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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5351–5400 of 17610 papers

TitleStatusHype
REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models—0
Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model—0
Remote Sensing Semantic Segmentation Quality Assessment based on Vision Language Model—0
UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge—0
TALKPLAY: Multimodal Music Recommendation with Large Language Models—0
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering—0
Reproducing NevIR: Negation in Neural Information RetrievalCode0
Megrez-Omni Technical Report—0
LLM should think and action as a human—0
Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning—0
What are Models Thinking about? Understanding Large Language Model Hallucinations "Psychology" through Model Inner State Analysis—0
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning—0
User Intent to Use DeepSeek for Healthcare Purposes and their Trust in the Large Language Model: Multinational Survey Study—0
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations—0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders—0
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs—0
BaKlaVa -- Budgeted Allocation of KV cache for Long-context Inference—0
Advanced simulation paradigm of human behaviour unveils complex financial systemic projection—0
Investigating and Extending Homans' Social Exchange Theory with Large Language Model based AgentsCode0
Benchmarking Automatic Speech Recognition coupled LLM Modules for Medical Diagnostics—0
Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on TextCode0
Private Text Generation by Seeding Large Language Model Prompts—0
Learning a High-quality Robotic Wiping Policy Using Systematic Reward Analysis and Visual-Language Model Based Curriculum—0
SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback SystemsCode0
Multilingual Language Model Pretraining using Machine-translated Data—0
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents—0
Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL—0
KL Penalty Control via Perturbation for Direct Preference OptimizationCode0
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding—0
Large Language Models Can Help Mitigate Barren Plateaus—0
TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents—0
SmartLLM: Smart Contract Auditing using Custom Generative AI—0
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model—0
ReviewEval: An Evaluation Framework for AI-Generated Reviews—0
RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration ExemplarsCode0
Learning to Reason at the Frontier of Learnability—0
Personality Structured Interview for Large Language Model Simulation in Personality Research—0
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data—0
Plant in Cupboard, Orange on Rably, Inat Aphone. Benchmarking Incremental Learning of Situation and Language Model using a Text-Simulated Situated Environment—0
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation—0
Unveiling Privacy Risks in LLM Agent Memory—0
ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining—0
Fast or Better? Balancing Accuracy and Cost in Retrieval-Augmented Generation with Flexible User ControlCode0
Competing LLM Agents in a Non-Cooperative Game of Opinion Polarisation—0
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder—0
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion—0
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs—0
Aligning Sentence Simplification with ESL Learner's Proficiency for Language AcquisitionCode0
DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with Model Editing—0
Connecting Large Language Model Agent to High Performance Computing Resource—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