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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 26012650 of 17610 papers

TitleStatusHype
Counterfactual Token Generation in Large Language ModelsCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
RetGen: A Joint framework for Retrieval and Grounded Text Generation ModelingCode1
Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model ReasoningCode1
JailDAM: Jailbreak Detection with Adaptive Memory for Vision-Language ModelCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoECode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
A Qualitative Evaluation of Language Models on Automatic Question-Answering for COVID-19Code1
Discovering Autoregressive Orderings with Variational InferenceCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed MetadataCode1
IvyGPT: InteractiVe Chinese pathwaY language model in medical domainCode1
cosFormer: Rethinking Softmax in AttentionCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
Iterative Few-shot Semantic Segmentation from Image Label TextCode1
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language ModelsCode1
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level ExtractionCode1
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learningCode1
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out StrategiesCode1
Latin BERT: A Contextual Language Model for Classical PhilologyCode1
ITER: Iterative Transformer-based Entity Recognition and Relation ExtractionCode1
Bootstrapping Interactive Image-Text Alignment for Remote Sensing Image CaptioningCode1
Dissecting Human and LLM PreferencesCode1
DMoERM: Recipes of Mixture-of-Experts for Effective Reward ModelingCode1
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Distillation and Refinement of Reasoning in Small Language Models for Document Re-rankingCode1
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language CorrectionsCode1
ArabicMMLU: Assessing Massive Multitask Language Understanding in ArabicCode1
Distilling a Pretrained Language Model to a Multilingual ASR ModelCode1
IterVM: Iterative Vision Modeling Module for Scene Text RecognitionCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Distilling Linguistic Context for Language Model CompressionCode1
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterpartsCode1
Arabisc: Context-Sensitive Neural Spelling CheckerCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Condenser: a Pre-training Architecture for Dense RetrievalCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
Learning Approximate Inference Networks for Structured PredictionCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical ReasoningCode1
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and ObjectsCode1
AraELECTRA: Pre-Training Text Discriminators for Arabic Language UnderstandingCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
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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