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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17401–17450 of 17610 papers

TitleStatusHype
Domain-Adaptive Continued Pre-Training of Small Language Models—0
Domain-aware Neural Language Models for Speech Recognition—0
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification—0
Domain Incremental Lifelong Learning in an Open World—0
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain—0
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release—0
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems—0
Domain Regeneration: How well do LLMs match syntactic properties of text domains?—0
Domain-slot Relationship Modeling using a Pre-trained Language Encoder for Multi-Domain Dialogue State Tracking—0
Domain-Specific Japanese ELECTRA Model Using a Small Corpus—0
Domain-specific knowledge distillation yields smaller and better models for conversational commerce—0
Domain Transfer based Data Augmentation for Neural Query Translation—0
Do Neural Nets Learn Statistical Laws behind Natural Language?—0
Looking Right is Sometimes Right: Investigating the Capabilities of Decoder-only LLMs for Sequence Labeling—0
Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change—0
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation—0
Don't Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information—0
Don’t Forget About Pronouns: Removing Gender Bias in Language Models without Losing Factual Gender Information—0
Don’t Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information—0
Don't Forget It! Conditional Sparse Autoencoder Clamping Works for Unlearning—0
Don't Forget to Connect! Improving RAG with Graph-based Reranking—0
Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration—0
Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning—0
Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic—0
Do People Prefer "Natural" code?—0
Doppelgänger's Watch: A Split Objective Approach to Large Language Models—0
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment—0
DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing—0
Do sequence-to-sequence VAEs learn global features of sentences?—0
Do Sparse Autoencoders Generalize? A Case Study of Answerability—0
Do Transformer Networks Improve the Discovery of Rules from Text?—0
Do Transformers Need Deep Long-Range Memory?—0
Do Transformers Parse while Predicting the Masked Word?—0
Double Visual Defense: Adversarial Pre-training and Instruction Tuning for Improving Vision-Language Model Robustness—0
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference—0
On the Need of Cross Validation for Discourse Relation Classification—0
Do You Trust ChatGPT? -- Perceived Credibility of Human and AI-Generated Content—0
DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image Editing—0
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon—0
Enhancing Jailbreak Attacks with Diversity Guidance—0
DPP-Net: Device-aware Progressive Search for Pareto-optimal Neural Architectures—0
DPRK-BERT: The Supreme Language Model—0
Drafting Event Schemas using Language Models—0
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
BPDec: Unveiling the Potential of Masked Language Modeling Decoder in BERT pretraining—0
DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately—0
DReSD: Dense Retrieval for Speculative Decoding—0
DressCode: Autoregressively Sewing and Generating Garments from Text Guidance—0
DReSS: Data-driven Regularized Structured Streamlining for Large 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