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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 451–500 of 17610 papers

TitleStatusHype
Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models—0
LaViDa: A Large Diffusion Language Model for Multimodal UnderstandingCode3
SATURN: SAT-based Reinforcement Learning to Unleash Language Model ReasoningCode0
Large Language Model-Empowered Interactive Load Forecasting—0
CASTILLO: Characterizing Response Length Distributions of Large Language ModelsCode0
PaTH Attention: Position Encoding via Accumulating Householder Transformations—0
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation—0
EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human ActionsCode0
LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning—0
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance—0
TensorAR: Refinement is All You Need in Autoregressive Image Generation—0
Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question AnsweringCode0
Latent Principle Discovery for Language Model Self-Improvement—0
Power-Law Decay Loss for Large Language Model Finetuning: Focusing on Information Sparsity to Enhance Generation QualityCode0
Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel DecodingCode2
Incremental Sequence Classification with Temporal Consistency—0
Plan and Budget: Effective and Efficient Test-Time Scaling on Large Language Model Reasoning—0
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial OptimizationCode1
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning—0
Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks—0
CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine—0
MM-MovieDubber: Towards Multi-Modal Learning for Multi-Modal Movie Dubbing—0
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector—0
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation—0
Aligning Dialogue Agents with Global Feedback via Large Language Model Reward Decomposition—0
Ensembling Sparse Autoencoders—0
ViQAgent: Zero-Shot Video Question Answering via Agent with Open-Vocabulary Grounding ValidationCode0
Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question AnsweringCode0
Internal and External Impacts of Natural Language Processing Papers—0
Diagnosing our datasets: How does my language model learn clinical information?Code0
MIKU-PAL: An Automated and Standardized Multi-Modal Method for Speech Paralinguistic and Affect Labeling—0
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective—0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—0
Segmentation-Variant Codebooks for Preservation of Paralinguistic and Prosodic Information—0
Likelihood Variance as Text Importance for Resampling Texts to Map Language Models—0
Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model—0
LyapLock: Bounded Knowledge Preservation in Sequential Large Language Model EditingCode0
Leveraging Online Data to Enhance Medical Knowledge in a Small Persian Language ModelCode0
Revealing Language Model Trajectories via Kullback-Leibler Divergence—0
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic SystemCode0
Self-GIVE: Associative Thinking from Limited Structured Knowledge for Enhanced Large Language Model Reasoning—0
Short-Range Dependency Effects on Transformer Instability and a Decomposed Attention Solution—0
Listen to the Context: Towards Faithful Large Language Models for Retrieval Augmented Generation on Climate Questions—0
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response TheoryCode0
Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering—0
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech TranslationCode0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning—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