge Distillation—0
Skeleton Key: Image Captioning by Skeleton-Attribute Decomposition—0
Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework—0
Skill Learning Using Process Mining for Large Language Model Plan Generation—0
SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model—0
Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation—0
Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers—0
Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning—0
Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster—0
SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task—0
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling—0
SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly—0
SLAG: Scalable Language-Augmented Gaussian Splatting—0
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training—0
Slaves to the Law of Large Numbers: An Asymptotic Equipartition Property for Perplexity in Generative Language Models—0
SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features—0
SLiC-HF: Sequence Likelihood Calibration with Human Feedback—0
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing—0
Slim Embedding Layers for Recurrent Neural Language Models—0
SlimIPL: Language-Model-Free Iterative Pseudo-Labeling—0
SlimLM: An Efficient Small Language Model for On-Device Document Assistance—0
Slimming Down LLMs Without Losing Their Minds—0
SLM: Bridge the thin gap between speech and text foundation models—0
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs—0
SLM: Learning a Discourse Language Representation with Sentence Unshuffling—0
SLOs-Serve: Optimized Serving of Multi-SLO LLMs—0
Slot-VLM: SlowFast Slots for Video-Language Modeling—0
Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay—0
SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-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