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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5251–5300 of 17610 papers

TitleStatusHype
Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language ModelCode1
Large Language Model Watermark Stealing With Mixed Integer Programming—0
LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating MetaheuristicsCode2
Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback—0
Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models—0
Leveraging Open-Source Large Language Models for encoding Social Determinants of Health using an Intelligent Router—0
Efficient Indirect LLM Jailbreak via Multimodal-LLM Jailbreak—0
SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths—0
Conveyor: Efficient Tool-aware LLM Serving with Tool Partial ExecutionCode0
Adaptive In-conversation Team Building for Language Model AgentsCode7
CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive LearningCode1
Posterior Sampling via Autoregressive Generation—0
A Full-duplex Speech Dialogue Scheme Based On Large Language Models—0
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models—0
X-VILA: Cross-Modality Alignment for Large Language Model—0
Multi-Modal Generative Embedding Model—0
Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study—0
Gemini & Physical World: Large Language Models Can Estimate the Intensity of Earthquake Shaking from Multi-Modal Social Media Posts—0
Contextual Position Encoding: Learning to Count What's Important—0
Nearest Neighbor Speculative Decoding for LLM Generation and Attribution—0
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model—0
ChartFormer: A Large Vision Language Model for Converting Chart Images into Tactile Accessible SVGsCode0
Enhancing Vision-Language Model with Unmasked Token AlignmentCode1
LLaMA-Reg: Using LLaMA 2 for Unsupervised Medical Image Registration—0
To FP8 and Back Again: Quantifying Reduced Precision Effects on LLM Training Stability—0
Language Generation with Strictly Proper Scoring RulesCode1
Matryoshka Query Transformer for Large Vision-Language ModelsCode2
Voice Jailbreak Attacks Against GPT-4oCode1
Robust Preference Optimization through Reward Model Distillation—0
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent SpaceCode1
Benchmarking and Improving Detail Image CaptionCode2
Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery—0
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model SeriesCode4
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language ModelsCode2
Kotlin ML Pack: Technical Report—0
Towards a theory of how the structure of language is acquired by deep neural networks—0
Black-Box Detection of Language Model Watermarks—0
Learning diverse attacks on large language models for robust red-teaming and safety tuningCode1
Text-only Synthesis for Image Captioning—0
Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experiments—0
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital TwinsCode1
SLMRec: Distilling Large Language Models into Small for Sequential RecommendationCode1
Automated Real-World Sustainability Data Generation from Images of Buildings—0
XL3M: A Training-free Framework for LLM Length Extension Based on Segment-wise Inference—0
Linguistic Collapse: Neural Collapse in (Large) Language ModelsCode1
Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving?—0
Aligning to Thousands of Preferences via System Message GeneralizationCode2
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented GeneratorCode0
Visual Anchors Are Strong Information Aggregators For Multimodal Large Language ModelCode0
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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