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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6851–6900 of 17610 papers

TitleStatusHype
Bridging the Gap: Deciphering Tabular Data Using Large Language Model—0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
Bridging Vision and Language: Modeling Causality and Temporality in Video Narratives—0
Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation—0
BriLLM: Brain-inspired Large Language Model—0
Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
Bring Remote Sensing Object Detect Into Nature Language Model: Using SFT Method—0
BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning—0
Broad Context Language Modeling as Reading Comprehension—0
Broadening Discovery through Structural Models: Multimodal Combination of Local and Structural Properties for Predicting Chemical Features—0
BROS: A Pre-trained Language Model for Understanding Texts in Document—0
Brown University at TREC Deep Learning 2019—0
bs,hr,srWaC - Web Corpora of Bosnian, Croatian and Serbian—0
BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora—0
BuDDIE: A Business Document Dataset for Multi-task Information Extraction—0
BudgetLongformer: Can we Cheaply Pretrain a SotA Legal Language Model From Scratch?—0
BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection—0
Buhscitu at SemEval-2020 Task 7: Assessing Humour in Edited News Headlines Using Hand-Crafted Features and Online Knowledge Bases—0
Building a Functional Machine Translation Corpus for Kpelle—0
Building a Lemmatizer and a Spell-checker for Sorani Kurdish—0
Building and Evaluating Somali Language Corpora—0
Building and Modelling Multilingual Subjective Corpora—0
Building astroBERT, a language model for Astronomy & Astrophysics—0
Building bilingual lexicon to create Dialect Tunisian corpora and adapt language model—0
Building competitive direct acoustics-to-word models for English conversational speech recognition—0
Building Decision Making Models Through Language Model Regime—0
Building English ASR model with regional language support—0
Building Flexible Machine Learning Models for Scientific Computing at Scale—0
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities—0
Building Intelligence Identification System via Large Language Model Watermarking: A Survey and Beyond—0
Building Language Models for Morphological Rich Low-Resource Languages using Data from Related Donor Languages: the Case of Uyghur—0
Building Metadata Inference Using a Transducer Based Language Model—0
Building Open-Ended Embodied Agent via Language-Policy Bidirectional Adaptation—0
Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline—0
Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents—0
Business Process Text Sketch Automation Generation Using Large Language Model—0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way—0
Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers—0
Bypassing LLM Watermarks with Color-Aware Substitutions—0
Byte-based Neural Machine Translation—0
ByteComposer: a Human-like Melody Composition Method based on Language Model Agent—0
ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity—0
C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition—0
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval—0
C3LLM: Conditional Multimodal Content Generation Using Large Language Models—0
C4Q: A Chatbot for Quantum—0
Cache-Augmented Latent Topic Language Models for Speech Retrieval—0
Cache & Distil: Optimising API Calls to Large Language Models—0
CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers—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