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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 13901–13950 of 17610 papers

TitleStatusHype
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection—0
HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning—0
Hybrid Morphological Segmentation for Phrase-Based Machine Translation—0
HybriDNA: A Hybrid Transformer-Mamba2 Long-Range DNA Language Model—0
Hybrid Offline-online Scheduling Method for Large Language Model Inference Optimization—0
Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier—0
Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track—0
Hybrid-RACA: Hybrid Retrieval-Augmented Composition Assistance for Real-time Text Prediction—0
Hybrid Selection of Language Model Training Data Using Linguistic Information and Perplexity—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
Hybrid Student-Teacher Large Language Model Refinement for Cancer Toxicity Symptom Extraction—0
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks—0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance—0
HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation—0
Hypernymization of named entity-rich captions for grounding-based multi-modal pretraining—0
Hyperparameter Optimization for Large Language Model Instruction-Tuning—0
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks—0
Hyperspherical Query Likelihood Models with Word Embeddings—0
HyperTuning: Toward Adapting Large Language Models without Back-propagation—0
Hypothesis-only Biases in Large Language Model-Elicited Natural Language Inference—0
Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings—0
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification—0
IAPT: Instruction-Aware Prompt Tuning for Large Language Models—0
IBM MNLP IE at CASE 2021 Task 1: Multigranular and Multilingual Event Detection on Protest News—0
I Can Has Cheezburger? A Nonparanormal Approach to Combining Textual and Visual Information for Predicting and Generating Popular Meme Descriptions—0
ICCV23 Visual-Dialog Emotion Explanation Challenge: SEU_309 Team Technical Report—0
ICE-SEARCH: A Language Model-Driven Feature Selection Approach—0
ICH-Qwen: A Large Language Model Towards Chinese Intangible Cultural Heritage—0
iCompass at SemEval-2020 Task 12: From a Syntax-ignorant N-gram Embeddings Model to a Deep Bidirectional Language Model—0
ICONS: Influence Consensus for Vision-Language Data Selection—0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation—0
ID-centric Pre-training for Recommendation—0
IDEAL: Data Equilibrium Adaptation for Multi-Capability Language Model Alignment—0
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining—0
Identification de profil clinique du patient: Une approche de classification de séquences utilisant des modèles de langage français contextualisés (Identification of patient clinical profiles : A sequence classification approach using contextualised French language models )—0
Identification of Enzymatic Active Sites with Unsupervised Language Modeling—0
Identifying Adversarial Attacks on Text Classifiers—0
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
Identifying and Manipulating the Personality Traits of Language Models—0
Identifying and Reducing Gender Bias in Word-Level Language Models—0
Identifying Comparable Corpora Using LDA—0
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy—0
Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses—0
Identifying Humor in Reviews using Background Text Sources—0
Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling—0
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating—0
Identifying Planetary Names in Astronomy Papers: A Multi-Step Approach—0
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing—0
Identifying the L1 of non-native writers: the CMU-Haifa system—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