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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 701–750 of 17610 papers

TitleStatusHype
Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model—0
A Large Language Model for Feasible and Diverse Population Synthesis—0
On-Device LLM for Context-Aware Wi-Fi RoamingCode0
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review—0
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis—0
A Vision-Language Model for Focal Liver Lesion Classification—0
Mitigating Image Captioning Hallucinations in Vision-Language Models—0
Validating the Effectiveness of a Large Language Model-based Approach for Identifying Children's Development across Various Free Play Settings in Kindergarten—0
Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering—0
VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language ModelCode4
Soft Best-of-n Sampling for Model Alignment—0
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling—0
Leveraging Protein Language Model Embeddings for Catalytic Turnover Prediction of Adenylate Kinase Orthologs in a Low-Data RegimeCode0
Automatic Proficiency Assessment in L2 English Learners—0
SIMPLEMIX: Frustratingly Simple Mixing of Off- and On-policy Data in Language Model Preference Learning—0
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices—0
Bielik 11B v2 Technical Report—0
Bielik v3 Small: Technical Report—0
Giving Simulated Cells a Voice: Evolving Prompt-to-Intervention Models for Cellular Control—0
Large Language Model Partitioning for Low-Latency Inference at the Edge—0
TeDA: Boosting Vision-Lanuage Models for Zero-Shot 3D Object Retrieval via Testing-time Distribution AlignmentCode0
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Technical Report: Evaluating Goal Drift in Language Model Agents—0
Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning—0
A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models—0
R-Bench: Graduate-level Multi-disciplinary Benchmarks for LLM & MLLM Complex Reasoning Evaluation—0
What do Language Model Probabilities Represent? From Distribution Estimation to Response Prediction—0
MemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based AgentsCode2
LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia LearningCode0
DNAZEN: Enhanced Gene Sequence Representations via Mixed Granularities of Coding UnitsCode0
Vision and Intention Boost Large Language Model in Long-Term Action Anticipation—0
Accelerating Large Language Model Reasoning via Speculative Search—0
Intra-Layer Recurrence in Transformers for Language ModelingCode0
Facilitating Video Story Interaction with Multi-Agent Collaborative System—0
WirelessAgent: Large Language Model Agents for Intelligent Wireless NetworksCode1
Seeking to Collide: Online Safety-Critical Scenario Generation for Autonomous Driving with Retrieval Augmented Large Language Models—0
On the Limitations of Steering in Language Model Alignment—0
Large Language Model-Driven Dynamic Assessment of Grammatical Accuracy in English Language Learner Writing—0
Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languages—0
FlowDubber: Movie Dubbing with LLM-based Semantic-aware Learning and Flow Matching based Voice Enhancing—0
Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation—0
Document Retrieval Augmented Fine-Tuning (DRAFT) for safety-critical software assessments—0
Nesterov Method for Asynchronous Pipeline Parallel OptimizationCode1
PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding—0
CodeSSM: Towards State Space Models for Code Understanding—0
LLM Watermarking Using Mixtures and Statistical-to-Computational Gaps—0
AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and CareCode0
Patchwork: A Unified Framework for RAG Serving—0
KoACD: The First Korean Adolescent Dataset for Cognitive Distortion Analysis—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