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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6551–6600 of 17610 papers

TitleStatusHype
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
A Voter-Based Stochastic Rejection-Method Framework for Asymptotically Safe Language Model Outputs—0
AVScan2Vec: Feature Learning on Antivirus Scan Data for Production-Scale Malware Corpora—0
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis—0
Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings—0
Awes, Laws, and Flaws From Today's LLM Research—0
A Wikipedia-based Corpus for Contextualized Machine Translation—0
AWOL: Analysis WithOut synthesis using Language—0
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs—0
Aya 23: Open Weight Releases to Further Multilingual Progress—0
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model—0
A Zero-Shot Classification Approach for a Word-Guessing Challenge—0
Babler - Data Collection from the Web to Support Speech Recognition and Keyword Search—0
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models—0
BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency—0
BabyLMs for isiXhosa: Data-Efficient Language Modelling in a Low-Resource Context—0
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop—0
Backdoor Attacks with Input-unique Triggers in NLP—0
Back from the future: bidirectional CTC decoding using future information in speech recognition—0
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity—0
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema—0
Backtracking Improves Generation Safety—0
Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification—0
Backward and Forward Language Modeling for Constrained Sentence Generation—0
Backward Lens: Projecting Language Model Gradients into the Vocabulary Space—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
BadRobot: Jailbreaking Embodied LLMs in the Physical World—0
BAGEL: Bootstrapping Agents by Guiding Exploration with Language—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline—0
BaKlaVa -- Budgeted Allocation of KV cache for Long-context Inference—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Balancing Average and Worst-case Accuracy in Multitask Learning—0
Balancing Computation Load and Representation Expressivity in Parallel Hybrid Neural Networks—0
Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability—0
Balancing Performance and Efficiency: A Multimodal Large Language Model Pruning Method based Image Text Interaction—0
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM—0
BAMBI: Developing Baby Language Models for Italian—0
BANANA: a Benchmark for the Assessment of Neural Architectures for Nucleic Acids—0
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla—0
BanglaHateBERT: BERT for Abusive Language Detection in Bengali—0
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Models—0
Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model—0
BART based semantic correction for Mandarin automatic speech recognition system—0
BART for Post-Correction of OCR Newspaper Text—0
BART-light: One Decoder Layer Is Enough—0
BAS: An Answer Selection Method Using BERT Language Model—0
BasedAI: A decentralized P2P network for Zero Knowledge Large Language Models (ZK-LLMs)—0
Show:102550
← PrevPage 132 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