SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9651–9700 of 17610 papers

TitleStatusHype
LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification—0
LRG at SemEval-2020 Task 7: Assessing the Ability of BERT and Derivative Models to Perform Short-Edits based Humor Grading—0
LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment—0
LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec—0
LSH Softmax: Sub-Linear Learning and Inference of the Softmax Layer in Deep Architectures—0
Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging—0
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues—0
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
LSTM Neural Reordering Feature for Statistical Machine Translation—0
LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better—0
LT3 at SemEval-2020 Task 7: Comparing Feature-Based and Transformer-Based Approaches to Detect Funny Headlines—0
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles—0
LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking—0
LucidDreaming: Controllable Object-Centric 3D Generation—0
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM—0
Lumos : Empowering Multimodal LLMs with Scene Text Recognition—0
Luna: An Evaluation Foundation Model to Catch Language Model Hallucinations with High Accuracy and Low Cost—0
LUT Tensor Core: A Software-Hardware Co-Design for LUT-Based Low-Bit LLM Inference—0
LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish—0
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications—0
LVP-CLIP:Revisiting CLIP for Continual Learning with Label Vector Pool—0
LV-ROVER: Lexicon Verified Recognizer Output Voting Error Reduction—0
M2D: A Multi-modal Framework for Automatic Medical Diagnosis—0
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems—0
MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction—0
MacBERTh: Development and Evaluation of a Historically Pre-trained Language Model for English (1450-1950)—0
Machine learning can guide experimental approaches for protein digestibility estimations—0
Machine Reading Comprehension for Answer Re-Ranking in Customer Support Chatbots—0
Machine Reading Comprehension: Generative or Extractive Reader?—0
Machine Translation Experiments on PADIC: A Parallel Arabic DIalect Corpus—0
Machine Translation for Subtitling: A Large-Scale Evaluation—0
Machine Translation from Spoken Language to Sign Language using Pre-trained Language Model as Encoder—0
Machine Translation Reference-less Evaluation using YiSi-2 with Bilingual Mappings of Massive Multilingual Language Model—0
Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions—0
Mac-Morpho Revisited: Towards Robust Part-of-Speech Tagging—0
MAESTRO: Matched Speech Text Representations through Modality Matching—0
MAFIA: Multi-Adapter Fused Inclusive LanguAge Models—0
MAGDA: Multi-agent guideline-driven diagnostic assistance—0
MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities—0
Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment—0
MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization—0
Magnushammer: A Transformer-Based Approach to Premise Selection—0
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian—0
Maintaining Journalistic Integrity in the Digital Age: A Comprehensive NLP Framework for Evaluating Online News Content—0
MAIRA-1: A specialised large multimodal model for radiology report generation—0
Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition—0
Make Every Move Count: LLM-based High-Quality RTL Code Generation Using MCTS—0
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation—0
Make Large Language Model a Better Ranker—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