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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13511400 of 17610 papers

TitleStatusHype
GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text GenerationCode1
GateLoop: Fully Data-Controlled Linear Recurrence for Sequence ModelingCode1
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue GenerationCode1
Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0Code1
FVEval: Understanding Language Model Capabilities in Formal Verification of Digital HardwareCode1
gaBERT -- an Irish Language ModelCode1
Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language ModelCode1
Analyzing the Source and Target Contributions to Predictions in Neural Machine TranslationCode1
Counterfactual Token Generation in Large Language ModelsCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from TextCode1
Accurate identification of bacteriophages from metagenomic data using TransformerCode1
Counterfactual Data Augmentation for Neural Machine TranslationCode1
FuzzCoder: Byte-level Fuzzing Test via Large Language ModelCode1
Analyzing the Generalization and Reliability of Steering VectorsCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
CoS: Enhancing Personalization and Mitigating Bias with Context SteeringCode1
FuseCap: Leveraging Large Language Models for Enriched Fused Image CaptionsCode1
cosFormer: Rethinking Softmax in AttentionCode1
Fusing Context Into Knowledge Graph for Commonsense Question AnsweringCode1
Fusing Pre-Trained Language Models With Multimodal Prompts Through Reinforcement LearningCode1
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language ModelCode1
ADIFF: Explaining audio difference using natural languageCode1
Frustratingly Simple Pretraining Alternatives to Masked Language ModelingCode1
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language ModelsCode1
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model InfillingCode1
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced DataCode1
GAMA: Generative Adversarial Multi-Object Scene AttacksCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Copy Is All You NeedCode1
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language ModelsCode1
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction TuningCode1
From Text to Pixel: Advancing Long-Context Understanding in MLLMsCode1
ADEPT: A DEbiasing PrompT FrameworkCode1
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More EffectiveCode1
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient TuningCode1
From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial InjectionCode1
From Two to One: A New Scene Text Recognizer with Visual Language Modeling NetworkCode1
t-SMILES: A Scalable Fragment-based Molecular Representation Framework for De Novo Molecule GenerationCode1
Free and Customizable Code Documentation with LLMs: A Fine-Tuning ApproachCode1
Control Prefixes for Parameter-Efficient Text GenerationCode1
f-PO: Generalizing Preference Optimization with f-divergence MinimizationCode1
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language ModelsCode1
Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree SearchCode1
Foundation TransformersCode1
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales DialogueCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
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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