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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9951–10000 of 17610 papers

TitleStatusHype
Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization—0
Mitigating harm in language models with conditional-likelihood filtration—0
Mitigating Image Captioning Hallucinations in Vision-Language Models—0
Mitigating Knowledge Conflicts in Language Model-Driven Question Answering—0
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting—0
Mitigating Large Language Model Hallucination with Faithful Finetuning—0
Mitigating LLM Hallucinations via Conformal Abstention—0
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals—0
Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer—0
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models—0
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models—0
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging—0
Mixed-Distil-BERT: Code-mixed Language Modeling for Bangla, English, and Hindi—0
Mixed Distillation Helps Smaller Language Model Better Reasoning—0
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories—0
Mixed Membership Word Embeddings for Computational Social Science—0
Mixer Metaphors: audio interfaces for non-musical applications—0
Mixing Multiple Translation Models in Statistical Machine Translation—0
MixMin: Finding Data Mixtures via Convex Minimization—0
Mix of Experts Language Model for Named Entity Recognition—0
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches—0
Mixtape: Breaking the Softmax Bottleneck Efficiently—0
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference—0
Mixture of Experts in Large Language Models—0
Mixture of Latent Experts Using Tensor Products—0
Mixture of Experts with Mixture of Precisions for Tuning Quality of Service—0
Mixture-of-Instructions: Comprehensive Alignment of a Large Language Model through the Mixture of Diverse System Prompting Instructions—0
Getting MoRE out of Mixture of Language Model Reasoning Experts—0
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding—0
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization—0
MLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking—0
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
M-LLM Based Video Frame Selection for Efficient Video Understanding—0
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning—0
MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation—0
MLLMReID: Multimodal Large Language Model-based Person Re-identification—0
ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2—0
MLMLM: Link Prediction with Mean Likelihood Masked Language Model—0
MLorc: Momentum Low-rank Compression for Large Language Model Adaptation—0
MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding—0
MMAC-Copilot: Multi-modal Agent Collaboration Operating Copilot—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
MMCR: Advancing Visual Language Model in Multimodal Multi-Turn Contextual Reasoning—0
MMDS: A Multimodal Medical Diagnosis System Integrating Image Analysis and Knowledge-based Departmental Consultation—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation—0
MMMModal -- Multi-Images Multi-Audio Multi-turn Multi-Modal—0
MMM: Multilingual Mutual Reinforcement Effect Mix Datasets & Test with Open-domain Information Extraction Large Language Models—0
MM-MovieDubber: Towards Multi-Modal Learning for Multi-Modal Movie Dubbing—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