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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2051–2100 of 17610 papers

TitleStatusHype
Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and VideosCode5
CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds Ratio on High-Resolution Point Clouds—0
Analyzing Bias in Swiss Federal Supreme Court Judgments Using Facebook's Holistic Bias Dataset: Implications for Language Model Training—0
Multi-Modal One-Shot Federated Ensemble Learning for Medical Data with Vision Large Language Model—0
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
LangFair: A Python Package for Assessing Bias and Fairness in Large Language Model Use CasesCode3
Segmenting Text and Learning Their Rewards for Improved RLHF in Language ModelCode1
IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment—0
Piano Transcription by Hierarchical Language Modeling with Pretrained Roll-based Encoders—0
Decoding fMRI Data into Captions using Prefix Language ModelingCode0
From Superficial Patterns to Semantic Understanding: Fine-Tuning Language Models on Contrast Sets—0
LLMPC: Large Language Model Predictive ControlCode0
Towards the Anonymization of the Language Modeling—0
Hengqin-RA-v1: Advanced Large Language Model for Diagnosis and Treatment of Rheumatoid Arthritis with Dataset based Traditional Chinese Medicine—0
DeServe: Towards Affordable Offline LLM Inference via Decentralization—0
Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving—0
Establishing baselines for generative discovery of inorganic crystalsCode1
Learning Evolution via Optimization Knowledge Adaptation—0
Graph-Aware Isomorphic Attention for Adaptive Dynamics in TransformersCode2
PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars—0
Integrating Domain Knowledge into Large Language Models for Enhanced Fashion Recommendations—0
FLAME: Financial Large-Language Model Assessment and Metrics EvaluationCode2
CarbonChat: Large Language Model-Based Corporate Carbon Emission Analysis and Climate Knowledge Q&A System—0
Time Series Language Model for Descriptive Caption Generation—0
Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration DecodingCode1
Abstractive Text Summarization for Contemporary Sanskrit Prose: Issues and Challenges—0
Reading Between the Lines: A dataset and a study on why some texts are tougher than othersCode0
Virgo: A Preliminary Exploration on Reproducing o1-like MLLMCode2
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
Metadata Conditioning Accelerates Language Model Pre-trainingCode2
Turning Logic Against Itself : Probing Model Defenses Through Contrastive QuestionsCode0
Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion—0
MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model—0
Does a Large Language Model Really Speak in Human-Like Language?—0
Risks of Cultural Erasure in Large Language Models—0
NeutraSum: A Language Model can help a Balanced Media Diet by Neutralizing News Summaries—0
MSWA: Refining Local Attention with Multi-ScaleWindow Attention—0
Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability—0
An Efficient Attention Mechanism for Sequential Recommendation Tasks: HydraRec—0
Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation—0
GroundingFace: Fine-grained Face Understanding via Pixel Grounding Multimodal Large Language Model—0
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling—0
Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language ModelCode0
LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale—0
Taxonomy-Aware Evaluation of Vision-Language Models—0
Once-Tuning-Multiple-Variants: Tuning Once and Expanded as Multiple Vision-Language Model Variants—0
HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction—0
HOIGPT: Learning Long-Sequence Hand-Object Interaction with Language Models—0
DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving—0
Symbolic Representation for Any-to-Any Generative Tasks—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