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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17511800 of 17610 papers

TitleStatusHype
Towards Aligning Language Models with Textual FeedbackCode1
INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language ModelCode1
TLCR: Token-Level Continuous Reward for Fine-grained Reinforcement Learning from Human FeedbackCode1
DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan DesignCode1
LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language ModelsCode1
dMel: Speech Tokenization made SimpleCode1
Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learningCode1
ViLLa: Video Reasoning Segmentation with Large Language ModelCode1
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBenchCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
Analyzing the Generalization and Reliability of Steering VectorsCode1
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic FinetuningCode1
Exploring Quantization for Efficient Pre-Training of Transformer Language ModelsCode1
LiteGPT: Large Vision-Language Model for Joint Chest X-ray Localization and Classification TaskCode1
InvAgent: A Large Language Model based Multi-Agent System for Inventory Management in Supply ChainsCode1
On Large Language Model Continual UnlearningCode1
ChatLogic: Integrating Logic Programming with Large Language Models for Multi-Step ReasoningCode1
IoT-LM: Large Multisensory Language Models for the Internet of ThingsCode1
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language GuidanceCode1
Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language ModelsCode1
Aligning Diffusion Behaviors with Q-functions for Efficient Continuous ControlCode1
Benchmarking Language Model Creativity: A Case Study on Code GenerationCode1
DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documentsCode1
Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary DetectionCode1
STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLMCode1
Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video UnderstandingCode1
AutoBencher: Creating Salient, Novel, Difficult Datasets for Language ModelsCode1
Incorporating Large Language Models into Production Systems for Enhanced Task Automation and FlexibilityCode1
Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic SegmentationCode1
Training on the Test Task Confounds Evaluation and EmergenceCode1
IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language ModelCode1
CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object DetectionCode1
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
DebUnc: Improving Large Language Model Agent Communication With Uncertainty MetricsCode1
Open-world Multi-label Text Classification with Extremely Weak SupervisionCode1
Large language models are good medical coders, if provided with toolsCode1
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based SpikingCode1
When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn SettingsCode1
Crafting Large Language Models for Enhanced InterpretabilityCode1
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMsCode1
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection SystemCode1
Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time CorrectionCode1
Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language ModelsCode1
GPTCast: a weather language model for precipitation nowcastingCode1
Language Model Alignment in Multilingual Trolley ProblemsCode1
SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language ModelCode1
Revisiting Random Walks for Learning on GraphsCode1
Meerkat: Audio-Visual Large Language Model for Grounding in Space and TimeCode1
MM-Instruct: Generated Visual Instructions for Large Multimodal Model AlignmentCode1
Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language ModelCode1
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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