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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 16201–16250 of 17610 papers

TitleStatusHype
Causal Inference with Large Language Model: A Survey—0
Causal Language Model for Zero-shot Constrained Keyphrase Generation—0
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment—0
Data Augmentations for Improved (Large) Language Model Generalization—0
caWaC -- A web corpus of Catalan and its application to language modeling and machine translation—0
CBAG: Conditional Biomedical Abstract Generation—0
CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering—0
CCAE: A Corpus of Chinese-based Asian Englishes—0
CCoE: A Compact LLM with Collaboration of Experts—0
CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering—0
CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels—0
CELI: Controller-Embedded Language Model Interactions—0
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments—0
CephGPT-4: An Interactive Multimodal Cephalometric Measurement and Diagnostic System with Visual Large Language Model—0
Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks—0
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System—0
CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model—0
Read and Think: An Efficient Step-wise Multimodal Language Model for Document Understanding and Reasoning—0
CFunModel: A "Funny" Language Model Capable of Chinese Humor Generation and Processing—0
CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection—0
Chain and Causal Attention for Efficient Entity Tracking—0
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners—0
Chain of Alignment: Integrating Public Will with Expert Intelligence for Language Model Alignment—0
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering—0
Chain-of-Though (CoT) prompting strategies for medical error detection and correction—0
Chain of Thoughtlessness? An Analysis of CoT in Planning—0
Chain of Thought Prompt Tuning in Vision Language Models—0
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization—0
Chain-of-Thought Training for Open E2E Spoken Dialogue Systems—0
Chain of Tools: Large Language Model is an Automatic Multi-tool Learner—0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment—0
Challenge Closed-book Science Exam: A Meta-learning Based Question Answering System—0
Challenges in Grounding Language in the Real World—0
Challenges in including extra-linguistic context in pre-trained language models—0
Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval—0
Challenges of Computational Processing of Code-Switching—0
Challenges with unsupervised LLM knowledge discovery—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness—0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech—0
ChapGTP, ILLC's Attempt at Raising a BabyLM: Improving Data Efficiency by Automatic Task Formation—0
Character and Subword-Based Word Representation for Neural Language Modeling Prediction—0
Character-aware audio-visual subtitling in context—0
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking—0
Character-based Neural Machine Translation—0
Character-based recurrent neural networks for morphological relational reasoning—0
Character-Centric Storytelling—0
CharacterChat: Supporting the Creation of Fictional Characters through Conversation and Progressive Manifestation with a Chatbot—0
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models—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