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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 27012750 of 17610 papers

TitleStatusHype
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl DataCode1
CC-Riddle: A Question Answering Dataset of Chinese Character RiddlesCode1
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and GenerationCode1
EscapeBench: Pushing Language Models to Think Outside the BoxCode1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
Entity-aware Transformers for Entity SearchCode1
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music RetrievalCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
Entity Tracking in Language ModelsCode1
In-Context Alignment: Chat with Vanilla Language Models Before Fine-TuningCode1
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation MethodCode1
In-Context Learning with Many Demonstration ExamplesCode1
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human PreferencesCode1
Enhancing Vision-Language Model with Unmasked Token AlignmentCode1
Incorporating Large Language Models into Production Systems for Enhanced Task Automation and FlexibilityCode1
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout GroupsCode1
Entropy-Regularized Token-Level Policy Optimization for Language Agent ReinforcementCode1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market DomainCode1
Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time CorrectionCode1
Does It Make Sense? And Why? A Pilot Study for Sense Making and ExplanationCode1
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuningCode1
InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language ModelCode1
CFBenchmark: Chinese Financial Assistant Benchmark for Large Language ModelCode1
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
CFGPT: Chinese Financial Assistant with Large Language ModelCode1
Acoustic Prompt Tuning: Empowering Large Language Models with Audition CapabilitiesCode1
RealFormer: Transformer Likes Residual AttentionCode1
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
-former: Infinite Memory TransformerCode1
Enhancing Indic Handwritten Text Recognition Using Global Semantic InformationCode1
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-GenerationCode1
Enhancing Domain Adaptation through Prompt Gradient AlignmentCode1
Enhancing Dialogue Generation via Dynamic Graph Knowledge AggregationCode1
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-TuningCode1
Chain of Images for Intuitively ReasoningCode1
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous SourcesCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
XMoE: Sparse Models with Fine-grained and Adaptive Expert SelectionCode1
Enhancing Clinical BERT Embedding using a Biomedical Knowledge BaseCode1
An Analysis and Mitigation of the Reversal CurseCode1
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics GraphCode1
Interaction-Aware Prompting for Zero-Shot Spatio-Temporal Action DetectionCode1
Enhancing Conversational Search: Large Language Model-Aided Informative Query RewritingCode1
Intermediate Training of BERT for Product MatchingCode1
A Comparison of Methods for OOV-word Recognition on a New Public DatasetCode1
Argmax Flows and Multinomial Diffusion: Learning Categorical DistributionsCode1
Enhancing Biomedical Relation Extraction with DirectionalityCode1
Enhancing Perception of Key Changes in Remote Sensing Image Change CaptioningCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified