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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 72517300 of 17610 papers

TitleStatusHype
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content CounterfactuallyCode0
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition0
Veagle: Advancements in Multimodal Representation LearningCode1
Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling0
Excuse me, sir? Your language model is leaking (information)Code1
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?0
Gradable ChatGPT Translation Evaluation0
Lateral Phishing With Large Language Models: A Large Organization Comparative Study0
Sketch-Guided Constrained Decoding for Boosting Blackbox Large Language Models without Logit AccessCode0
Self-Rewarding Language ModelsCode1
VMamba: Visual State Space ModelCode7
Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments0
Evolutionary Computation in the Era of Large Language Model: Survey and RoadmapCode2
Spatial-Temporal Large Language Model for Traffic PredictionCode2
SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language ModelCode2
A Fast, Performant, Secure Distributed Training Framework For Large Language Model0
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation0
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study0
ADCNet: a unified framework for predicting the activity of antibody-drug conjugatesCode1
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images0
Asynchronous Local-SGD Training for Language ModelingCode1
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints0
Vlogger: Make Your Dream A VlogCode1
TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in ConversationCode1
Into the crossfire: evaluating the use of a language model to crowdsource gun violence reportsCode0
MultiPLY: A Multisensory Object-Centric Embodied Large Language Model in 3D World0
Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions0
Anchor function: a type of benchmark functions for studying language models0
Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructions0
Self-Imagine: Effective Unimodal Reasoning with Multimodal Models using Self-Imagination0
A character-based steganography using masked language modelingCode0
Graph database while computationally efficient filters out quickly the ESG integrated equities in investment management0
Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection0
On the importance of Data Scale in Pretraining Arabic Language Models0
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization0
Quantum Transfer Learning for Acceptability Judgements0
Activations and Gradients Compression for Model-Parallel TrainingCode0
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERTCode0
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative DecodingCode5
Editing Arbitrary Propositions in LLMs without Subject Labels0
A Novel Approach for Automatic Program Repair using Round-Trip Translation with Large Language ModelsCode0
When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment0
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered ChatbotCode1
Small Language Model Can Self-correct0
Small LLMs Are Weak Tool Learners: A Multi-LLM AgentCode3
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization0
ELLA-V: Stable Neural Codec Language Modeling with Alignment-guided Sequence Reordering0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation0
Distilling Event Sequence Knowledge From Large Language Models0
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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