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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11301–11350 of 17610 papers

TitleStatusHype
From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models—0
Zero-shot Visual Question Answering with Language Model FeedbackCode0
Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across LanguagesCode0
Leveraging Domain Knowledge for Inclusive and Bias-aware Humanitarian Response Entry ClassificationCode0
SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)—0
Large language models improve Alzheimer's disease diagnosis using multi-modality data—0
Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing—0
Masked and Permuted Implicit Context Learning for Scene Text RecognitionCode0
RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting—0
BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model—0
Improving Scheduled Sampling for Neural Transducer-based ASR—0
VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation—0
Textless Speech-to-Speech Translation With Limited Parallel DataCode0
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language ModelCode0
Just CHOP: Embarrassingly Simple LLM Compression—0
Self-Evolution Learning for Discriminative Language Model PretrainingCode0
Neural Summarization of Electronic Health Records—0
Alt-Text with Context: Improving Accessibility for Images on Twitter—0
Trade-Offs Between Fairness and Privacy in Language ModelingCode0
Lexinvariant Language Models—0
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language ModelsCode0
Spoken Question Answering and Speech Continuation Using Spectrogram-Powered LLMCode0
Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization—0
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions—0
Getting MoRE out of Mixture of Language Model Reasoning Experts—0
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning—0
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image ManipulationCode0
Large Language Models are Few-Shot Health Learners—0
This Land is Your, My Land: Evaluating Geopolitical Biases in Language ModelsCode0
Allies: Prompting Large Language Model with Beam Search—0
Eliciting the Translation Ability of Large Language Models via Multilingual Finetuning with Translation Instructions—0
Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic SystemsCode0
Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering—0
Estimating class separability of text embeddings with persistent homology—0
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
Estimating Large Language Model Capabilities without Labeled Test DataCode0
Drafting Event Schemas using Language Models—0
Emergent inabilities? Inverse scaling over the course of pretraining—0
In-Context Demonstration Selection with Cross Entropy Difference—0
Focus Your Attention (with Adaptive IIR Filters)—0
Dynamic Masking Rate Schedules for MLM Pretraining—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought—0
Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation—0
APPLS: Evaluating Evaluation Metrics for Plain Language SummarizationCode0
GenSpectrum Chat: Data Exploration in Public Health Using Large Language Models—0
Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction—0
Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models—0
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-rankerCode0
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate ModelsCode0
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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