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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6251–6300 of 17610 papers

TitleStatusHype
Strategic Responses to Technological Change: Evidence from Online Labor Markets—0
Evaluating GPT-4 with Vision on Detection of Radiological Findings on Chest Radiographs—0
Measuring Gender and Racial Biases in Large Language Models—0
Unifying Large Language Model and Deep Reinforcement Learning for Human-in-Loop Interactive Socially-aware Navigation—0
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal ModelsCode2
Controlled Training Data Generation with Diffusion Models—0
Text Clustering with Large Language Model Embeddings—0
Long-CLIP: Unlocking the Long-Text Capability of CLIPCode4
CoLLEGe: Concept Embedding Generation for Large Language Models—0
Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case StudyCode0
Make VLM Recognize Visual Hallucination on Cartoon Character Image with Pose Information—0
Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit ReasoningCode0
Bioinformatics and Biomedical Informatics with ChatGPT: Year One Review—0
An Exploratory Investigation into Code License Infringements in Large Language Model Training DatasetsCode0
Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives—0
Sequential Decision-Making for Inline Text Autocomplete—0
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design—0
VidLA: Video-Language Alignment at Scale—0
WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language ModelsCode1
LayoutLLM: Large Language Model Instruction Tuning for Visually Rich Document Understanding—0
A Multimodal Approach to Device-Directed Speech Detection with Large Language Models—0
MyVLM: Personalizing VLMs for User-Specific Queries—0
Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering—0
Recourse for reclamation: Chatting with generative language models—0
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient InferenceCode3
PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning—0
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge DistillationCode1
Exosense: A Vision-Based Scene Understanding System For Exoskeletons—0
Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts—0
LLM-based Extraction of Contradictions from Patents—0
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation—0
Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural NetworkCode0
Lexicon-Level Contrastive Visual-Grounding Improves Language ModelingCode1
gTBLS: Generating Tables from Text by Conditional Question Answering—0
Locating and Mitigating Gender Bias in Large Language Models—0
Vi-Mistral-X: Building a Vietnamese Language Model with Advanced Continual Pre-training—0
Community Needs and Assets: A Computational Analysis of Community ConversationsCode0
Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification—0
RewardBench: Evaluating Reward Models for Language ModelingCode4
Out-of-Distribution Detection Using Peer-Class Generated by Large Language Model—0
Learning from Synthetic Data for Visual Grounding—0
Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases—0
Enhancing Gait Video Analysis in Neurodegenerative Diseases by Knowledge Augmentation in Vision Language Model—0
VL-Mamba: Exploring State Space Models for Multimodal Learning—0
Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
A New Massive Multilingual Dataset for High-Performance Language Technologies—0
Different Tokenization Schemes Lead to Comparable Performance in Spanish Number Agreement—0
SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual SummarizationCode0
VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model—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