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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 7151–7200 of 17610 papers

TitleStatusHype
Sequential Decision-Making for Inline Text Autocomplete—0
Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning—0
Sequential LLM Framework for Fashion Recommendation—0
Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?—0
Sequential-Parallel Duality in Prefix Scannable Models—0
Sequential Recurrent Neural Networks for Language Modeling—0
Serial Recall Effects in Neural Language Modeling—0
SeRpEnt: Selective Resampling for Expressive State Space Models—0
SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models—0
Service Composition Scenarios for Task-Oriented Translation—0
Session-level Language Modeling for Conversational Speech—0
SeTformer is What You Need for Vision and Language—0
Setting Standards in Turkish NLP: TR-MMLU for Large Language Model Evaluation—0
Set-to-Sequence Methods in Machine Learning: a Review—0
Manipulating and Mitigating Generative Model Biases without Retraining—0
SFS-TUE: Compound Paraphrasing with a Language Model and Discriminative Reranking—0
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe—0
SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment—0
SGEdit: Bridging LLM with Text2Image Generative Model for Scene Graph-based Image Editing—0
SGORNN: Combining Scalar Gates and Orthogonal Constraints in Recurrent Networks—0
SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis—0
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation—0
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer's Patients—0
Shai: A large language model for asset management—0
SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments—0
Shallow Discourse Parsing Using Convolutional Neural Network—0
ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing—0
ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model—0
Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions—0
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models—0
Shareable Representations for Search Query Understanding—0
Shared Global and Local Geometry of Language Model Embeddings—0
Sharpness-Aware Minimization Improves Language Model Generalization—0
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models—0
SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models—0
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.—0
Sheffield Systems for the English-Romanian WMT Translation Task—0
SHEF-LIUM-NN: Sentence level Quality Estimation with Neural Network Features—0
SHEF-NN: Translation Quality Estimation with Neural Networks—0
ShieldGemma 2: Robust and Tractable Image Content Moderation—0
Short Answer Grading Using One-shot Prompting and Text Similarity Scoring Model—0
Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences—0
Short-Range Dependency Effects on Transformer Instability and a Decomposed Attention Solution—0
Short-term memory in neural language models—0
Short-Text Classification Using Unsupervised Keyword Expansion—0
Short Wins Long: Short Codes with Language Model Semantic Correction Outperform Long Codes—0
Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL—0
Should Semantic Vector Composition be Explicit? Can it be Linear?—0
"Show Me What's Wrong!": Combining Charts and Text to Guide Data Analysis—0
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness—0
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All datasetsWikiText-103Penn Treebank (Word Level)enwik8The PileWikiText-2LAMBADAOne Billion WordText8Penn Treebank (Character Level)Hutter PrizeOpenWebTextSALMon

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67—Unverified
2GRUValidation perplexity53.78—Unverified
3LSTMValidation perplexity52.73—Unverified
4LSTMTest perplexity48.7—Unverified
5Temporal CNNTest perplexity45.2—Unverified
6TCNTest perplexity45.19—Unverified
7GCNN-8Test perplexity44.9—Unverified
8Neural cache model (size = 100)Test perplexity44.8—Unverified
9Neural cache model (size = 2,000)Test perplexity40.8—Unverified
10GPT-2 SmallTest perplexity37.5—Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47—Unverified
2Seq-U-NetTest perplexity107.95—Unverified
3GRU (Bai et al., 2018)Test perplexity92.48—Unverified
4R-TransformerTest perplexity84.38—Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7—Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7—Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93—Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4—Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2—Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67—Unverified
2HypernetworksBit per Character (BPC)1.34—Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33—Unverified
4LN HM-LSTMBit per Character (BPC)1.32—Unverified
5ByteNetBit per Character (BPC)1.31—Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27—Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25—Unverified
8Large mLSTMBit per Character (BPC)1.24—Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23—Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22—Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33—Unverified
2OPT 125MTest perplexity32.26—Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1—Unverified
4OPT 1.3BTest perplexity19.55—Unverified
5GPT-Neo 125MTest perplexity17.83—Unverified
6OPT 2.7BTest perplexity17.81—Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12—Unverified
8GPT-Neo 1.3BTest perplexity11.46—Unverified
9Transformer 125MTest perplexity10.7—Unverified
10GPT-Neo 2.7BTest perplexity10.44—Unverified