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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11951–12000 of 17610 papers

TitleStatusHype
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images—0
VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models—0
ZCU-NLP at MADAR 2019: Recognizing Arabic Dialects—0
VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis—0
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts—0
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models—0
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models—0
VU-BERT: A Unified framework for Visual Dialog—0
Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning—0
VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model—0
ZEETAD: Adapting Pretrained Vision-Language Model for Zero-Shot End-to-End Temporal Action Detection—0
UPAR: A Kantian-Inspired Prompting Framework for Enhancing Large Language Model Capabilities—0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment—0
WAE_RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence—0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
WAFFLE: Multimodal Floorplan Understanding in the Wild—0
UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis—0
WaLDORf: Wasteless Language-model Distillation On Reading-comprehension—0
UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information—0
Unifying Structure and Language Semantic for Efficient Contrastive Knowledge Graph Completion with Structured Entity Anchors—0
Unveiling Privacy Risks in LLM Agent Memory—0
WalkVLM:Aid Visually Impaired People Walking by Vision Language Model—0
Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability—0
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model—0
Unifying Multimodal Retrieval via Document Screenshot Embedding—0
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model—0
WangLab at MEDIQA-CORR 2024: Optimized LLM-based Programs for Medical Error Detection and Correction—0
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa—0
Unveiling Hidden Links Between Unseen Security Entities—0
Unveiling Disparities in Maternity Care: A Topic Modelling Approach to Analysing Maternity Incident Investigation Reports—0
Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons—0
Source Attribution for Large Language Model-Generated Data—0
Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation—0
ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning—0
Unsupervised word segmentation and lexicon discovery using acoustic word embeddings—0
Watching a Language Model Learning Chess—0
Unifying Input and Output Smoothing in Neural Machine Translation—0
Unsupervised Word Discovery with Segmental Neural Language Models—0
Watermarking Language Models through Language Models—0
Learning to Discover, Ground and Use Words with Segmental Neural Language Models—0
Unsupervised Vocabulary Adaptation for Morph-based Language Models—0
Unsupervised training of maximum-entropy models for lexical selection in rule-based machine translation—0
Unifying Corroborative and Contributive Attributions in Large Language Models—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning for Low-Resource Speech Recognition—0
Unsupervised Text Style Transfer with Content Embeddings—0
Wave Network: An Ultra-Small Language Model—0
WavLLM: Towards Robust and Adaptive Speech Large Language Model—0
Unsupervised Stemming based Language Model for Telugu Broadcast News Transcription—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