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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 3101–3150 of 17610 papers

TitleStatusHype
Reverse-Engineering the ReaderCode0
HerO at AVeriTeC: The Herd of Open Large Language Models for Verifying Real-World ClaimsCode1
Tuning Language Models by Mixture-of-Depths Ensemble—0
Optimizing Low-Resource Language Model Training: Comprehensive Analysis of Multi-Epoch, Multi-Lingual, and Two-Stage Approaches—0
MedAide: Towards an Omni Medical Aide via Specialized LLM-based Multi-Agent Collaboration—0
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning—0
Can We Reverse In-Context Knowledge Edits?—0
ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing—0
LFOSum: Summarizing Long-form Opinions with Large Language Models—0
BenchmarkCards: Large Language Model and Risk Reporting—0
Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation—0
StyleDistance: Stronger Content-Independent Style Embeddings with Synthetic Parallel Examples—0
End-to-end Planner Training for Language Modeling—0
Tracking Universal Features Through Fine-Tuning and Model Merging—0
CREAM: Consistency Regularized Self-Rewarding Language ModelsCode1
Sarcasm Detection in a Less-Resourced LanguageCode0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
Explainable Moral Values: a neuro-symbolic approach to value classification—0
Revisited Large Language Model for Time Series Analysis through Modality Alignment—0
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuningCode0
VividMed: Vision Language Model with Versatile Visual Grounding for MedicineCode1
Search Engines in an AI Era: The False Promise of Factual and Verifiable Source-Cited ResponsesCode1
FVEval: Understanding Language Model Capabilities in Formal Verification of Digital HardwareCode1
Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models—0
The Moral Case for Using Language Model Agents for Recommendation—0
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language ModelsCode0
De-jargonizing Science for Journalists with GPT-4: A Pilot StudyCode0
Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing—0
The Fair Language Model Paradox—0
O-Edit: Orthogonal Subspace Editing for Language Model Sequential Editing—0
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router—0
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices—0
TopoLM: brain-like spatio-functional organization in a topographic language modelCode1
LargePiG: Your Large Language Model is Secretly a Pointer Generator—0
A Framework for Adapting Human-Robot Interaction to Diverse User GroupsCode0
WeatherDG: LLM-assisted Diffusion Model for Procedural Weather Generation in Domain-Generalized Semantic SegmentationCode2
Sabiá-3 Technical Report—0
EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation—0
DISP-LLM: Dimension-Independent Structural Pruning for Large Language ModelsCode1
Tokenization and Morphology in Multilingual Language Models: A Comparative Analysis of mT5 and ByT5—0
Sequential LLM Framework for Fashion Recommendation—0
SGEdit: Bridging LLM with Text2Image Generative Model for Scene Graph-based Image Editing—0
SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments—0
Retrieval Augmented Spelling Correction for E-Commerce Applications—0
A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem—0
GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable RecommendationCode1
Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex CapabilitiesCode5
ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training—0
PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model—0
MoChat: Joints-Grouped Spatio-Temporal Grounding LLM for Multi-Turn Motion Comprehension and Description—0
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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