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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5901–5950 of 17610 papers

TitleStatusHype
Analyzing Linguistic Knowledge in Sequential Model of Sentence—0
Analyzing Optimization for Statistical Machine Translation: MERT Learns Verbosity, PRO Learns Length—0
Analyzing Pokémon and Mario Streamers' Twitch Chat with LLM-based User Embeddings—0
Analyzing Information Leakage of Updates to Natural Language Models—0
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models—0
Analyzing Similarity Metrics for Data Selection for Language Model Pretraining—0
Analyzing the Efficacy of an LLM-Only Approach for Image-based Document Question Answering—0
Analyzing the Implicit Position Encoding Ability of Transformer Decoder—0
Analyzing the Impact of Fake News on the Anticipated Outcome of the 2024 Election Ahead of Time—0
Analyzing the Performance of ChatGPT in Cardiology and Vascular Pathologies—0
Analyzing the Roles of Language and Vision in Learning from Limited Data—0
Analyzing the Structure of Attention in a Transformer Language Model—0
Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks—0
An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets—0
An analysis of incorporating an external language model into a sequence-to-sequence model—0
An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language—0
An Anchor Learning Approach for Citation Field Learning—0
An Annotated Dataset and Automatic Approaches for Discourse Mode Identification in Low-resource Bengali Language—0
Anaphora Models and Reordering for Phrase-Based SMT—0
An Application of Pseudo-Log-Likelihoods to Natural Language Scoring—0
An Appraisal-Based Chain-Of-Emotion Architecture for Affective Language Model Game Agents—0
An Approach to Build Zero-Shot Slot-Filling System for Industry-Grade Conversational Assistants—0
An Approach to Improve Robustness of NLP Systems against ASR Errors—0
Neuradicon: operational representation learning of neuroimaging reports—0
An Assessment of the Impact of OCR Noise on Language Models—0
A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
An Automatic SOAP Classification System Using Weakly Supervision And Transfer Learning—0
An Autonomous Large Language Model Agent for Chemical Literature Data Mining—0
Anchor-based Robust Finetuning of Vision-Language Models—0
Anchored Diffusion Language Model—0
Anchor function: a type of benchmark functions for studying language models—0
Anchor & Transform: Learning Sparse Representations of Discrete Objects—0
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies—0
AND does not mean OR: Using Formal Languages to Study Language Models' Representations—0
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking—0
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features—0
An Effective Data Creation Pipeline to Generate High-quality Financial Instruction Data for Large Language Model—0
An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction—0
An Efficient Approach for Machine Translation on Low-resource Languages: A Case Study in Vietnamese-Chinese—0
An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering—0
An Efficient Attention Mechanism for Sequential Recommendation Tasks: HydraRec—0
An efficient language independent toolkit for complete morphological disambiguation—0
An Efficient Language Model Using Double-Array Structures—0
An empathic GPT-based chatbot to talk about mental disorders with Spanish teenagers—0
An Empirical Comparison Between N-gram and Syntactic Language Models for Word Ordering—0
An Empirical Comparison of LM-based Question and Answer Generation Methods—0
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation—0
An Empirical Exploration in Quality Filtering of Text Data—0
An Empirical Exploration of Local Ordering Pre-training for Structured Prediction—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