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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10301–10350 of 17610 papers

TitleStatusHype
NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative—0
Narrow Transformer: StarCoder-Based Java-LM For Desktop—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
NASTEA: Investigating Narrative Schemas through Annotated Entities—0
Natural Language Decomposition and Interpretation of Complex Utterances—0
Natural Language Descriptions for Human Activities in Video Streams—0
Natural Language Generation from Pictographs—0
Natural Language Generation through Character-based RNNs with Finite-state Prior Knowledge—0
Natural Language Instructions for Intuitive Human Interaction with Robotic Assistants in Field Construction Work—0
Natural Language Model Re-usability for Scaling to Different Domains—0
Natural language processing for clusterization of genes according to their functions—0
Natural Language to Code Generation in Interactive Data Science Notebooks—0
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving—0
Naver Labs Europe’s Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020—0
NAVER Machine Translation System for WAT 2015—0
NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation—0
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models—0
Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning—0
Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models—0
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning—0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space—0
NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extraction—0
Nearest Class-Center Simplification through Intermediate Layers—0
Nearest Neighbor Language Models for Stylistic Controllable Generation—0
Nearest Neighbor Speculative Decoding for LLM Generation and Attribution—0
Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters—0
Needle in the Haystack for Memory Based Large Language Models—0
Negation: A Pink Elephant in the Large Language Models' Room?—0
Negative-Prompt-driven Alignment for Generative Language Model—0
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings—0
Nemotron-4 15B Technical Report—0
Nepali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for Nepali Text Classification—0
NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging—0
Healthcare NER Models Using Language Model Pretraining—0
NERVE at ROCLING 2022 Shared Task: A Comparison of Three Named Entity Recognition Frameworks Based on Language Model and Lexicon Approach—0
Networks of Networks: Complexity Class Principles Applied to Compound AI Systems Design—0
Network Visualization of ChatGPT Research: a study based on term and keyword co-occurrence network analysis—0
Neural and rule-based Finnish NLP models---expectations, experiments and experiences—0
Neural Architecture Search for Natural Language Understanding—0
Neural Borrowing Detection with Monolingual Lexical Models—0
Neural Composition: Learning to Generate from Multiple Models—0
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model—0
Neural DrugNet—0
Neural Embeddings for Text—0
Neural-FST Class Language Model for End-to-End Speech Recognition—0
Neural Generation for Czech: Data and Baselines—0
Neural Grammatical Error Correction with Finite State Transducers—0
Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction—0
Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision—0
Neural Headline Generation on Abstract Meaning Representation—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