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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 31513200 of 17610 papers

TitleStatusHype
Enhancing Clinical BERT Embedding using a Biomedical Knowledge BaseCode1
XMoE: Sparse Models with Fine-grained and Adaptive Expert SelectionCode1
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
Codified audio language modeling learns useful representations for music information retrievalCode1
Content-Based Collaborative Generation for Recommender SystemsCode1
Enhancing Biomedical Relation Extraction with DirectionalityCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense GraphsCode1
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics GraphCode1
Enhancing Indic Handwritten Text Recognition Using Global Semantic InformationCode1
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal ModelsCode1
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive ModelsCode1
Endowing Protein Language Models with Structural KnowledgeCode1
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error CorrectionCode1
CogBench: a large language model walks into a psychology labCode1
End-to-end Audio-visual Speech Recognition with ConformersCode1
Enabling Language Models to Fill in the BlanksCode1
Emulated Disalignment: Safety Alignment for Large Language Models May Backfire!Code1
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product OperatorsCode1
CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language ModelsCode1
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?Code1
Cognitive Reframing of Negative Thoughts through Human-Language Model InteractionCode1
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge AlignmentCode1
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question AnsweringCode1
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and QuantizationCode1
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding MatchingCode1
End-to-End Automatic Speech Recognition for GujaratiCode1
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News ArticlesCode1
Empower Entity Set Expansion via Language Model ProbingCode1
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent CollaborationCode1
Empowering Large Language Model Agents through Action LearningCode1
EmojiLM: Modeling the New Emoji LanguageCode1
EMO: Earth Mover Distance Optimization for Auto-Regressive Language ModelingCode1
Emotion-Aware Transformer Encoder for Empathetic Dialogue GenerationCode1
Empowering Large Language Model for Continual Video Question Answering with Collaborative PromptingCode1
Co-Learning: Code Learning for Multi-Agent Reinforcement Collaborative Framework with Conversational Natural Language InterfacesCode1
A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based PerspectiveCode1
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient FrameworkCode1
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
Algorithmic progress in language modelsCode1
eMLM: A New Pre-training Objective for Emotion Related TasksCode1
EMMA: Efficient Visual Alignment in Multi-Modal LLMsCode1
Collaborative Large Language Model for Recommender SystemsCode1
Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LMCode1
BiLD: Bi-directional Logits Difference Loss for Large Language Model DistillationCode1
Collaborative Retrieval for Large Language Model-based Conversational Recommender SystemsCode1
Emergent Analogical Reasoning in Large Language ModelsCode1
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language ModelsCode1
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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