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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 18011850 of 17610 papers

TitleStatusHype
Decoding-Time Language Model Alignment with Multiple ObjectivesCode1
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and InterpretabilityCode1
A Refer-and-Ground Multimodal Large Language Model for BiomedicineCode1
Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image TranslationCode1
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysisCode1
CogMG: Collaborative Augmentation Between Large Language Model and Knowledge GraphCode1
Retrieval-style In-Context Learning for Few-shot Hierarchical Text ClassificationCode1
Multi-property Steering of Large Language Models with Dynamic Activation CompositionCode1
The FineWeb Datasets: Decanting the Web for the Finest Text Data at ScaleCode1
Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse GradientsCode1
CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue CoreferenceCode1
The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data AnnotatorsCode1
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-ResolutionCode1
C-LLM: Learn to Check Chinese Spelling Errors Character by CharacterCode1
RES-Q: Evaluating Code-Editing Large Language Model Systems at the Repository ScaleCode1
TacoLM: GaTed Attention Equipped Codec Language Model are Efficient Zero-Shot Text to Speech SynthesizersCode1
TinyStyler: Efficient Few-Shot Text Style Transfer with Authorship EmbeddingsCode1
InternLM-Law: An Open Source Chinese Legal Large Language ModelCode1
Safely Learning with Private Data: A Federated Learning Framework for Large Language ModelCode1
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal BehaviorsCode1
LiveMind: Low-latency Large Language Models with Simultaneous InferenceCode1
VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language ModelCode1
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMsCode1
CityBench: Evaluating the Capabilities of Large Language Models for Urban TasksCode1
On AI-Inspired UI-DesignCode1
BiLD: Bi-directional Logits Difference Loss for Large Language Model DistillationCode1
Improving Visual Commonsense in Language Models via Multiple Image GenerationCode1
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component AnalysisCode1
RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image UnderstandingCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQLCode1
A Simple and Effective L_2 Norm-Based Strategy for KV Cache CompressionCode1
Fairer Preferences Elicit Improved Human-Aligned Large Language Model JudgmentsCode1
UniGLM: Training One Unified Language Model for Text-Attributed Graph EmbeddingCode1
Dialogue Action Tokens: Steering Language Models in Goal-Directed Dialogue with a Multi-Turn PlannerCode1
Language Modeling with Editable External KnowledgeCode1
SPA-VL: A Comprehensive Safety Preference Alignment Dataset for Vision Language ModelCode1
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language ModelCode1
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-trainingCode1
Self-Supervised Representation Learning with Spatial-Temporal Consistency for Sign Language RecognitionCode1
Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language ModelsCode1
Large language model validity via enhanced conformal prediction methodsCode1
The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Pre-trained Language ModelsCode1
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
Enhancing Domain Adaptation through Prompt Gradient AlignmentCode1
Newswire: A Large-Scale Structured Database of a Century of Historical NewsCode1
Large Language Model Unlearning via Embedding-Corrupted PromptsCode1
Collective Constitutional AI: Aligning a Language Model with Public InputCode1
Advancing High Resolution Vision-Language Models in BiomedicineCode1
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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