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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8201–8250 of 17610 papers

TitleStatusHype
Large Language Models as Generalizable Policies for Embodied Tasks—0
LightLM: A Lightweight Deep and Narrow Language Model for Generative RecommendationCode1
Test-time Augmentation for Factual ProbingCode0
Interactive Robot Learning from Verbal Correction—0
CompeteAI: Understanding the Competition Dynamics in Large Language Model-based AgentsCode1
De-novo Chemical Reaction Generation by Means of Temporal Convolutional Neural Networks—0
An Open Source Data Contamination Report for Large Language ModelsCode1
Content-based Controls For Music Large Language ModelingCode1
InstOptima: Evolutionary Multi-objective Instruction Optimization via Large Language Model-based Instruction OperatorsCode1
Using GPT-4 to Augment Unbalanced Data for Automatic Scoring—0
ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language AdaptersCode0
URL-BERT: Training Webpage Representations via Social Media Engagements—0
Unraveling Feature Extraction Mechanisms in Neural NetworksCode0
Zephyr: Direct Distillation of LM AlignmentCode5
Transformer-based Live Update Generation for Soccer Matches from Microblog Posts—0
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUsCode1
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models—0
Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training—0
math-PVS: A Large Language Model Framework to Map Scientific Publications to PVS Theories—0
The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model PretrainingCode0
Controlled Decoding from Language Models—0
Conditionally Combining Robot Skills using Large Language ModelsCode0
General Point Model with Autoencoding and Autoregressive—0
Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text GenerationCode0
BOOST: Harnessing Black-Box Control to Boost Commonsense in LMs' Generation—0
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data AugmentationCode0
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph—0
Discrete Diffusion Modeling by Estimating the Ratios of the Data DistributionCode2
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment—0
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning—0
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression RecognitionCode1
XFEVER: Exploring Fact Verification across LanguagesCode0
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction—0
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Optimal Inflationary Potentials—0
SkyMath: Technical ReportCode3
SuperHF: Supervised Iterative Learning from Human FeedbackCode1
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge RecoveryCode1
Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce—0
DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding—0
FLTrojan: Privacy Leakage Attacks against Federated Language Models Through Selective Weight Tampering—0
BLP-2023 Task 2: Sentiment Analysis—0
Locally Differentially Private Document Generation Using Zero Shot PromptingCode0
Self-Guard: Empower the LLM to Safeguard Itself—0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
Prevalence and prevention of large language model use in crowd work—0
Mixture of Tokens: Continuous MoE through Cross-Example AggregationCode2
MindLLM: Pre-training Lightweight Large Language Model from Scratch, Evaluations and Domain Applications—0
TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction—0
Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment—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