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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8451–8500 of 17610 papers

TitleStatusHype
Drop the beat! Freestyler for Accompaniment Conditioned Rapping Voice Generation—0
DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction—0
DrugLLM: Open Large Language Model for Few-shot Molecule Generation—0
Drug Repurposing for Cancer: An NLP Approach to Identify Low-Cost Therapies—0
DSL Shared Task 2016: Perfect Is The Enemy of Good Language Discrimination Through Expectation--Maximization and Chunk-based Language Model—0
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs—0
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design—0
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines—0
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog—0
Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition—0
Dual Debiasing: Remove Stereotypes and Keep Factual Gender for Fair Language Modeling and Translation—0
On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR—0
Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models—0
Dual Language Models for Code Switched Speech Recognition—0
MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning—0
Dual Mechanism Priming Effects in Hindi Word Order—0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension—0
Learning to Prompt Your Domain for Vision-Language Models—0
Dual-State Capsule Networks for Text Classification—0
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion—0
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization—0
DubWise: Video-Guided Speech Duration Control in Multimodal LLM-based Text-to-Speech for Dubbing—0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
Dudley North visits North London: Learning When to Transliterate to Arabic—0
Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence—0
Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech—0
Dutch Humor Detection by Generating Negative Examples—0
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
DYNA: Disease-Specific Language Model for Variant Pathogenicity—0
DynaMaR: Dynamic Prompt with Mask Token Representation—0
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks—0
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices—0
Dynamically Learned Test-Time Model Routing in Language Model Zoos with Service Level Guarantees—0
Dynamic Behaviour of Connectionist Speech Recognition with Strong Latency Constraints—0
Dynamic Cell Structure via Recursive-Recurrent Neural Networks—0
Dynamic Code Orchestration: Harnessing the Power of Large Language Models for Adaptive Script Execution—0
Dynamic Context-Aware Streaming Pretrained Language Model For Inverse Text Normalization—0
Dynamic Fusion: Attentional Language Model for Neural Machine Translation—0
NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning—0
Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits—0
Dynamic Inference With Grounding Based Vision and Language Models—0
Dynamic Information Sub-Selection for Decision Support—0
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Dynamic Language Models for Streaming Text—0
Dynamic Large Language Models on Blockchains—0
Dynamic Masking Rate Schedules for MLM Pretraining—0
Dynamic Motion Synthesis: Masked Audio-Text Conditioned Spatio-Temporal Transformers—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
Dynamic Parallel Tree Search for Efficient LLM Reasoning—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