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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 21512200 of 17610 papers

TitleStatusHype
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved NegativesCode1
Lexical Simplification with Pretrained EncodersCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
Improving antibody language models with native pairingCode1
Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation ApproachCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
Improving Aspect Sentiment Quad Prediction via Template-Order Data AugmentationCode1
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta InformationCode1
Cross-lingual Visual Pre-training for Multimodal Machine TranslationCode1
Aioli: A Unified Optimization Framework for Language Model Data MixingCode1
Improved training of end-to-end attention models for speech recognitionCode1
Steering Language Models With Activation EngineeringCode1
A Simple and Effective L_2 Norm-Based Strategy for KV Cache CompressionCode1
ImProver: Agent-Based Automated Proof OptimizationCode1
Imposing Relation Structure in Language-Model Embeddings Using Contrastive LearningCode1
Implicit Unlikelihood Training: Improving Neural Text Generation with Reinforcement LearningCode1
Improved GUI Grounding via Iterative NarrowingCode1
Caution for the Environment: Multimodal Agents are Susceptible to Environmental DistractionsCode1
Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical NotesCode1
Improving Conversational Recommendation Systems via Counterfactual Data SimulationCode1
CDLM: Cross-Document Language ModelingCode1
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model BiasCode1
ImagineBench: Evaluating Reinforcement Learning with Large Language Model RolloutsCode1
Cross-Align: Modeling Deep Cross-lingual Interactions for Word AlignmentCode1
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility StudyCode1
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense RetrievalCode1
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language GuidanceCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
A Batch Normalized Inference Network Keeps the KL Vanishing AwayCode1
UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language ModelingCode1
ImaginaryNet: Learning Object Detectors without Real Images and AnnotationsCode1
Implementing contextual biasing in GPU decoder for online ASRCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language ModelCode1
Image Hijacks: Adversarial Images can Control Generative Models at RuntimeCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
A Cross-Modal Approach to Silent Speech with LLM-Enhanced RecognitionCode1
Image Super-Resolution with Text Prompt DiffusionCode1
CriticEval: Evaluating Large Language Model as CriticCode1
I Know What You Do Not Know: Knowledge Graph Embedding via Co-distillation LearningCode1
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Image-Text Co-Decomposition for Text-Supervised Semantic SegmentationCode1
Implicit Language Models are RNNs: Balancing Parallelization and ExpressivityCode1
Improving End-to-End SLU performance with Prosodic Attention and DistillationCode1
In-Context Alignment: Chat with Vanilla Language Models Before Fine-TuningCode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
A Second Wave of UD Hebrew Treebanking and Cross-Domain ParsingCode1
CPT: Efficient Deep Neural Network Training via Cyclic PrecisionCode1
CPM: A Large-scale Generative Chinese Pre-trained 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