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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3301–3350 of 17610 papers

TitleStatusHype
PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse SamplingCode2
Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model—0
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics—0
ChatVis: Automating Scientific Visualization with a Large Language ModelCode0
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths—0
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction—0
Filtering Discomforting Recommendations with Large Language Models—0
Towards the generation of hierarchical attack models from cybersecurity vulnerabilities using language models—0
Falcon Mamba: The First Competitive Attention-free 7B Language Model—0
Transformers learn variable-order Markov chains in-context—0
Chain and Causal Attention for Efficient Entity Tracking—0
Neural machine translation system for Lezgian, Russian and Azerbaijani languagesCode0
Differential TransformerCode2
Efficient Inference for Large Language Model-based Generative RecommendationCode0
DEPT: Decoupled Embeddings for Pre-training Language Models—0
Synthesizing Interpretable Control Policies through Large Language Model Guided SearchCode0
LPZero: Language Model Zero-cost Proxy Search from Zero—0
ImProver: Agent-Based Automated Proof OptimizationCode1
Data Advisor: Dynamic Data Curation for Safety Alignment of Large Language ModelsCode0
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot CornucopiaCode1
Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law—0
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention CausalityCode2
Learning How Hard to Think: Input-Adaptive Allocation of LM Computation—0
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe—0
Activation Scaling for Steering and Interpreting Language Models—0
Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM—0
TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer TokensCode2
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
On the Reliability of Large Language Models to Misinformed and Demographically-Informed PromptsCode0
HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis—0
ReTok: Replacing Tokenizer to Enhance Representation Efficiency in Large Language Model—0
Large Language Model Inference Acceleration: A Comprehensive Hardware PerspectiveCode1
OD-Stega: LLM-Based Near-Imperceptible Steganography via Optimized Distributions—0
GenSim: A General Social Simulation Platform with Large Language Model based AgentsCode2
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model InferenceCode3
Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?—0
Language Model-Driven Data Pruning Enables Efficient Active Learning—0
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations—0
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language ModelsCode2
RoQLlama: A Lightweight Romanian Adapted Language Model—0
Toxic Subword Pruning for Dialogue Response Generation on Large Language Models—0
Evaluating Language Model Character TraitsCode0
Persona Knowledge-Aligned Prompt Tuning Method for Online DebateCode0
LongGenBench: Long-context Generation BenchmarkCode1
SyllableLM: Learning Coarse Semantic Units for Speech Language ModelsCode2
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge GraphsCode0
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation—0
Textless Streaming Speech-to-Speech Translation using Semantic Speech Tokens—0
Searching for Best Practices in Medical Transcription with Large Language ModelCode0
Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style—0
Show:102550
← PrevPage 67 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