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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3551–3600 of 17610 papers

TitleStatusHype
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style ControlCode3
Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation—0
dnaGrinder: a lightweight and high-capacity genomic foundation model—0
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQLCode0
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
EnIGMA: Enhanced Interactive Generative Model Agent for CTF ChallengesCode0
BeSimulator: A Large Language Model Powered Text-based Behavior Simulator—0
Small Language Models: Survey, Measurements, and InsightsCode2
LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation GenerationCode1
VLMine: Long-Tail Data Mining with Vision Language Models—0
A-VL: Adaptive Attention for Large Vision-Language ModelsCode0
Automatic Feature Learning for Essence: a Case Study on Car Sequencing—0
Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond—0
Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog—0
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks—0
Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task—0
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling—0
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning—0
AlphaZip: Neural Network-Enhanced Lossless Text CompressionCode0
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning—0
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI UnderstandingCode2
Target-Aware Language Modeling via Granular Data Sampling—0
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video UnderstandingCode4
Backtracking Improves Generation Safety—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec modelsCode5
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning—0
Test Time Learning for Time Series Forecasting—0
OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching—0
Instruction Following without Instruction TuningCode1
Data-centric NLP Backdoor Defense from the Lens of Memorization—0
Probing Context Localization of Polysemous Words in Pre-trained Language Model Sub-Layers—0
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas—0
Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch—0
A Survey on Large Language Model-empowered Autonomous Driving—0
Loop Neural Networks for Parameter Sharing—0
ShizishanGPT: An Agricultural Large Language Model Integrating Tools and ResourcesCode1
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction—0
LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR—0
Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia—0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-TuningCode0
Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and ManagementCode2
Aligning Language Models Using Follow-up Likelihood as Reward SignalCode0
One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP TasksCode1
CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data—0
Measuring Copyright Risks of Large Language Model via Partial Information ProbingCode0
On-Device Collaborative Language Modeling via a Mixture of Generalists and SpecialistsCode0
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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