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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 30013050 of 17610 papers

TitleStatusHype
Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment GenerationCode1
Escalation Risks from Language Models in Military and Diplomatic Decision-MakingCode1
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model FinetuningCode1
LSBert: A Simple Framework for Lexical SimplificationCode1
Evaluation Benchmarks for Spanish Sentence RepresentationsCode1
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-TuningCode1
Extracting Latent Steering Vectors from Pretrained Language ModelsCode1
FonBund: A Library for Combining Cross-lingual Phonological Segment DataCode1
Connecting Language and Vision for Natural Language-Based Vehicle RetrievalCode1
M2D2: A Massively Multi-domain Language Modeling DatasetCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
ClimateBert: A Pretrained Language Model for Climate-Related TextCode1
M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment AnalysisCode1
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model PredictionsCode1
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics GraphCode1
Enhancing Clinical BERT Embedding using a Biomedical Knowledge BaseCode1
BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in BiologyCode1
Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge EncodingCode1
Content-Based Collaborative Generation for Recommender SystemsCode1
Enhancing Biomedical Relation Extraction with DirectionalityCode1
Enhancing Conversational Search: Large Language Model-Aided Informative Query RewritingCode1
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical NotesCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
BiomedRAG: A Retrieval Augmented Large Language Model for BiomedicineCode1
Working Memory Capacity of ChatGPT: An Empirical StudyCode1
MarianCG: a code generation transformer model inspired by machine translationCode1
Markovian Transformers for Informative Language ModelingCode1
End-to-End Automatic Speech Recognition for GujaratiCode1
A Fine-tuning Dataset and Benchmark for Large Language Models for Protein UnderstandingCode1
Reinforcement Learning Friendly Vision-Language Model for MinecraftCode1
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language ModelCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge NotesCode1
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
ASSISTGUI: Task-Oriented Desktop Graphical User Interface AutomationCode1
Matching Patients to Clinical Trials with Large Language ModelsCode1
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive LearningCode1
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error CorrectionCode1
Endowing Protein Language Models with Structural KnowledgeCode1
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!Code1
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding MatchingCode1
Enabling Language Models to Fill in the BlanksCode1
MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question ComplexityCode1
Codified audio language modeling learns useful representations for music information retrievalCode1
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question AnsweringCode1
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product OperatorsCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance SegmentationCode1
Enhancing Dialogue Generation via Dynamic Graph Knowledge AggregationCode1
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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