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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1005110100 of 17610 papers

TitleStatusHype
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional ReasoningCode0
ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations0
ChatGPT in the Classroom: An Analysis of Its Strengths and Weaknesses for Solving Undergraduate Computer Science Questions0
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl DataCode1
Framing the News:From Human Perception to Large Language Model Inferences0
A Modular Approach for Multilingual Timex Detection and Normalization using Deep Learning and Grammar-based methodsCode0
UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource languagesCode0
q2d: Turning Questions into Dialogs to Teach Models How to Search0
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale InstructionsCode2
SweCTRL-Mini: a data-transparent Transformer-based large language model for controllable text generation in SwedishCode0
PMC-LLaMA: Towards Building Open-source Language Models for MedicineCode2
Large Language Models are Strong Zero-Shot Retriever0
Learning Human-Human Interactions in Images from Weak Textual Supervision0
Controlled Text Generation with Natural Language Instructions0
Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement0
Vision Conformer: Incorporating Convolutions into Vision Transformer LayersCode0
ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPT0
The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification TasksCode1
Extracting Structured Seed-Mediated Gold Nanorod Growth Procedures from Literature with GPT-30
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and BeyondCode6
Enhancing Large Language Model with Self-Controlled Memory FrameworkCode1
MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions, Experiences and Claims from Social Media using Knowledge-Augmented Pre-trained Language Models0
What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD FilesCode0
Hypernymization of named entity-rich captions for grounding-based multi-modal pretraining0
Generative Relevance Feedback with Large Language Models0
Compressing Sentence Representation with maximum Coding Rate Reduction0
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot SettingCode0
CitePrompt: Using Prompts to Identify Citation Intent in Scientific PapersCode0
Blockchain Large Language Models0
GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters0
State Spaces Aren't Enough: Machine Translation Needs Attention0
Nondeterministic Stacks in Neural Networks0
KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis0
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling0
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLMCode1
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release0
A Lightweight Constrained Generation Alternative for Query-focused SummarizationCode0
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens0
SAILER: Structure-aware Pre-trained Language Model for Legal Case RetrievalCode1
LaMP: When Large Language Models Meet PersonalizationCode1
Semantic Specialization for Knowledge-based Word Sense DisambiguationCode0
Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey0
Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs0
Evaluating Transformer Language Models on Arithmetic Operations Using Number DecompositionCode0
KitchenScale: Learning to predict ingredient quantities from recipe contextsCode0
Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT0
Spatial-Language Attention Policies for Efficient Robot Learning0
SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model0
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language ModelsCode7
CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering0
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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