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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16551–16600 of 17610 papers

TitleStatusHype
Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?—0
Comparing in context: Improving cosine similarity measures with a metric tensor—0
Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss—0
Comparing Machines and Children: Using Developmental Psychology Experiments to Assess the Strengths and Weaknesses of LaMDA Responses—0
Comparing MT Approaches for Text Normalization—0
Comparing Neural- and N-Gram-Based Language Models for Word Segmentation—0
Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation—0
Comparing Representations of Semantic Roles for String-To-Tree Decoding—0
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures—0
Comparing the Utility, Preference, and Performance of Course Material Search Functionality and Retrieval-Augmented Generation Large Language Model (RAG-LLM) AI Chatbots in Information-Seeking Tasks—0
Comparing Top-Down and Bottom-Up Neural Generative Dependency Models—0
Comparison between two models of language for the automatic phonetic labeling of an undocumented language of the South-Asia: the case of Mo Piu—0
Comparison of Decoding Strategies for CTC Acoustic Models—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Comparison of Modified Kneser-Ney and Witten-Bell Smoothing Techniques in Statistical Language Model of Bahasa Indonesia—0
Comparison of Turkish Word Representations Trained on Different Morphological Forms—0
Comparison Study Between Token Classification and Sequence Classification In Text Classification—0
COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling—0
Compass: Large Multilingual Language Model for South-east Asia—0
Competing LLM Agents in a Non-Cooperative Game of Opinion Polarisation—0
Compilable Neural Code Generation with Compiler Feedback—0
Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation—0
Complete Chess Games Enable LLM Become A Chess Master—0
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents—0
ComplexDec: A Domain-robust High-fidelity Neural Audio Codec with Complex Spectrum Modeling—0
ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity—0
Complex Ontology Matching with Large Language Model Embeddings—0
Complex Reading Comprehension Through Question Decomposition—0
Complex System Diagnostics Using a Knowledge Graph-Informed and Large Language Model-Enhanced Framework—0
CompLx@SMM4H’22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets—0
ComPO: Community Preferences for Language Model Personalization—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
Composing Structure-Aware Batches for Pairwise Sentence Classification—0
Planner3D: LLM-enhanced graph prior meets 3D indoor scene explicit regularization—0
Compositional Foundation Models for Hierarchical Planning—0
Compositional Hardness of Code in Large Language Models -- A Probabilistic Perspective—0
Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication—0
Composition of Word Representations Improves Semantic Role Labelling—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
Compressing Deep Neural Networks via Layer Fusion—0
Compressing Language Models using Doped Kronecker Products—0
Compressing Sentence Representation via Homomorphic Projective Distillation—0
Compressing Sentence Representation with maximum Coding Rate Reduction—0
Compression of Recurrent Neural Networks for Efficient Language Modeling—0
Compressive Performers in Language Modelling—0
Computational Approaches to Sentence Completion—0
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems—0
Computational Argumentation Synthesis as a Language Modeling Task—0
Computational Bottlenecks of Training Small-scale Large Language Models—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