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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16011650 of 17610 papers

TitleStatusHype
Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited ResponsesCode1
EasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMsCode1
HARDMath: A Benchmark Dataset for Challenging Problems in Applied MathematicsCode1
PoisonBench: Assessing Large Language Model Vulnerability to Data PoisoningCode1
Parameter-Efficient Fine-Tuning of State Space ModelsCode1
PEAR: A Robust and Flexible Automation Framework for Ptychography Enabled by Multiple Large Language Model AgentsCode1
Hespi: A pipeline for automatically detecting information from hebarium specimen sheetsCode1
Zeroth-Order Fine-Tuning of LLMs in Random SubspacesCode1
Retraining-Free Merging of Sparse MoE via Hierarchical ClusteringCode1
Do Unlearning Methods Remove Information from Language Model Weights?Code1
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical ReasoningCode1
AgroGPT: Efficient Agricultural Vision-Language Model with Expert TuningCode1
Bilinear MLPs enable weight-based mechanistic interpretabilityCode1
Multi-Agent Collaborative Data Selection for Efficient LLM PretrainingCode1
OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model PromptingCode1
AuditWen:An Open-Source Large Language Model for AuditCode1
Simplicity Prevails: Rethinking Negative Preference Optimization for LLM UnlearningCode1
Vector-ICL: In-context Learning with Continuous Vector RepresentationsCode1
Training-free Diffusion Model Alignment with Sampling DemonsCode1
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot CornucopiaCode1
ImProver: Agent-Based Automated Proof OptimizationCode1
Large Language Model Inference Acceleration: A Comprehensive Hardware PerspectiveCode1
LongGenBench: Long-context Generation BenchmarkCode1
Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music RetrievalCode1
You Know What I'm Saying: Jailbreak Attack via Implicit ReferenceCode1
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language ModelCode1
General Preference Modeling with Preference Representations for Aligning Language ModelsCode1
DivScene: Benchmarking LVLMs for Object Navigation with Diverse Scenes and ObjectsCode1
ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent CollaborationCode1
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model CompressionCode1
Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence ModelingCode1
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge AcquisitionCode1
EMMA: Efficient Visual Alignment in Multi-Modal LLMsCode1
Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade DevicesCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
RisingBALLER: A player is a token, a match is a sentence, A path towards a foundational model for football players data analyticsCode1
Empowering Large Language Model for Continual Video Question Answering with Collaborative PromptingCode1
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMsCode1
LML-DAP: Language Model Learning a Dataset for Data-Augmented PredictionCode1
DualAD: Dual-Layer Planning for Reasoning in Autonomous DrivingCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Counterfactual Token Generation in Large Language ModelsCode1
Training Language Models to Win Debates with Self-Play Improves Judge AccuracyCode1
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient ModificationCode1
FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text CompressionCode1
LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation GenerationCode1
Instruction Following without Instruction TuningCode1
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP TasksCode1
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and ResourcesCode1
DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic ConsistencyCode1
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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