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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8801–8850 of 17610 papers

TitleStatusHype
Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models—0
Enhancing Language Generation with Effective Checkpoints of Pre-trained Language Model—0
Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning—0
Semantic Self-Consistency: Enhancing Language Model Reasoning via Semantic Weighting—0
Enhancing Large Language Model-based Speech Recognition by Contextualization for Rare and Ambiguous Words—0
Enhancing Large Language Model Efficiencyvia Symbolic Compression: A Formal Approach Towards Interpretability—0
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals—0
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately—0
Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction—0
Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System—0
Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM)—0
Enhancing Medical Specialty Assignment to Patients using NLP Techniques—0
Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies—0
Enhancing Medication Recommendation with LLM Text Representation—0
Enhancing Multi-Criteria Decision Analysis with AI: Integrating Analytic Hierarchy Process and GPT-4 for Automated Decision Support—0
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing—0
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion—0
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network—0
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
Enhancing Pre-trained Language Model with Lexical Simplification—0
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
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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