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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 88018850 of 17610 papers

TitleStatusHype
Compositional Foundation Models for Hierarchical Planning0
FedJudge: Federated Legal Large Language ModelCode1
Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative DecodingCode2
Indian-BhED: A Dataset for Measuring India-Centric Biases in Large Language ModelsCode0
Towards Practical and Efficient Image-to-Speech Captioning with Vision-Language Pre-training and Multi-modal Tokens0
Self-Consistent Narrative Prompts on Abductive Natural Language InferenceCode0
Sparse Autoencoders Find Highly Interpretable Features in Language ModelsCode3
Neural Machine Translation Models Can Learn to be Few-shot Learners0
Structural Self-Supervised Objectives for TransformersCode0
VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model0
Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level0
ChatGPT v Bard v Bing v Claude 2 v Aria v human-expert. How good are AI chatbots at scientific writing?0
Anchor Points: Benchmarking Models with Much Fewer ExamplesCode0
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the WildCode1
Masked Diffusion with Task-awareness for Procedure Planning in Instructional VideosCode0
PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts0
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and DialectsCode1
Leveraging Contextual Information for Effective Entity Salience Detection0
Voxtlm: unified decoder-only models for consolidating speech recognition/synthesis and speech/text continuation tasks0
A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender SystemsCode0
Computer says 'no': Exploring systemic bias in ChatGPT using an audit approach0
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?0
Hybrid Attention-based Encoder-decoder Model for Efficient Language Model Adaptation0
Incorporating Class-based Language Model for Named Entity Recognition in Factorized Neural Transducer0
Advancing Regular Language Reasoning in Linear Recurrent Neural NetworksCode0
Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text0
ExpertQA: Expert-Curated Questions and Attributed AnswersCode1
Generative AI Text Classification using Ensemble LLM Approaches0
Text Classification of Cancer Clinical Trial Eligibility Criteria0
The Rise and Potential of Large Language Model Based Agents: A SurveyCode5
Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer0
MMICL: Empowering Vision-language Model with Multi-Modal In-Context LearningCode2
Unified Human-Scene Interaction via Prompted Chain-of-ContactsCode2
TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation ModelsCode1
Unsupervised Contrast-Consistent Ranking with Language ModelsCode0
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast AdaptationCode1
Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion EnhancementCode0
Statistical Rejection Sampling Improves Preference Optimization0
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in RoboticsCode0
Recovering from Privacy-Preserving Masking with Large Language Models0
Improving Robustness of Neural Inverse Text Normalization via Data-Augmentation, Semi-Supervised Learning, and Post-Aligning Method0
AstroLLaMA: Towards Specialized Foundation Models in Astronomy0
Do Generative Large Language Models need billions of parameters?0
Efficient Memory Management for Large Language Model Serving with PagedAttentionCode6
Characterizing Latent Perspectives of Media Houses Towards Public Figures0
RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair0
Leveraging Large Language Models for Automated Dialogue AnalysisCode0
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language ModelCode0
Unsupervised Bias Detection in College Student Newspapers0
Uncovering mesa-optimization algorithms in Transformers0
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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