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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6951–7000 of 17610 papers

TitleStatusHype
Multi-Modal Generative AI: Multi-modal LLM, Diffusion and Beyond—0
RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning—0
Target-Aware Language Modeling via Granular Data Sampling—0
Location is Key: Leveraging Large Language Model for Functional Bug Localization in Verilog—0
VLMine: Long-Tail Data Mining with Vision Language Models—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
Backtracking Improves Generation Safety—0
ECHO: Environmental Sound Classification with Hierarchical Ontology-guided Semi-Supervised Learning—0
Can Language Model Understand Word Semantics as A Chatbot? An Empirical Study of Language Model Internal External Mismatch—0
Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas—0
Probing Context Localization of Polysemous Words in Pre-trained Language Model Sub-Layers—0
Data-centric NLP Backdoor Defense from the Lens of Memorization—0
A Survey on Large Language Model-empowered Autonomous Driving—0
Test Time Learning for Time Series Forecasting—0
Loop Neural Networks for Parameter Sharing—0
OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching—0
LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR—0
On-Device Collaborative Language Modeling via a Mixture of Generalists and SpecialistsCode0
Measuring Copyright Risks of Large Language Model via Partial Information ProbingCode0
Prompting Large Language Models for Supporting the Differential Diagnosis of Anemia—0
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction—0
CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data—0
Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-TuningCode0
Exploring Scaling Laws for Local SGD in Large Language Model Training—0
Aligning Language Models Using Follow-up Likelihood as Reward SignalCode0
CLAIR-A: Leveraging Large Language Models to Judge Audio CaptionsCode0
Incremental and Data-Efficient Concept Formation to Support Masked Word Prediction—0
FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists—0
Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization—0
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries—0
Small Language Models are Equation Reasoners—0
LARE: Latent Augmentation using Regional Embedding with Vision-Language Model—0
Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease DetectionCode0
LLMR: Knowledge Distillation with a Large Language Model-Induced Reward—0
KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning—0
Preference Alignment Improves Language Model-Based TTS—0
PersonaFlow: Boosting Research Ideation with LLM-Simulated Expert Personas—0
Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference—0
Takin: A Cohort of Superior Quality Zero-shot Speech Generation Models—0
RUIE: Retrieval-based Unified Information Extraction using Large Language ModelCode0
The Impact of Element Ordering on LM Agent PerformanceCode0
Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-PlayingCode0
LLMs + Persona-Plug = Personalized LLMs—0
GRIN: GRadient-INformed MoE—0
FLARE: Fusing Language Models and Collaborative Architectures for Recommender Enhancement—0
MeTHanol: Modularized Thinking Language Models with Intermediate Layer Thinking, Decoding and Bootstrapping Reasoning—0
VERA: Validation and Enhancement for Retrieval Augmented systems—0
Semformer: Transformer Language Models with Semantic Planning—0
ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering—0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach—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