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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12851–12900 of 17610 papers

TitleStatusHype
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
Explainable Behavior Cloning: Teaching Large Language Model Agents through Learning by Demonstration—0
Explainable Clinical Decision Support from Text—0
Explainable CTR Prediction via LLM Reasoning—0
ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis—0
Explainable Moral Values: a neuro-symbolic approach to value classification—0
Explainable Multi-hop Verbal Reasoning Through Internal Monologue—0
Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning—0
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources—0
Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency—0
Explaining Agent Behavior with Large Language Models—0
Explaining Chest X-ray Pathology Models using Textual Concepts—0
Explaining Face Presentation Attack Detection Using Natural Language—0
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)—0
Parrot Mind: Towards Explaining the Complex Task Reasoning of Pretrained Large Language Models with Template-Content Structure—0
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning—0
Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization—0
Explanation for Trajectory Planning using Multi-modal Large Language Model for Autonomous Driving—0
Explanation is All You Need in Distillation: Mitigating Bias and Shortcut Learning—0
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas—0
Explicit and Implicit Syntactic Features for Text Classification—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