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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1030110350 of 17610 papers

TitleStatusHype
Robustifying Language Models with Test-Time Adaptation0
MILL: Mutual Verification with Large Language Models for Zero-Shot Query ExpansionCode0
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain0
Translating away Translationese without Parallel Data0
ProMap: Effective Bilingual Lexicon Induction via Language Model PromptingCode0
Robust NL-to-Cypher Translation for KBQA: Harnessing Large Language Model with Chain of Prompts0
Identifying Conspiracy Theories News based on Event Relation GraphCode0
Automating the Correctness Assessment of AI-generated Code for Security ContextsCode0
Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies0
SOUL: Towards Sentiment and Opinion Understanding of LanguageCode0
Siamese-DETR for Generic Multi-Object TrackingCode0
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue SystemCode0
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 20230
Elevating Code-mixed Text Handling through Auditory Information of WordsCode0
De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks0
Interactive Robot Learning from Verbal Correction0
Test-time Augmentation for Factual ProbingCode0
Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike WaysCode0
The impact of responding to patient messages with large language model assistanceCode0
Large Language Models as Generalizable Policies for Embodied Tasks0
math-PVS: A Large Language Model Framework to Map Scientific Publications to PVS Theories0
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models0
The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model PretrainingCode0
Optimal Inflationary Potentials0
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training0
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction0
Transformer-based Live Update Generation for Soccer Matches from Microblog Posts0
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model0
ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language AdaptersCode0
Unraveling Feature Extraction Mechanisms in Neural NetworksCode0
URL-BERT: Training Webpage Representations via Social Media Engagements0
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring0
XFEVER: Exploring Fact Verification across LanguagesCode0
Conditionally Combining Robot Skills using Large Language ModelsCode0
BOOST: Harnessing Black-Box Control to Boost Commonsense in LMs' Generation0
Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text GenerationCode0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment0
Controlled Decoding from Language Models0
General Point Model with Autoencoding and Autoregressive0
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data AugmentationCode0
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning0
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph0
CRaSh: Clustering, Removing, and Sharing Enhance Fine-tuning without Full Large Language ModelCode0
E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity0
DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding0
A Language Model with Limited Memory Capacity Captures Interference in Human Sentence Processing0
Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring0
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering0
BLP-2023 Task 2: Sentiment Analysis0
A statistical significance testing approach for measuring term burstiness with applications to domain-specific terminology extractionCode0
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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