SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9601–9650 of 17610 papers

TitleStatusHype
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models—0
KL-Divergence Guided Temperature SamplingCode0
The Information Pathways Hypothesis: Transformers are Dynamic Self-EnsemblesCode1
Revisiting the Role of Language Priors in Vision-Language ModelsCode1
Preference-grounded Token-level Guidance for Language Model Fine-tuningCode1
Vocabulary-free Image ClassificationCode1
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayCode4
Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formulaCode0
Faster Causal Attention Over Large Sequences Through Sparse Flash AttentionCode1
Graph-Level Embedding for Time-Evolving Graphs—0
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
Explanation Graph Generation via Generative Pre-training over Synthetic GraphsCode0
Exposing Attention Glitches with Flip-Flop Language Modeling—0
CapText: Large Language Model-based Caption Generation From Image Context and Description—0
How Generative Spoken Language Modeling Encodes Noisy Speech: Investigation from Phonetics to Syntactics—0
AWQ: Activation-aware Weight Quantization for LLM Compression and AccelerationCode6
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning—0
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression—0
Training-free Neural Architecture Search for RNNs and TransformersCode1
Adverbs, Surprisingly—0
An Invariant Learning Characterization of Controlled Text GenerationCode0
IDAS: Intent Discovery with Abstractive SummarizationCode1
BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline—0
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured DataCode1
Multilingual Multi-Figurative Language DetectionCode1
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised TrainingCode2
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal RepresentationCode1
Neuron to Graph: Interpreting Language Model Neurons at ScaleCode0
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of MindCode0
Perception and Semantic Aware Regularization for Sequential Confidence CalibrationCode1
LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model PromptingCode0
Red Teaming Language Model Detectors with Language ModelsCode1
Human or Not? A Gamified Approach to the Turing Test—0
RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment—0
Catalysis distillation neural network for the few shot open catalyst challenge—0
Likelihood-Based Diffusion Language ModelsCode1
LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual ImagesCode1
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructionCode2
Blockwise Parallel Transformer for Large Context ModelsCode2
GPT4GEO: How a Language Model Sees the World's Geography—0
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuningCode0
Empirical Sufficiency Lower Bounds for Language Modeling with Locally-Bootstrapped Semantic StructuresCode0
Universality and Limitations of Prompt Tuning—0
KEYword based Sampling (KEYS) for Large Language Models—0
PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation—0
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language ModelsCode1
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey—0
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and DatasetCode2
Direct Preference Optimization: Your Language Model is Secretly a Reward ModelCode6
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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