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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 2751–2800 of 17610 papers

TitleStatusHype
Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model—0
Recycled Attention: Efficient inference for long-context language modelsCode0
LBPE: Long-token-first Tokenization to Improve Large Language Models—0
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning—0
End-to-End Navigation with Vision Language Models: Transforming Spatial Reasoning into Question-AnsweringCode2
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
A Two-Step Concept-Based Approach for Enhanced Interpretability and Trust in Skin Lesion DiagnosisCode0
Real-World Offline Reinforcement Learning from Vision Language Model Feedback—0
AgentOps: Enabling Observability of LLM Agents—0
Aioli: A Unified Optimization Framework for Language Model Data MixingCode1
Watermarking Language Models through Language Models—0
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein EngineeringCode1
SuffixDecoding: Extreme Speculative Decoding for Emerging AI ApplicationsCode3
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos—0
LLM2CLIP: Powerful Language Model Unlocks Richer Visual RepresentationCode4
DELIFT: Data Efficient Language model Instruction Fine TuningCode1
Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language ModelCode0
CUIfy the XR: An Open-Source Package to Embed LLM-powered Conversational Agents in XR—0
PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-trainingCode2
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model—0
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models—0
When Does Classical Chinese Help? Quantifying Cross-Lingual Transfer in Hanja and KanbunCode0
Scaling Laws for Pre-training Agents and World Models—0
BendVLM: Test-Time Debiasing of Vision-Language EmbeddingsCode0
Benchmarking Large Language Models with Integer Sequence Generation Tasks—0
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction—0
Large Generative Model-assisted Talking-face Semantic Communication System—0
The N-Grammys: Accelerating Autoregressive Inference with Learning-Free Batched Speculation—0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA—0
Deploying Multi-task Online Server with Large Language Model—0
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-AwarenessCode0
Unified Pathological Speech Analysis with Prompt Tuning—0
AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution—0
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity DatasetCode1
ChatGPT in Research and Education: Exploring Benefits and Threats—0
HumanVLM: Foundation for Human-Scene Vision-Language Model—0
Spontaneous Emergence of Agent Individuality through Social Interactions in LLM-Based Communities—0
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI—0
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status—0
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning—0
PersianRAG: A Retrieval-Augmented Generation System for Persian Language—0
V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference OptimizationCode2
The Evolution of RWKV: Advancements in Efficient Language Modeling—0
AVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis—0
Zebra-Llama: A Context-Aware Large Language Model for Democratizing Rare Disease KnowledgeCode1
TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for NetworkCode1
Wave Network: An Ultra-Small Language Model—0
GraphVL: Graph-Enhanced Semantic Modeling via Vision-Language Models for Generalized Class Discovery—0
ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model—0
KptLLM: Unveiling the Power of Large Language Model for Keypoint Comprehension—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