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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1501–1550 of 17610 papers

TitleStatusHype
Echo: A Large Language Model with Temporal Episodic Memory—0
IPO: Your Language Model is Secretly a Preference ClassifierCode0
Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents—0
Dynamic Parallel Tree Search for Efficient LLM Reasoning—0
Coherency Improved Explainable Recommendation via Large Language Model—0
A Training-free LLM-based Approach to General Chinese Character Error CorrectionCode2
MOVE: A Mixture-of-Vision-Encoders Approach for Domain-Focused Vision-Language Processing—0
DReSD: Dense Retrieval for Speculative Decoding—0
A general language model for peptide identificationCode0
Generalizing From Short to Long: Effective Data Synthesis for Long-Context Instruction TuningCode0
ARS: Automatic Routing Solver with Large Language ModelsCode1
Tight Clusters Make Specialized ExpertsCode0
Machine-generated text detection prevents language model collapseCode0
Bridging vision language model (VLM) evaluation gaps with a framework for scalable and cost-effective benchmark generation—0
Chitrarth: Bridging Vision and Language for a Billion People—0
Forecasting Frontier Language Model Agent Capabilities—0
R^3Mem: Bridging Memory Retention and Retrieval via Reversible Compression—0
Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models—0
Enhancing RWKV-based Language Models for Long-Sequence Text GenerationCode0
Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses—0
Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience InterviewsCode0
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable ExplanationsCode0
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System—0
LEDD: Large Language Model-Empowered Data Discovery in Data Lakes—0
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model TrainingCode0
ESPnet-SpeechLM: An Open Speech Language Model Toolkit—0
Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language ModelsCode1
Optimizing Singular Spectrum for Large Language Model Compression—0
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness—0
Rapid Word Learning Through Meta In-Context Learning—0
Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generationCode0
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton OperatorsCode2
LabTOP: A Unified Model for Lab Test Outcome Prediction on Electronic Health RecordsCode1
Prompt-to-LeaderboardCode3
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling—0
HPS: Hard Preference Sampling for Human Preference Alignment—0
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language ModelsCode1
STeCa: Step-level Trajectory Calibration for LLM Agent LearningCode1
Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic SimilarityCode0
SR-LLM: Rethinking the Structured Representation in Large Language Model—0
Exploring Advanced Techniques for Visual Question Answering: A Comprehensive Comparison—0
Megrez-Omni Technical Report—0
Collaborative Retrieval for Large Language Model-based Conversational Recommender SystemsCode1
Slamming: Training a Speech Language Model on One GPU in a DayCode3
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning with Large Vision and Language Models—0
Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrieval—0
UniKnow: A Unified Framework for Reliable Language Model Behavior across Parametric and External Knowledge—0
TESS 2: A Large-Scale Generalist Diffusion Language ModelCode2
Retrieving Versus Understanding Extractive Evidence in Few-Shot Learning—0
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder—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