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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 12651–12700 of 17610 papers

TitleStatusHype
Enhancing Q-Learning with Large Language Model Heuristics—0
Enhancing Reasoning Capacity of SLM using Cognitive Enhancement—0
Enhancing Recommender Systems with Large Language Model Reasoning Graphs—0
Enhancing Retrieval Processes for Language Generation with Augmented Queries—0
Enhancing Robustness of Pre-trained Language Model with Lexical Simplification—0
Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker—0
Enhancing Self-Disclosure In Neural Dialog Models By Candidate Re-ranking—0
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization—0
Enhancing Sentiment Analysis in Bengali Texts: A Hybrid Approach Using Lexicon-Based Algorithm and Pretrained Language Model Bangla-BERT—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies—0
Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs—0
Enhancing Speech Recognition Decoding via Layer Aggregation—0
Enhancing Spoken Discourse Modeling in Language Models Using Gestural Cues—0
Enhancing SQL Query Generation with Neurosymbolic Reasoning—0
Enhancing Structured-Data Retrieval with GraphRAG: Soccer Data Case Study—0
Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy—0
Enhancing Subtask Performance of Multi-modal Large Language Model—0
Enhancing Surgical Robots with Embodied Intelligence for Autonomous Ultrasound Scanning—0
Enhancing TCR-Peptide Interaction Prediction with Pretrained Language Models and Molecular Representations—0
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models—0
Enhancing the LLM-Based Robot Manipulation Through Human-Robot Collaboration—0
Enhancing the Spatial Awareness Capability of Multi-Modal Large Language Model—0
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks—0
Enhancing the Traditional Chinese Medicine Capabilities of Large Language Model through Reinforcement Learning from AI Feedback—0
Enhancing Token Filtering Efficiency in Large Language Model Training with Collider—0
Enhancing Transformer with Sememe Knowledge—0
Enhancing Translation Language Models with Word Embedding for Information Retrieval—0
Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach—0
Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning—0
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation—0
Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation—0
PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment—0
Watermarking Language Models for Many Adaptive Users—0
Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering—0
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving—0
ENRICH4ALL: A First Luxembourgish BERT Model for a Multilingual Chatbot—0
Enriching a Model's Notion of Belief using a Persistent Memory—0
Enriching Cold Start Personalized Language Model Using Social Network Information—0
Enriching Epidemiological Thematic Features For Disease Surveillance Corpora Classification—0
Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—0
Enriching Vulnerability Reports Through Automated and Augmented Description Summarization—0
Enriching Word Sense Embeddings with Translational Context—0
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation—0
Ensembles and Cocktails: Robust Finetuning for Natural Language Generation—0
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning—0
Ensembling Sparse Autoencoders—0
EnsemW2S: Enhancing Weak-to-Strong Generalization with Large Language Model Ensembles—0
Ensuring Fair LLM Serving Amid Diverse Applications—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