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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1001–1050 of 17610 papers

TitleStatusHype
BioAtt: Anatomical Prior Driven Low-Dose CT Denoising—0
LLM-mediated Dynamic Plan Generation with a Multi-Agent Approach—0
Representation Bending for Large Language Model SafetyCode1
When Persuasion Overrides Truth in Multi-Agent LLM Debates: Introducing a Confidence-Weighted Persuasion Override Rate (CW-POR)—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
Command A: An Enterprise-Ready Large Language Model—0
Multi-Token Attention—0
VerifiAgent: a Unified Verification Agent in Language Model ReasoningCode0
Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models—0
4th PVUW MeViS 3rd Place Report: Sa2VACode5
ShieldGemma 2: Robust and Tractable Image Content Moderation—0
Automated detection of atomicity violations in large-scale systems—0
CrowdVLM-R1: Expanding R1 Ability to Vision Language Model for Crowd Counting using Fuzzy Group Relative Policy RewardCode1
WebMap -- Large Language Model-assisted Semantic Link Induction in the Web—0
Aud-Sur: An Audio Analyzer Assistant for Audio Surveillance Applications—0
Rethinking Key-Value Cache Compression Techniques for Large Language Model ServingCode1
Orchestrate Multimodal Data with Batch Post-Balancing to Accelerate Multimodal Large Language Model Training—0
HumanAesExpert: Advancing a Multi-Modality Foundation Model for Human Image Aesthetic Assessment—0
AirCache: Activating Inter-modal Relevancy KV Cache Compression for Efficient Large Vision-Language Model Inference—0
Evolutionary Prompt Optimization Discovers Emergent Multimodal Reasoning Strategies in Vision-Language Models—0
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model InferenceCode0
Carbon Footprint Evaluation of Code Generation through LLM as a Service—0
Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework—0
Order Independence With Finetuning—0
Simple Feedfoward Neural Networks are Almost All You Need for Time Series Forecasting—0
ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Model—0
DAT: Dynamic Alpha Tuning for Hybrid Retrieval in Retrieval-Augmented Generation—0
Sparse Mixture of Experts as Unified Competitive Learning—0
Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense RetrievalCode1
Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language ModelsCode0
Fast Training of Recurrent Neural Networks with Stationary State FeedbacksCode0
Factored Agents: Decoupling In-Context Learning and Memorization for Robust Tool Use—0
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning—0
Token-Driven GammaTune: Adaptive Calibration for Enhanced Speculative Decoding—0
Generalization Bias in Large Language Model Summarization of Scientific Research—0
Celler:A Genomic Language Model for Long-Tailed Single-Cell AnnotationCode0
Resona: Improving Context Copying in Linear Recurrence Models with Retrieval—0
Negation: A Pink Elephant in the Large Language Models' Room?—0
Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankersCode0
Long-Tail Crisis in Nearest Neighbor Language Models—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
Unicorn: Text-Only Data Synthesis for Vision Language Model TrainingCode2
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
PharmAgents: Building a Virtual Pharma with Large Language Model Agents—0
Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation—0
Boosting Large Language Models with Mask Fine-TuningCode0
RedditESS: A Mental Health Social Support Interaction Dataset -- Understanding Effective Social Support to Refine AI-Driven Support Tools—0
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction—0
OpenHuEval: Evaluating Large Language Model on Hungarian SpecificsCode1
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness—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