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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 11001–11050 of 17610 papers

TitleStatusHype
PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM—0
Multimodal LLMs for health grounded in individual-specific data—0
Promoting Exploration in Memory-Augmented Adam using Critical MomentaCode0
SLMGAN: Exploiting Speech Language Model Representations for Unsupervised Zero-Shot Voice Conversion in GANs—0
Linearized Relative Positional EncodingCode0
Integration of Large Language Models and Federated Learning—0
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning—0
Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain—0
Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model—0
Gender mobility in the labor market with skills-based matching models—0
Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research—0
Using an LLM to Help With Code Understanding—0
Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages—0
Fast Quantum Algorithm for Attention Computation—0
The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant—0
Transformers are Universal Predictors—0
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Improving BERT with Hybrid Pooling Network and Drop Mask—0
MorphPiece : A Linguistic Tokenizer for Large Language Models—0
Population Expansion for Training Language Models with Private Federated Learning—0
Mega-TTS 2: Boosting Prompting Mechanisms for Zero-Shot Speech Synthesis—0
Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section—0
Electoral Agitation Data Set: The Use Case of the Polish ElectionCode0
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?—0
Instruction Mining: Instruction Data Selection for Tuning Large Language Models—0
Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems—0
PolyLM: An Open Source Polyglot Large Language Model—0
Transformers in Reinforcement Learning: A Survey—0
Lightweight reranking for language model generations—0
SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization—0
Model Card and Evaluations for Claude Models—0
SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation—0
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization—0
Text Descriptions are Compressive and Invariant Representations for Visual Learning—0
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training—0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?—0
Assessing the efficacy of large language models in generating accurate teacher responses—0
Natural Language Instructions for Intuitive Human Interaction with Robotic Assistants in Field Construction Work—0
On decoder-only architecture for speech-to-text and large language model integration—0
Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes—0
Bidirectional Attention as a Mixture of Continuous Word ExpertsCode0
A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification—0
Procedurally generating rules to adapt difficulty for narrative puzzle games—0
Masked Latent Semantic Modeling: an Efficient Pre-training Alternative to Masked Language ModelingCode0
S2vNTM: Semi-supervised vMF Neural Topic Modeling—0
RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language ModelsCode0
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence—0
Agentività e telicità in GilBERTo: implicazioni cognitive—0
Can ChatGPT's Responses Boost Traditional Natural Language Processing?Code0
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language—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