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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5851–5900 of 17610 papers

TitleStatusHype
KunServe: Efficient Parameter-centric Memory Management for LLM Serving—0
PLD-Tree: Persistent Laplacian Decision Tree for Protein-Protein Binding Free Energy Prediction—0
Zero-resource Speech Translation and Recognition with LLMs—0
VisionLLM-based Multimodal Fusion Network for Glottic Carcinoma Early Detection—0
Scaling Capability in Token Space: An Analysis of Large Vision Language ModelCode0
VITRO: Vocabulary Inversion for Time-series Representation Optimization—0
Deliberation in Latent Space via Differentiable Cache Augmentation—0
Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models—0
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents—0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
GCS-M3VLT: Guided Context Self-Attention based Multi-modal Medical Vision Language Transformer for Retinal Image Captioning—0
Interweaving Memories of a Siamese Large Language ModelCode0
FedTLU: Federated Learning with Targeted Layer Updates—0
BenCzechMark : A Czech-centric Multitask and Multimetric Benchmark for Large Language Models with Duel Scoring Mechanism—0
PepTune: De Novo Generation of Therapeutic Peptides with Multi-Objective-Guided Discrete Diffusion—0
Measuring Contextual Informativeness in Child-Directed TextCode0
Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced RelevanceCode0
Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models—0
SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults—0
DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately—0
Better Think with Tables: Leveraging Tables to Enhance Large Language Model Comprehension—0
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language ModelsCode0
Autoregressive Speech Synthesis with Next-Distribution Prediction—0
FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story VideosCode0
A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation—0
A Reality Check on Context Utilisation for Retrieval-Augmented GenerationCode0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
Application of Multimodal Large Language Models in Autonomous Driving—0
Correcting Large Language Model Behavior via Influence Function—0
Large Language Model Can Be a Foundation for Hidden Rationale-Based RetrievalCode0
Speech Retrieval-Augmented Generation without Automatic Speech Recognition—0
The Task Shield: Enforcing Task Alignment to Defend Against Indirect Prompt Injection in LLM Agents—0
Technical Report: Small Language Model for Japanese Clinical and Medicine—0
Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research—0
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage—0
QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning—0
J-EDI QA: Benchmark for deep-sea organism-specific multimodal LLM—0
PromptOptMe: Error-Aware Prompt Compression for LLM-based MT Evaluation Metrics—0
PolySmart and VIREO @ TRECVid 2024 Ad-hoc Video Search—0
VirusT5: Harnessing Large Language Models to Predicting SARS-CoV-2 EvolutionCode0
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Continual Learning Using Only Large Language Model Prompting—0
Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems—0
Development of a Large-scale Dataset of Chest Computed Tomography Reports in Japanese and a High-performance Finding Classification Model—0
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models—0
Data-Driven Mechanism Design: Jointly Eliciting Preferences and Information—0
Interleaved Speech-Text Language Models are Simple Streaming Text to Speech Synthesizers—0
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning—0
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
Frequency Is What You Need: Word-frequency Masking Benefits Vision-Language Model Pre-trainingCode0
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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