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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 14511500 of 17610 papers

TitleStatusHype
Gemstones: A Model Suite for Multi-Faceted Scaling LawsCode1
Position-aware Automatic Circuit DiscoveryCode1
ADIFF: Explaining audio difference using natural languageCode1
Great Models Think Alike and this Undermines AI OversightCode1
Robotouille: An Asynchronous Planning Benchmark for LLM AgentsCode1
Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for Efficient On-Device AgentsCode1
Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning DynamicsCode1
Intent Representation Learning with Large Language Model for RecommendationCode1
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific ApplicationsCode1
Do Large Language Model Benchmarks Test Reliability?Code1
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level RoutingCode1
Fine-Tuning Discrete Diffusion Models with Policy Gradient MethodsCode1
Polynomial, trigonometric, and tropical activationsCode1
Simulating Rumor Spreading in Social Networks using LLM AgentsCode1
Speculative Ensemble: Fast Large Language Model Ensemble via SpeculationCode1
Scalable-Softmax Is Superior for AttentionCode1
Low-Rank Adapting Models for Sparse AutoencodersCode1
WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-TrainingCode1
2SSP: A Two-Stage Framework for Structured Pruning of LLMsCode1
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token ReprogrammingsCode1
Atla Selene Mini: A General Purpose Evaluation ModelCode1
ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from TransformerCode1
Ocean-OCR: Towards General OCR Application via a Vision-Language ModelCode1
DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace EditingCode1
RealCritic: Towards Effectiveness-Driven Evaluation of Language Model CritiquesCode1
Enhancing Biomedical Relation Extraction with DirectionalityCode1
PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language ModelCode1
Glinthawk: A Two-Tiered Architecture for Offline LLM InferenceCode1
EndoChat: Grounded Multimodal Large Language Model for Endoscopic SurgeryCode1
AdaptiveLog: An Adaptive Log Analysis Framework with the Collaboration of Large and Small Language ModelCode1
LAVCap: LLM-based Audio-Visual Captioning using Optimal TransportCode1
WhiSPA: Semantically and Psychologically Aligned Whisper with Self-Supervised Contrastive and Student-Teacher LearningCode1
3UR-LLM: An End-to-End Multimodal Large Language Model for 3D Scene UnderstandingCode1
Gandalf the Red: Adaptive Security for LLMsCode1
VASparse: Towards Efficient Visual Hallucination Mitigation for Large Vision-Language Model via Visual-Aware SparsificationCode1
Merging Feed-Forward Sublayers for Compressed TransformersCode1
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
Segmenting Text and Learning Their Rewards for Improved RLHF in Language ModelCode1
Establishing baselines for generative discovery of inorganic crystalsCode1
Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration DecodingCode1
Rethinking Addressing in Language Models via Contexualized Equivariant Positional EncodingCode1
LLM-Rubric: A Multidimensional, Calibrated Approach to Automated Evaluation of Natural Language TextsCode1
TinyHelen's First Curriculum: Training and Evaluating Tiny Language Models in a Simpler Language EnvironmentCode1
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive DefenseCode1
Facilitating large language model Russian adaptation with Learned Embedding PropagationCode1
No Preference Left Behind: Group Distributional Preference OptimizationCode1
An Engorgio Prompt Makes Large Language Model Babble onCode1
Learning to engineer protein flexibilityCode1
Brain-to-Text Benchmark '24: Lessons LearnedCode1
Resource-Aware Arabic LLM Creation: Model Adaptation, Integration, and Multi-Domain TestingCode1
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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