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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4401–4450 of 17610 papers

TitleStatusHype
Skip-Thinking: Chunk-wise Chain-of-Thought Distillation Enable Smaller Language Models to Reason Better and Faster—0
TULUN: Transparent and Adaptable Low-resource Machine TranslationCode0
MSA at BEA 2025 Shared Task: Disagreement-Aware Instruction Tuning for Multi-Dimensional Evaluation of LLMs as Math Tutors—0
Anchored Diffusion Language Model—0
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment—0
EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models—0
Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking—0
Inference Compute-Optimal Video Vision Language Models—0
Building a Functional Machine Translation Corpus for Kpelle—0
Disentangling Knowledge Representations for Large Language Model Editing—0
BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks—0
ELDeR: Getting Efficient LLMs through Data-Driven Regularized Layer-wise Pruning—0
Inference-Time Decomposition of Activations (ITDA): A Scalable Approach to Interpreting Large Language ModelsCode0
Large language model as user daily behavior data generator: balancing population diversity and individual personality—0
Simulating Macroeconomic Expectations using LLM Agents—0
Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification—0
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization—0
Selection Mechanisms for Sequence Modeling using Linear State Space Models—0
SpectraLDS: Provable Distillation for Linear Dynamical Systems—0
keepitsimple at SemEval-2025 Task 3: LLM-Uncertainty based Approach for Multilingual Hallucination Span DetectionCode0
Runaway is Ashamed, But Helpful: On the Early-Exit Behavior of Large Language Model-based Agents in Embodied EnvironmentsCode0
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling—0
Retrieval Augmented Generation-based Large Language Models for Bridging Transportation Cybersecurity Legal Knowledge Gaps—0
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache—0
Taming LLMs with Negative Samples: A Reference-Free Framework to Evaluate Presentation Content with Actionable Feedback—0
LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning—0
Power-Law Decay Loss for Large Language Model Finetuning: Focusing on Information Sparsity to Enhance Generation QualityCode0
TensorAR: Refinement is All You Need in Autoregressive Image Generation—0
PaTH Attention: Position Encoding via Accumulating Householder Transformations—0
Large Language Model-Empowered Interactive Load Forecasting—0
Plan and Budget: Effective and Efficient Test-Time Scaling on Large Language Model Reasoning—0
SATURN: SAT-based Reinforcement Learning to Unleash Language Model ReasoningCode0
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
Latent Principle Discovery for Language Model Self-Improvement—0
Small-to-Large Generalization: Data Influences Models Consistently Across Scale—0
MM-MovieDubber: Towards Multi-Modal Learning for Multi-Modal Movie Dubbing—0
Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models—0
Evaluating Large Language Model with Knowledge Oriented Language Specific Simple Question AnsweringCode0
Incremental Sequence Classification with Temporal Consistency—0
INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling—0
DeepRec: Towards a Deep Dive Into the Item Space with Large Language Model Based Recommendation—0
Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine—0
Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoning—0
CASTILLO: Characterizing Response Length Distributions of Large Language ModelsCode0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance—0
A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLPCode0
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
EMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human ActionsCode0
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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