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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation ModelsCode1
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image ClassificationCode1
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional EncodingCode1
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction TuningCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
AdaSplash: Adaptive Sparse Flash AttentionCode1
Fluent dreaming for language modelsCode1
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal DomainCode1
Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-ModelingCode1
A Model of Cross-Lingual Knowledge-Grounded Response Generation for Open-Domain Dialogue SystemsCode1
AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot ClassificationCode1
Protein Structure Tokenization: Benchmarking and New RecipeCode1
FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual ModelsCode1
Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-TuningCode1
PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language ModelCode1
Mask-Predict: Parallel Decoding of Conditional Masked Language ModelsCode1
FinVis-GPT: A Multimodal Large Language Model for Financial Chart AnalysisCode1
FIRE: Fact-checking with Iterative Retrieval and VerificationCode1
FocusLLM: Precise Understanding of Long Context by Dynamic CondensingCode1
PPL-MCTS: Constrained Textual Generation Through Discriminator-Guided MCTS DecodingCode1
Grounded Multi-Hop VideoQA in Long-Form Egocentric VideosCode1
Finetuning Large Language Model for Personalized RankingCode1
Fine-Tuning Language Models via Epistemic Neural NetworksCode1
Fine-tuning Large Language Models for Adaptive Machine TranslationCode1
Fine-Tuning InstructPix2Pix for Advanced Image ColorizationCode1
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks AdaptivelyCode1
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics SimulationsCode1
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot CornucopiaCode1
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support ConversationCode1
Fine-Tuning Discrete Diffusion Models with Policy Gradient MethodsCode1
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training ApproachCode1
Fine-grained Audible Video DescriptionCode1
Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven NavigationCode1
FinBERT: A Pretrained Language Model for Financial CommunicationsCode1
Finding Universal Grammatical Relations in Multilingual BERTCode1
Fill in the BLANC: Human-free quality estimation of document summariesCode1
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling ApproachCode1
FiLM: Fill-in Language Models for Any-Order GenerationCode1
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion PromptsCode1
AuditWen:An Open-Source Large Language Model for AuditCode1
FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis AssistantCode1
Filtering Noisy Parallel Corpus using Transformers with Proxy Task LearningCode1
FineRec:Exploring Fine-grained Sequential RecommendationCode1
Finetuning Pretrained Transformers into Variational AutoencodersCode1
Few-Shot Detection of Machine-Generated Text using Style RepresentationsCode1
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI AgentsCode1
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR PredictionCode1
Felix: Flexible Text Editing Through Tagging and InsertionCode1
Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation and Instance GenerationCode1
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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