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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 37013750 of 17610 papers

TitleStatusHype
Full-text Error Correction for Chinese Speech Recognition with Large Language Model0
OmniQuery: Contextually Augmenting Captured Multimodal Memory to Enable Personal Question Answering0
Stable Language Model Pre-training by Reducing Embedding Variability0
WirelessAgent: Large Language Model Agents for Intelligent Wireless NetworksCode1
Mapping Biomedical Ontology Terms to IDs: Effect of Domain Prevalence on Prediction Accuracy0
STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM0
Salmon: A Suite for Acoustic Language Model EvaluationCode1
AdaCAD: Adaptively Decoding to Balance Conflicts between Contextual and Parametric KnowledgeCode1
Cross-Refine: Improving Natural Language Explanation Generation by Learning in TandemCode0
Agent Workflow MemoryCode3
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models0
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination0
FreeRide: Harvesting Bubbles in Pipeline Parallelism0
Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization0
Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language ModelCode1
MiniDrive: More Efficient Vision-Language Models with Multi-Level 2D Features as Text Tokens for Autonomous DrivingCode2
Synthetic continued pretrainingCode2
PiTe: Pixel-Temporal Alignment for Large Video-Language ModelCode1
Language agents achieve superhuman synthesis of scientific knowledgeCode9
Enhancing Large Language Models with Domain-Specific Knowledge: The Case in Topological Materials0
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model0
Multimodal Large Language Model Driven Scenario Testing for Autonomous Vehicles0
MAGDA: Multi-agent guideline-driven diagnostic assistance0
Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review0
MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context UnderstandingCode0
DetailCLIP: Detail-Oriented CLIP for Fine-Grained TasksCode2
Enhancing Temporal Understanding in Audio Question Answering for Large Audio Language Models0
HierLLM: Hierarchical Large Language Model for Question Recommendation0
User Preferences for Large Language Model versus Template-Based Explanations of Movie Recommendations: A Pilot Study0
DiPT: Enhancing LLM reasoning through diversified perspective-taking0
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding0
LegiLM: A Fine-Tuned Legal Language Model for Data ComplianceCode0
Leveraging Content and Acoustic Representations for Speech Emotion RecognitionCode0
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning0
SongCreator: Lyrics-based Universal Song Generation0
DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning0
FairHome: A Fair Housing and Fair Lending Dataset0
STLM Engineering Report: DropoutCode1
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning0
Regression with Large Language Models for Materials and Molecular Property Prediction0
TransformerRanker: A Tool for Efficiently Finding the Best-Suited Language Models for Downstream Classification TasksCode2
A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets0
Doppelgänger's Watch: A Split Objective Approach to Large Language Models0
WinoPron: Revisiting English Winogender Schemas for Consistency, Coverage, and Grammatical CaseCode0
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language ModelingCode0
Evidence from fMRI Supports a Two-Phase Abstraction Process in Language Models0
Retrofitting Temporal Graph Neural Networks with TransformerCode1
TextToucher: Fine-Grained Text-to-Touch GenerationCode1
On the Relationship between Truth and Political Bias in Language ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified