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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8751–8800 of 17610 papers

TitleStatusHype
Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers—0
Towards Understanding the Influence of Reward Margin on Preference Model Performance—0
How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM—0
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector—0
X-VARS: Introducing Explainability in Football Refereeing with Multi-Modal Large Language Model—0
What Happens When Small Is Made Smaller? Exploring the Impact of Compression on Small Data Pretrained Language Models—0
Binary Classifier Optimization for Large Language Model Alignment—0
Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology—0
Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm—0
Physics Event Classification Using Large Language ModelsCode0
player2vec: A Language Modeling Approach to Understand Player Behavior in Games—0
BuDDIE: A Business Document Dataset for Multi-task Information Extraction—0
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model—0
Implicit Bias of AdamW: _ Norm Constrained Optimization—0
A Comparison of Methods for Evaluating Generative IRCode0
Forget NLI, Use a Dictionary: Zero-Shot Topic Classification for Low-Resource Languages with Application to LuxembourgishCode0
Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving—0
Dwell in the Beginning: How Language Models Embed Long Documents for Dense RetrievalCode0
Edisum: Summarizing and Explaining Wikipedia Edits at ScaleCode0
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought—0
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification—0
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training—0
Mitigating LLM Hallucinations via Conformal Abstention—0
Multi-modal Learning for WebAssembly Reverse Engineering—0
SemGrasp: Semantic Grasp Generation via Language Aligned Discretization—0
Towards Pareto Optimal Throughput in Small Language Model Serving—0
OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning—0
Bias Amplification in Language Model Evolution: An Iterated Learning PerspectiveCode0
Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation RegularizationCode0
RALL-E: Robust Codec Language Modeling with Chain-of-Thought Prompting for Text-to-Speech Synthesis—0
Standardizing Knowledge Engineering Practices with a Reference Architecture—0
Understanding Language Modeling Paradigm Adaptations in Recommender Systems: Lessons Learned and Open ChallengesCode0
Manipulating and Mitigating Generative Model Biases without Retraining—0
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian—0
PhonologyBench: Evaluating Phonological Skills of Large Language Models—0
Testing the Effect of Code Documentation on Large Language Model Code Understanding—0
Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages—0
LVLM-Interpret: An Interpretability Tool for Large Vision-Language ModelsCode0
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison—0
Vocabulary Attack to Hijack Large Language Model Applications—0
Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model—0
Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models—0
Attributions toward Artificial Agents in a modified Moral Turing Test—0
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers—0
CLaM-TTS: Improving Neural Codec Language Model for Zero-Shot Text-to-Speech—0
From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives—0
Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation—0
FPT: Feature Prompt Tuning for Few-shot Readability AssessmentCode0
ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language ModelCode0
AWOL: Analysis WithOut synthesis using Language—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