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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 51015150 of 17610 papers

TitleStatusHype
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources0
Large Generative Graph Models0
Are Large Language Models the New Interface for Data Pipelines?0
HORAE: A Domain-Agnostic Language for Automated Service RegulationCode0
Small-E: Small Language Model with Linear Attention for Efficient Speech SynthesisCode2
What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages0
LLplace: The 3D Indoor Scene Layout Generation and Editing via Large Language Model0
Simplified and Generalized Masked Diffusion for Discrete DataCode2
Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation0
Efficient 3D-Aware Facial Image Editing via Attribute-Specific Prompt LearningCode1
Improving Audio Codec-based Zero-Shot Text-to-Speech Synthesis with Multi-Modal Context and Large Language Model0
BLSP-Emo: Towards Empathetic Large Speech-Language ModelsCode2
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning0
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean DataCode2
Jailbreak Vision Language Models via Bi-Modal Adversarial PromptCode2
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness0
DeepStack: Deeply Stacking Visual Tokens is Surprisingly Simple and Effective for LMMs0
Scaling and evaluating sparse autoencodersCode4
Confabulation: The Surprising Value of Large Language Model Hallucinations0
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & AdaptationCode1
Legal Documents Drafting with Fine-Tuned Pre-Trained Large Language ModelCode0
AgentGym: Evolving Large Language Model-based Agents across Diverse EnvironmentsCode4
Tool-Planner: Task Planning with Clusters across Multiple ToolsCode2
Every Answer Matters: Evaluating Commonsense with Probabilistic MeasuresCode0
Queue management for slo-oriented large language model servingCode1
Ranking Manipulation for Conversational Search EnginesCode0
Item-Language Model for Conversational Recommendation0
Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notes0
Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task0
Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning0
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine FeedbackCode0
Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language ModelsCode2
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization0
Prompt-based Visual Alignment for Zero-shot Policy Transfer0
The Task-oriented Queries Benchmark (ToQB)0
PosterLLaVa: Constructing a Unified Multi-modal Layout Generator with LLMCode2
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMsCode1
Error-preserving Automatic Speech Recognition of Young English Learners' LanguageCode0
DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social ExperiencesCode2
PLaD: Preference-based Large Language Model Distillation with Pseudo-Preference Pairs0
Does your data spark joy? Performance gains from domain upsampling at the end of training0
Xmodel-LM Technical ReportCode1
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking MechanismsCode1
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language ModelsCode2
From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning TasksCode2
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step0
Order-Independence Without Fine TuningCode0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing0
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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