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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5001–5050 of 17610 papers

TitleStatusHype
Breaking the Encoder Barrier for Seamless Video-Language Understanding—0
Autoregressive Language Models for Knowledge Base Population: A case study in the space mission domain—0
A Survey of Large Language Model Agents for Question Answering—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Distil-xLSTM: Learning Attention Mechanisms through Recurrent Structures—0
Human-Object Interaction with Vision-Language Model Guided Relative Movement Dynamics—0
ClinText-SP and RigoBERTa Clinical: a new set of open resources for Spanish Clinical NLP—0
ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses—0
Detection of Somali-written Fake News and Toxic Messages on the Social Media Using Transformer-based Language Models—0
Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agents—0
LakotaBERT: A Transformer-based Model for Low Resource Lakota Language—0
MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation—0
Payload-Aware Intrusion Detection with CMAE and Large Language Models—0
WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training—0
CountLLM: Towards Generalizable Repetitive Action Counting via Large Language Model—0
Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models—0
Field-Mediated Semantic Organization in Large Language Models: Evidence for Quantum-Like Properties in Artificial Neural Systems—0
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement—0
Efficient Knowledge Distillation via Curriculum Extraction—0
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Variance Control via Weight Rescaling in LLM Pre-trainingCode0
Large Language Model Compression via the Nested Activation-Aware Decomposition—0
Entropy-based Exploration Conduction for Multi-step Reasoning—0
ChatGPT and U(X): A Rapid Review on Measuring the User Experience—0
Improving Autoregressive Image Generation through Coarse-to-Fine Token Prediction—0
Code Evolution Graphs: Understanding Large Language Model Driven Design of Algorithms—0
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing CapabilitiesCode0
Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial AnalysisCode0
Using Language Models to Decipher the Motivation Behind Human Behaviors—0
Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture—0
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation—0
Shushing! Let's Imagine an Authentic Speech from the Silent Video—0
Robust Transmission of Punctured Text with Large Language Model-based Recovery—0
Probing the topology of the space of tokens with structured prompts—0
Leveraging MoE-based Large Language Model for Zero-Shot Multi-Task Semantic Communication—0
Sig2text, a Vision-language model for Non-cooperative Radar Signal ParsingCode0
GraspCorrect: Robotic Grasp Correction via Vision-Language Model-Guided Feedback—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Aligning Crowd-sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models—0
Good/Evil Reputation Judgment of Celebrities by LLMs via Retrieval Augmented Generation—0
Enabling Inclusive Systematic Reviews: Incorporating Preprint Articles with Large Language Model-Driven Evaluations—0
ChatBEV: A Visual Language Model that Understands BEV Maps—0
VARP: Reinforcement Learning from Vision-Language Model Feedback with Agent Regularized Preferences—0
The Empty Chair: Using LLMs to Raise Missing Perspectives in Policy Deliberations—0
SpaceVLLM: Endowing Multimodal Large Language Model with Spatio-Temporal Video Grounding Capability—0
MoK-RAG: Mixture of Knowledge Paths Enhanced Retrieval-Augmented Generation for Embodied AI Environments—0
Layer-wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning—0
Towards a Barrier-free GeoQA Portal: Natural Language Interaction with Geospatial Data Using Multi-Agent LLMs and Semantic Search—0
KVShare: An LLM Service System with Efficient and Effective Multi-Tenant KV Cache Reuse—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