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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9001–9050 of 17610 papers

TitleStatusHype
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
Event participant modelling with neural networks—0
Event-Priori-Based Vision-Language Model for Efficient Visual Understanding—0
Event Segmentation Applications in Large Language Model Enabled Automated Recall Assessments—0
EventVL: Understand Event Streams via Multimodal Large Language Model—0
Evidence-Based Temporal Fact Verification—0
Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models—0
EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering—0
E-ViLM: Efficient Video-Language Model via Masked Video Modeling with Semantic Vector-Quantized Tokenizer—0
EVLM: An Efficient Vision-Language Model for Visual Understanding—0
EVLM: Self-Reflective Multimodal Reasoning for Cross-Dimensional Visual Editing—0
Evolutionary Contrastive Distillation for Language Model Alignment—0
Evolutionary Multi-Objective Optimization of Large Language Model Prompts for Balancing Sentiments—0
Evolutionary optimization of contexts for phonetic correction in speech recognition systems—0
Evolutionary Prompt Optimization Discovers Emergent Multimodal Reasoning Strategies in Vision-Language Models—0
Evolution through Large Models—0
Evolution without Large Models: Training Language Model with Task Principles—0
Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation—0
Evolving Code with A Large Language Model—0
Evolving Deeper LLM Thinking—0
Evolving Interpretable Visual Classifiers with Large Language Models—0
Personalized Large Language Model Assistant with Evolving Conditional Memory—0
EvoMerge: Neuroevolution for Large Language Models—0
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
Exact Decoding for Phrase-Based Statistical Machine Translation—0
Exact Decoding with Multi Bottom-Up Tree Transducers—0
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy—0
Exact Sampling and Decoding in High-Order Hidden Markov Models—0
Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation—0
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples—0
Examining Scaling and Transfer of Language Model Architectures for Machine Translation—0
Examining the Influence of Political Bias on Large Language Model Performance in Stance Classification—0
EXAONE 3.0 7.8B Instruction Tuned Language Model—0
Existential Conversations with Large Language Models: Content, Community, and Culture—0
Exosense: A Vision-Based Scene Understanding System For Exoskeletons—0
Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment—0
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?—0
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling—0
Expanding the Language model in a low-resource hybrid MT system—0
Expediting and Elevating Large Language Model Reasoning via Hidden Chain-of-Thought Decoding—0
Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture—0
When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma—0
Experimenting with Power Divergences for Language Modeling—0
Experiments in Medical Translation Shared Task at WMT 2014—0
Experiments of ASR-based mispronunciation detection for children and adult English learners—0
ExpertAF: Expert Actionable Feedback from Video—0
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Performance Characterization of Expert Router for Scalable LLM Inference—0
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs—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