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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10001–10050 of 17610 papers

TitleStatusHype
On the steerability of large language models toward data-driven personas—0
On the Thinking-Language Modeling Gap in Large Language Models—0
On the Transformer Growth for Progressive BERT Training—0
On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models—0
On the Use of Entity Embeddings from Pre-Trained Language Models for Knowledge Graph Completion—0
On the use of linguistic similarities to improve Neural Machine Translation for African Languages—0
OntoType: Ontology-Guided and Pre-Trained Language Model Assisted Fine-Grained Entity Typing—0
A Study of Backdoors in Instruction Fine-tuned Language Models—0
On Unified Prompt Tuning for Request Quality Assurance in Public Code Review—0
On Unsupervised Training of Link Grammar Based Language Models—0
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue—0
Open ASR for Icelandic: Resources and a Baseline System—0
Open-Category Human-Object Interaction Pre-Training via Language Modeling Framework—0
Open-Domain Dialogue Generation Based on Pre-trained Language Models—0
Open-Domain Name Error Detection using a Multi-Task RNN—0
OpenECAD: An Efficient Visual Language Model for Editable 3D-CAD Design—0
OpenHOI: Open-World Hand-Object Interaction Synthesis with Multimodal Large Language Model—0
Open Information Extraction for SOV Language Based on Entity-Predicate Pair Detection—0
A Survey on Open Information Extraction from Rule-based Model to Large Language Model—0
Open Information Extraction from Conjunctive Sentences—0
Towards Uncovering How Large Language Model Works: An Explainability Perspective—0
OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews—0
Open Set Video HOI detection from Action-Centric Chain-of-Look Prompting—0
Open-Source Conversational AI with SpeechBrain 1.0—0
OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting—0
OpenThaiGPT 1.5: A Thai-Centric Open Source Large Language Model—0
Open-vocabulary object 6D pose estimation—0
Open-Vocabulary Temporal Action Localization using Multimodal Guidance—0
Open-World Evaluation for Retrieving Diverse Perspectives—0
Open-World Object Manipulation using Pre-trained Vision-Language Models—0
Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment—0
Ophtha-LLaMA2: A Large Language Model for Ophthalmology—0
OPI at SemEval 2023 Task 9: A Simple But Effective Approach to Multilingual Tweet Intimacy Analysis—0
Opportunities and Challenges of Generative-AI in Finance—0
OPSD: an Offensive Persian Social media Dataset and its baseline evaluations—0
Fine-Tuning Adaptive Stochastic Optimizers: Determining the Optimal Hyperparameter ε via Gradient Magnitude Histogram Analysis—0
Mapping of attention mechanisms to a generalized Potts model—0
Optimization of Retrieval-Augmented Generation Context with Outlier Detection—0
Rail-only: A Low-Cost High-Performance Network for Training LLMs with Trillion Parameters—0
Optimizing and Fine-tuning Large Language Model for Urban Renewal—0
Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs—0
Optimizing Language Models for Human Preferences is a Causal Inference Problem—0
Optimizing Language Models for Inference Time Objectives using Reinforcement Learning—0
Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training—0
Optimizing Large Language Models to Expedite the Development of Smart Contracts—0
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks—0
Optimizing Large Language Model Training Using FP4 Quantization—0
Optimizing Low-Resource Language Model Training: Comprehensive Analysis of Multi-Epoch, Multi-Lingual, and Two-Stage Approaches—0
Optimizing open-domain question answering with graph-based retrieval augmented generation—0
Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery—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