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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4701–4750 of 17610 papers

TitleStatusHype
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting—0
Discrete Diffusion Language Model for Long Text Summarization—0
High Fidelity Text-to-Speech Via Discrete Tokens Using Token Transducer and Group Masked Language Model—0
LABOR-LLM: Language-Based Occupational Representations with Large Language Models—0
From Distributional to Overton Pluralism: Investigating Large Language Model AlignmentCode0
Human-Object Interaction from Human-Level Instructions—0
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial DesignCode0
TRAWL: Tensor Reduced and Approximated Weights for Large Language ModelsCode0
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy TrainingCode0
Accelerating Clinical Evidence Synthesis with Large Language Models—0
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse GradientsCode1
Semi-supervised classification of dental conditions in panoramic radiographs using large language model and instance segmentation: A real-world dataset evaluation—0
VarBench: Robust Language Model Benchmarking Through Dynamic Variable PerturbationCode0
CogMG: Collaborative Augmentation Between Large Language Model and Knowledge GraphCode1
Retrieval-style In-Context Learning for Few-shot Hierarchical Text ClassificationCode1
Multi-property Steering of Large Language Models with Dynamic Activation CompositionCode1
Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model—0
The FineWeb Datasets: Decanting the Web for the Finest Text Data at ScaleCode1
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?Code0
AG-LSEC: Audio Grounded Lexical Speaker Error Correction—0
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR—0
Understanding Language Model Circuits through Knowledge Editing—0
Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks—0
Native Design Bias: Studying the Impact of English Nativeness on Language Model PerformanceCode0
Enhancing Tool Retrieval with Iterative Feedback from Large Language ModelsCode0
Improving Robustness of LLM-based Speech Synthesis by Learning Monotonic Alignment—0
Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-LevelsCode0
Classification of Geological Borehole Descriptions Using a Domain Adapted Large Language Model—0
Modulating Language Model Experiences through Frictions—0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
GPT-4V Explorations: Mining Autonomous Driving—0
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
Finding Transformer Circuits with Edge PruningCode2
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
C-LLM: Learn to Check Chinese Spelling Errors Character by CharacterCode1
UniCoder: Scaling Code Large Language Model via Universal Code—0
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance—0
AnnotatedTables: A Large Tabular Dataset with Language Model Annotations—0
RaTEScore: A Metric for Radiology Report GenerationCode4
Does Cross-Cultural Alignment Change the Commonsense Morality of Language Models?—0
Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers—0
Large Vocabulary Size Improves Large Language Models—0
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness—0
Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models—0
Long Context Transfer from Language to VisionCode4
Inducing Group Fairness in Prompt-Based Language Model Decisions—0
Evaluation of Language Models in the Medical Context Under Resource-Constrained SettingsCode0
RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository ScaleCode1
Understanding and Mitigating Tokenization Bias in Language Models—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