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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10351–10400 of 17610 papers

TitleStatusHype
Integrating Language Models into Direct Speech Translation: An Inference-Time Solution to Control Gender Inflection—0
ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into Principles—0
PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows—0
Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment—0
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications—0
Locally Differentially Private Document Generation Using Zero Shot PromptingCode0
Self-Guard: Empower the LLM to Safeguard Itself—0
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction—0
Prevalence and prevention of large language model use in crowd work—0
Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce—0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
Unnatural language processing: How do language models handle machine-generated prompts?—0
WebWISE: Web Interface Control and Sequential Exploration with Large Language Models—0
Chain-of-Factors Paper-Reviewer MatchingCode0
Why LLMs Hallucinate, and How to Get (Evidential) Closure: Perceptual, Intensional, and Extensional Learning for Faithful Natural Language Generation—0
Irreducible Curriculum for Language Model Pretraining—0
Generative Pre-trained Transformer for Vietnamese Community-based COVID-19 Question Answering—0
DoGE: Domain Reweighting with Generalization Estimation—0
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation—0
Health Disparities through Generative AI Models: A Comparison Study Using A Domain Specific large language model—0
Counting the Bugs in ChatGPT's Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model—0
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument CompatibilitiesCode0
Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic RulesCode0
Understanding the Inner Workings of Language Models Through Representation Dissimilarity—0
Large Search Model: Redefining Search Stack in the Era of LLMs—0
SpecTr: Fast Speculative Decoding via Optimal Transport—0
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific RewardsCode0
Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language ModelsCode0
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions—0
Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True DistributionCode0
QUDEVAL: The Evaluation of Questions Under Discussion Discourse ParsingCode0
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research—0
Manifold-Preserving Transformers are Effective for Short-Long Range EncodingCode0
Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis—0
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
MoPe: Model Perturbation-based Privacy Attacks on Language Models—0
PHD: Pixel-Based Language Modeling of Historical DocumentsCode0
Which Prompts Make The Difference? Data Prioritization For Efficient Human LLM Evaluation—0
Customising General Large Language Models for Specialised Emotion Recognition Tasks—0
CLMSM: A Multi-Task Learning Framework for Pre-training on Procedural TextCode0
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and BiasCode0
Boosting Unsupervised Machine Translation with Pseudo-Parallel Data—0
Sentiment Analysis Across Multiple African Languages: A Current Benchmark—0
Learning Reward for Physical Skills using Large Language Model—0
MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation—0
RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience VisualizationCode0
Optimizing Retrieval-augmented Reader Models via Token EliminationCode0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
The Past, Present, and Future of Typological Databases in NLP—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