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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4301–4350 of 17610 papers

TitleStatusHype
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model—0
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code—0
CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation—0
Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks—0
Code-Mixed to Monolingual Translation Framework—0
CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Code Representation Learning At Scale—0
Code Representation Pre-training with Complements from Program Executions—0
Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators—0
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences—0
Code-Switching Detection with Data-Augmented Acoustic and Language Models—0
Code-switching in text and speech reveals information-theoretic audience design—0
Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning—0
Code-switching Language Modeling With Bilingual Word Embeddings: A Case Study for Egyptian Arabic-English—0
Code Switching Language Model Using Monolingual Training Data—0
Textual Data Augmentation for Arabic-English Code-Switching Speech Recognition—0
Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition—0
Code Vulnerability Repair with Large Language Model using Context-Aware Prompt Tuning—0
CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation—0
CoDi-2: In-Context Interleaved and Interactive Any-to-Any Generation—0
Coeditor: Leveraging Contextual Changes for Multi-round Code Auto-editing—0
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework—0
COFS: Controllable Furniture layout Synthesis—0
CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics—0
CogLM: Tracking Cognitive Development of Large Language Models—0
COGNET-MD, an evaluation framework and dataset for Large Language Model benchmarks in the medical domain—0
Coherency Improved Explainable Recommendation via Large Language Model—0
Coherent Dialogue with Attention-based Language Models—0
Rethinking Coherence Modeling: Synthetic vs. Downstream Tasks—0
CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation—0
Cold Fusion: Training Seq2Seq Models Together with Language Models—0
Co-learning of Word Representations and Morpheme Representations—0
Collaborating with language models for embodied reasoning—0
Collaborative Semantic Alignment in Recommendation Systems—0
Collaborative Cross-modal Fusion with Large Language Model for Recommendation—0
Collaboratively Constructed Linguistic Resources for Language Variants and their Exploitation in NLP Application -- the case of Tunisian Arabic and the Social Media—0
Collaborative Storytelling with Human Actors and AI Narrators—0
Collaborative Storytelling with Large-scale Neural Language Models—0
Collecting Code-Switched Data from Social Media—0
Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking—0
CoLLEGe: Concept Embedding Generation for Large Language Models—0
Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code—0
CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction—0
Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification—0
ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning—0
Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French—0
Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling—0
Combining, Adapting and Reusing Bi-texts between Related Languages: Application to Statistical Machine Translation (invited talk)—0
Combining Constraint Programming Reasoning with Large Language Model Predictions—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