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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 17351–17400 of 17610 papers

TitleStatusHype
Dobby: A Conversational Service Robot Driven by GPT-4—0
DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model—0
Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation—0
DocGraphLM: Documental Graph Language Model for Information Extraction—0
Do Children Texts Hold The Key To Commonsense Knowledge?—0
DocLLM: A layout-aware generative language model for multimodal document understanding—0
DOCmT5: Document-Level Pre-training of Multilingual Language Models—0
DOCmT5: Document-Level Pretraining of Multilingual Language Models—0
DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding—0
DocReLM: Mastering Document Retrieval with Language Model—0
Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation—0
Document-Level Language Models for Machine Translation—0
Document-Level Machine Translation with Word Vector Models—0
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments—0
Document-Wide Decoding for Phrase-Based Statistical Machine Translation—0
DocuT5: Seq2seq SQL Generation with Table Documentation—0
Do End-to-End Speech Recognition Models Care About Context?—0
Does a Large Language Model Really Speak in Human-Like Language?—0
Does BERT Understand Sentiment? Leveraging Comparisons Between Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment Models—0
Does Cross-Cultural Alignment Change the Commonsense Morality of Language Models?—0
Does He Wink or Does He Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models—0
Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers—0
Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?—0
Does Knowledge Help General NLU? An Empirical Study—0
Does Meta-learning Help mBERT for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?—0
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?—0
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge—0
Does She Wink or Does She Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models—0
Does Syntactic Knowledge in Multilingual Language Models Transfer Across Languages?—0
Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring?—0
Does your data spark joy? Performance gains from domain upsampling at the end of training—0
DoGE: Domain Reweighting with Generalization Estimation—0
Do Generative Large Language Models need billions of parameters?—0
Do GPT Language Models Suffer From Split Personality Disorder? The Advent Of Substrate-Free Psychometrics—0
Doing More with Less -- Implementing Routing Strategies in Large Language Model-Based Systems: An Extended Survey—0
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software—0
Do Language Models Have Common Sense?—0
Do Language Models Know the Way to Rome?—0
Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items—0
Do Language Models Understand Measurements?—0
Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings—0
Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5—0
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models—0
Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection—0
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization—0
Domain Adaptation for Medical Text Translation using Web Resources—0
Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found—0
Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation—0
Domain-adaptation of spherical embeddings—0
Domain-adapted large language models for classifying nuclear medicine reports—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