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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11951–12000 of 17610 papers

TitleStatusHype
What do LLMs Know about Financial Markets? A Case Study on Reddit Market Sentiment Analysis—0
ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models—0
Controllable Text Generation with Language Constraints—0
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?—0
Is GPT-3 a Good Data Annotator?Code0
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities—0
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
AnyTOD: A Programmable Task-Oriented Dialog System—0
A Measure-Theoretic Characterization of Tight Language Models—0
EIT: Enhanced Interactive TransformerCode0
Identifying and Manipulating the Personality Traits of Language Models—0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
Perplexed by Quality: A Perplexity-based Method for Adult and Harmful Content Detection in Multilingual Heterogeneous Web Data—0
Language Modeling with Latent Situations—0
KronA: Parameter Efficient Tuning with Kronecker Adapter—0
Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysis—0
Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters—0
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
Natural Language to Code Generation in Interactive Data Science Notebooks—0
KNIFE: Distilling Reasoning Knowledge From Free-Text Rationales—0
Mu^2SLAM: Multitask, Multilingual Speech and Language Models—0
Very Large Language Model as a Unified Methodology of Text MiningCode0
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning—0
Improved Long-Form Spoken Language Translation with Large Language Models—0
Explanation Regeneration via Information BottleneckCode0
An overview of open source Deep Learning-based libraries for Neuroscience—0
Language model acceptability judgements are not always robust to context—0
Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing—0
Claim Optimization in Computational ArgumentationCode0
Homonymy Information for English WordNetCode0
ALERT: Adapting Language Models to Reasoning Tasks—0
Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language—0
LegalRelectra: Mixed-domain Language Modeling for Long-range Legal Text Comprehension—0
POIBERT: A Transformer-based Model for the Tour Recommendation Problem—0
Joint processing of linguistic properties in brains and language modelsCode0
The Effects of In-domain Corpus Size on pre-training BERTCode0
DeepJoin: Joinable Table Discovery with Pre-trained Language Models—0
Improving Chess Commentaries by Combining Language Models with Symbolic Reasoning Engines—0
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models—0
Attention as a Guide for Simultaneous Speech TranslationCode0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference—0
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning—0
MANTa: Efficient Gradient-Based Tokenization for Robust End-to-End Language Modeling—0
Technical Report -- Competition Solution for Prompt Tuning using Pretrained Language Model—0
The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique—0
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?Code0
Deep Image Style Transfer from Freeform Text—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