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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8551–8600 of 17610 papers

TitleStatusHype
ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations—0
Copy Suppression: Comprehensively Understanding an Attention HeadCode1
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity—0
Functional Interpolation for Relative Positions Improves Long Context Transformers—0
An In-Context Learning Agent for Formal Theorem-ProvingCode1
Chain of Natural Language Inference for Reducing Large Language Model Ungrounded HallucinationsCode1
From task structures to world models: What do LLMs know?—0
Lending Interaction Wings to Recommender Systems with Conversational Agents—0
Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interface—0
Quantized Transformer Language Model Implementations on Edge Devices—0
RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective AugmentationCode1
Understanding prompt engineering may not require rethinking generalization—0
PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language ModelingCode1
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction—0
Neural Language Model Pruning for Automatic Speech Recognition—0
Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards—0
GoLLIE: Annotation Guidelines improve Zero-Shot Information-ExtractionCode2
TRAM: Bridging Trust Regions and Sharpness Aware MinimizationCode0
Beyond One-Preference-Fits-All Alignment: Multi-Objective Direct Preference OptimizationCode1
A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions—0
Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity—0
DSPy: Compiling Declarative Language Model Calls into Self-Improving PipelinesCode7
Learning to Prompt Your Domain for Vision-Language Models—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory ArchitectureCode1
Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient ReasoningCode1
DOMINO: A Dual-System for Multi-step Visual Language ReasoningCode1
Discovering Knowledge-Critical Subnetworks in Pretrained Language ModelsCode0
From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inference—0
Heterogeneous Federated Learning Using Knowledge Codistillation—0
Zero Resource Code-switched Speech Benchmark Using Speech Utterance Pairs For Multiple Spoken LanguagesCode0
HPC-GPT: Integrating Large Language Model for High-Performance Computing—0
An evolutionary model of personality traits related to cooperative behavior using a large language model—0
Can a student Large Language Model perform as well as it's teacher?—0
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context RetrievalCode0
Linear Recurrent Units for Sequential RecommendationCode1
Dodo: Dynamic Contextual Compression for Decoder-only LMs—0
Self-Taught Optimizer (STOP): Recursively Self-Improving Code GenerationCode1
Ring Attention with Blockwise Transformers for Near-Infinite ContextCode2
Large Language Models for Test-Free Fault LocalizationCode1
SEA: Sparse Linear Attention with Estimated Attention MaskCode1
Stack Attention: Improving the Ability of Transformers to Model Hierarchical PatternsCode1
Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and BeyondCode1
OceanGPT: A Large Language Model for Ocean Science TasksCode3
Nugget: Neural Agglomerative Embeddings of TextCode0
Sieve: Multimodal Dataset Pruning Using Image Captioning ModelsCode1
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous DrivingCode1
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent CollaborationCode1
Who's Harry Potter? Approximate Unlearning in LLMs—0
TWIZ-v2: The Wizard of Multimodal Conversational-Stimulus—0
Show:102550
← PrevPage 172 of 353Next →
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