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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11551–11600 of 17610 papers

TitleStatusHype
Nondeterministic Stacks in Neural Networks—0
What's in a Name? Evaluating Assembly-Part Semantic Knowledge in Language Models through User-Provided Names in CAD FilesCode0
Generative Relevance Feedback with Large Language Models—0
GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters—0
Compressing Sentence Representation with maximum Coding Rate Reduction—0
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot SettingCode0
Blockchain Large Language Models—0
CitePrompt: Using Prompts to Identify Citation Intent in Scientific PapersCode0
Hypernymization of named entity-rich captions for grounding-based multi-modal pretraining—0
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling—0
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release—0
A Lightweight Constrained Generation Alternative for Query-focused SummarizationCode0
Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs—0
Semantic Specialization for Knowledge-based Word Sense DisambiguationCode0
Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey—0
Transformer-Based Language Model Surprisal Predicts Human Reading Times Best with About Two Billion Training Tokens—0
Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT—0
Spatial-Language Attention Policies for Efficient Robot Learning—0
SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model—0
KitchenScale: Learning to predict ingredient quantities from recipe contextsCode0
Evaluating Transformer Language Models on Arithmetic Operations Using Number DecompositionCode0
Indian Sign Language Recognition Using Mediapipe Holistic—0
Analyzing FOMC Minutes: Accuracy and Constraints of Language Models—0
CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering—0
Word Sense Induction with Knowledge Distillation from BERT—0
Supporting Human-AI Collaboration in Auditing LLMs with LLMs—0
LLM as A Robotic Brain: Unifying Egocentric Memory and Control—0
BRENT: Bidirectional Retrieval Enhanced Norwegian TransformerCode0
EC^2: Emergent Communication for Embodied Control—0
A Theory on Adam Instability in Large-Scale Machine Learning—0
Is ChatGPT Equipped with Emotional Dialogue Capabilities?—0
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification—0
Creating Large Language Model Resistant Exams: Guidelines and Strategies—0
CodeKGC: Code Language Model for Generative Knowledge Graph ConstructionCode0
A Survey for Biomedical Text Summarization: From Pre-trained to Large Language ModelsCode0
HeRo: RoBERTa and Longformer Hebrew Language Models—0
Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions—0
Large Language Models Based Automatic Synthesis of Software Specifications—0
Masked Language Model Based Textual Adversarial Example DetectionCode0
Typos-aware Bottlenecked Pre-Training for Robust Dense RetrievalCode0
The MiniPile Challenge for Data-Efficient Language ModelsCode0
Learning To Rank Resources with GNN—0
Political corpus creation through automatic speech recognition on EU debatesCode0
VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning—0
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model—0
An Evaluation on Large Language Model Outputs: Discourse and Memorization—0
Chain of Thought Prompt Tuning in Vision Language Models—0
PBNR: Prompt-based News Recommender System—0
Neural Machine Translation For Low Resource LanguagesCode0
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision—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