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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 50015050 of 17610 papers

TitleStatusHype
Distinguishability Calibration to In-Context LearningCode0
How about Time? Probing a Multilingual Language Model for Temporal RelationsCode0
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language ModelCode0
How Decoding Strategies Affect the Verifiability of Generated TextCode0
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared TaskCode0
Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared TaskCode0
A Lightweight Constrained Generation Alternative for Query-focused SummarizationCode0
How does the task complexity of masked pretraining objectives affect downstream performance?Code0
Distributional Discrepancy: A Metric for Unconditional Text GenerationCode0
Distributionally Robust Language ModelingCode0
How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior SimulationCode0
Distributionally robust self-supervised learning for tabular dataCode0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
A Semi-Supervised Approach for Low-Resourced Text GenerationCode0
Adversarial Dropout for Recurrent Neural NetworksCode0
How Long Is Enough? Exploring the Optimal Intervals of Long-Range Clinical Note Language ModelingCode0
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?Code0
How Personality Traits Influence Negotiation Outcomes? A Simulation based on Large Language ModelsCode0
How Phonotactics Affect Multilingual and Zero-shot ASR PerformanceCode0
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
Aligned Music Notation and Lyrics TranscriptionCode0
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing CapabilitiesCode0
ChatVis: Automating Scientific Visualization with a Large Language ModelCode0
Diversity Measures: Domain-Independent Proxies for Failure in Language Model QueriesCode0
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text GenerationCode0
How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical SurveyCode0
How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?Code0
How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation MetricsCode0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
How to Leverage Personal Textual Knowledge for Personalized Conversational Information RetrievalCode0
How to Protect Copyright Data in Optimization of Large Language Models?Code0
Adversarially Regularising Neural NLI Models to Integrate Logical Background KnowledgeCode0
Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository ExplorationCode0
How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?Code0
How transformers learn structured data: insights from hierarchical filteringCode0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for DepressionCode0
Chemical Language Model Linker: blending text and molecules with modular adaptersCode0
How would Stance Detection Techniques Evolve after the Launch of ChatGPT?Code0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
DNAHLM -- DNA sequence and Human Language mixed large language ModelCode0
DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare DiseasesCode0
DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding UnitsCode0
DnDScore: Decontextualization and Decomposition for Factuality Verification in Long-Form Text GenerationCode0
Decomposed Prompting to Answer Questions on a Course Discussion BoardCode0
HUBERT Untangles BERT to Improve Transfer across NLP TasksCode0
HuBo-VLM: Unified Vision-Language Model designed for HUman roBOt interaction tasksCode0
hULMonA: The Universal Language Model in ArabicCode0
Human-Centered LLM-Agent User Interface: A Position PaperCode0
Humane Speech Synthesis through Zero-Shot Emotion and Disfluency GenerationCode0
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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