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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6801–6850 of 17610 papers

TitleStatusHype
A Dutch Financial Large Language ModelCode0
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling—0
Selective Attention Improves Transformer—0
SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM InversionCode0
Large Language Model Aided Multi-objective Evolutionary Algorithm: a Low-cost Adaptive Approach—0
LLMCO2: Advancing Accurate Carbon Footprint Prediction for LLM Inferences—0
UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation—0
Multi-modal clothing recommendation model based on large model and VAE enhancement—0
NL-Eye: Abductive NLI for Images—0
Neutral residues: revisiting adapters for model extension—0
SEAL: SEmantic-Augmented Imitation Learning via Language Model—0
On the Proper Treatment of Tokenization in PsycholinguisticsCode0
MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation—0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data—0
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
Morphological evaluation of subwords vocabulary used by BETO language model—0
Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDL—0
Learning the Latent Rules of a Game from Data: A Chess Story—0
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition—0
Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce—0
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning—0
LS-HAR: Language Supervised Human Action Recognition with Salient Fusion, Construction Sites as a Use-Case—0
TypedThinker: Typed Thinking Improves Large Language Model ReasoningCode0
Racing Thoughts: Explaining Contextualization Errors in Large Language Models—0
OCC-MLLM:Empowering Multimodal Large Language Model For the Understanding of Occluded Objects—0
Spoken Grammar Assessment Using LLM—0
Long-range gene expression prediction with token alignment of large language model—0
Mind Scramble: Unveiling Large Language Model Psychology Via TypoglycemiaCode0
SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific TopicsCode0
Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker—0
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Frozen Large Language Models Can Perceive Paralinguistic Aspects of Speech—0
From Reward Shaping to Q-Shaping: Achieving Unbiased Learning with LLM-Guided Knowledge—0
Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models—0
Investigating on RLHF methodology—0
Agent-Driven Large Language Models for Mandarin Lyric Generation—0
Efficient Length-Generalizable Attention via Causal Retrieval for Long-Context Language Modeling—0
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model—0
Elaborative Subtopic Query Reformulation for Broad and Indirect Queries in Travel Destination RecommendationCode0
Automatic deductive coding in discourse analysis: an application of large language models in learning analyticsCode0
Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes—0
FARM: Functional Group-Aware Representations for Small Molecules—0
ConServe: Harvesting GPUs for Low-Latency and High-Throughput Large Language Model Serving—0
When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1—0
Unveiling Language Skills via Path-Level Circuit DiscoveryCode0
ViDAS: Vision-based Danger Assessment and Scoring—0
ReXplain: Translating Radiology into Patient-Friendly Video Reports—0
Removing Distributional Discrepancies in Captions Improves Image-Text Alignment—0
ScVLM: Enhancing Vision-Language Model for Safety-Critical Event UnderstandingCode0
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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