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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2851–2900 of 17610 papers

TitleStatusHype
TypeT5: Seq2seq Type Inference using Static AnalysisCode1
Jump to Conclusions: Short-Cutting Transformers With Linear TransformationsCode1
Rethinking Model Ensemble in Transfer-based Adversarial AttacksCode1
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!Code1
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language DescriptionsCode1
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilityCode1
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language ModelsCode1
Iterative Few-shot Semantic Segmentation from Image Label TextCode1
Stylometric Detection of AI-Generated Text in Twitter TimelinesCode1
A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text ClassificationCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion TasksCode1
Prismer: A Vision-Language Model with Multi-Task ExpertsCode1
Investigating the Translation Performance of a Large Multilingual Language Model: the Case of BLOOMCode1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue GenerationCode1
BrainBERT: Self-supervised representation learning for intracranial recordingsCode1
The ROOTS Search Tool: Data Transparency for LLMsCode1
Pretraining De-Biased Language Model with Large-scale Click Logs for Document RankingCode1
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled DataCode1
Vision-Language Generative Model for View-Specific Chest X-ray GenerationCode1
Federated Learning for ASR based on Wav2vec 2.0Code1
Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion InferenceCode1
Pretraining Language Models with Human PreferencesCode1
Learning Performance-Improving Code EditsCode1
SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource DomainsCode1
Guiding Pretraining in Reinforcement Learning with Large Language ModelsCode1
The Wisdom of Hindsight Makes Language Models Better Instruction FollowersCode1
In-Context Learning with Many Demonstration ExamplesCode1
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model StudyCode1
UDApter -- Efficient Domain Adaptation Using AdaptersCode1
Representation Deficiency in Masked Language ModelingCode1
GLADIS: A General and Large Acronym Disambiguation BenchmarkCode1
Bioformer: an efficient transformer language model for biomedical text miningCode1
Large Language Models Can Be Easily Distracted by Irrelevant ContextCode1
Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical DistillationCode1
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text PretrainingCode1
Unifying Molecular and Textual Representations via Multi-task Language ModellingCode1
On Pre-trained Language Models for AntibodyCode1
Prompt-Based Editing for Text Style TransferCode1
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpusCode1
Byte Pair Encoding for Symbolic MusicCode1
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context LearningCode1
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-EfficientCode1
Domain-Agnostic Molecular Generation with Chemical FeedbackCode1
GPU-based Private Information Retrieval for On-Device Machine Learning InferenceCode1
ExaRanker: Explanation-Augmented Neural RankerCode1
ViDeBERTa: A powerful pre-trained language model for VietnameseCode1
Lexi: Self-Supervised Learning of the UI LanguageCode1
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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