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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1690116950 of 17610 papers

TitleStatusHype
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal TokensCode0
Building Language Models for Text with Named EntitiesCode0
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social MediaCode0
Enhancing Tool Retrieval with Iterative Feedback from Large Language ModelsCode0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
Building a Taiwanese Mandarin Spoken Language Model: A First AttemptCode0
Joint Source-Target Self Attention with Locality ConstraintsCode0
Incremental Neural Lexical Coherence ModelingCode0
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?Code0
Aligning Sentence Simplification with ESL Learner's Proficiency for Language AcquisitionCode0
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot SettingCode0
Enhancing Vision-Language Model Pre-training with Image-text Pair Pruning Based on Word FrequencyCode0
HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic AnalysisCode0
Cross-lingual Similarity of Multilingual Representations RevisitedCode0
Building a Swedish Open-Domain Conversational Language ModelCode0
Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLUCode0
EnIGMA: Enhanced Interactive Generative Model Agent for CTF ChallengesCode0
Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR CorrectionCode0
BTRec: BERT-Based Trajectory Recommendation for Personalized ToursCode0
Bridging the Gap Between Open-Source and Proprietary LLMs in Table QACode0
Enriching BERT with Knowledge Graph Embeddings for Document ClassificationCode0
A Commonsense-Infused Language-Agnostic Learning Framework for Enhancing Prediction of Political Polarity in Multilingual News HeadlinesCode0
Language Models with TransformersCode0
Enriching language models with graph-based context information to better understand textual dataCode0
Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language ModelsCode0
Cross-lingual Language Model PretrainingCode0
Enriching Pre-trained Language Model with Entity Information for Relation ClassificationCode0
Applying language models to algebraic topology: generating simplicial cycles using multi-labeling in Wu's formulaCode0
Bridging Information-Theoretic and Geometric Compression in Language ModelsCode0
GapPredict: A Language Model for Resolving Gaps in Draft Genome AssembliesCode0
Garbage in, garbage out: Zero-shot detection of crime using Large Language ModelsCode0
Garden-Path Traversal in GPT-2Code0
Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant ContentCode0
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-trainingCode0
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
EnsLM: Ensemble Language Model for Data Diversity by Semantic ClusteringCode0
BRENT: Bidirectional Retrieval Enhanced Norwegian TransformerCode0
Interweaving Memories of a Siamese Large Language ModelCode0
Entailment Semantics Can Be Extracted from an Ideal Language ModelCode0
Enterprise Benchmarks for Large Language Model EvaluationCode0
Gated Word-Character Recurrent Language ModelCode0
Entities as Experts: Sparse Memory Access with Entity SupervisionCode0
Breaking Time Invariance: Assorted-Time Normalization for RNNsCode0
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative ClassificationCode0
Entity at SemEval-2021 Task 5: Weakly Supervised Token Labelling for Toxic Spans DetectionCode0
Gates Are Not What You Need in RNNsCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
Cross-lingual Information Retrieval with BERTCode0
Gating Revisited: Deep Multi-layer RNNs That Can Be TrainedCode0
HLAT: High-quality Large Language Model Pre-trained on AWS TrainiumCode0
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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