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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 51–100 of 17610 papers

TitleStatusHype
SharpZO: Hybrid Sharpness-Aware Vision Language Model Prompt Tuning via Forward-Only PassesCode0
V2X-REALM: Vision-Language Model-Based Robust End-to-End Cooperative Autonomous Driving with Adaptive Long-Tail Modeling—0
Prompt-Guided Turn-Taking Prediction—0
World-aware Planning Narratives Enhance Large Vision-Language Model Planner—0
SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs—0
AALC: Large Language Model Efficient Reasoning via Adaptive Accuracy-Length ControlCode0
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios—0
Towards Community-Driven Agents for Machine Learning EngineeringCode0
Large Language Model-Driven Code Compliance Checking in Building Information Modeling—0
Automatic Demonstration Selection for LLM-based Tabular Data Classification—0
Language Modeling by Language ModelsCode2
OctoThinker: Mid-training Incentivizes Reinforcement Learning ScalingCode2
Biomed-Enriched: A Biomedical Dataset Enriched with LLMs for Pretraining and Extracting Rare and Hidden Content—0
A Multi-Pass Large Language Model Framework for Precise and Efficient Radiology Report Error DetectionCode0
Enterprise Large Language Model Evaluation Benchmark—0
GPTailor: Large Language Model Pruning Through Layer Cutting and StitchingCode1
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language ModelsCode0
PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries—0
A Large Language Model-based Multi-Agent Framework for Analog Circuits' Sizing Relationships Extraction—0
GradualDiff-Fed: A Federated Learning Specialized Framework for Large Language Model—0
Smart-LLaMA-DPO: Reinforced Large Language Model for Explainable Smart Contract Vulnerability Detection—0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model—0
ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image GenerationCode3
Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation BoosterCode2
Leveraging Large Language Model for Intelligent Log Processing and Autonomous Debugging in Cloud AI Platforms—0
Reflective Verbal Reward Design for Pluralistic Alignment—0
Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?—0
Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems—0
Challenges in Grounding Language in the Real World—0
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems—0
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization—0
LLMs in Coding and their Impact on the Commercial Software Engineering Landscape—0
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling ResearchCode1
Watermarking Autoregressive Image GenerationCode2
From RAG to Agentic: Validating Islamic-Medicine Responses with LLM Agents—0
Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks—0
RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World EnvironmentsCode0
Show-o2: Improved Native Unified Multimodal ModelsCode5
Finance Language Model Evaluation (FLaME)—0
BMFM-RNA: An Open Framework for Building and Evaluating Transcriptomic Foundation ModelsCode2
Thinking in Directivity: Speech Large Language Model for Multi-Talker Directional Speech Recognition—0
Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings—0
Don't Make It Up: Preserving Ignorance Awareness in LLM Fine-Tuning—0
Lightweight Relevance Grader in RAGCode0
From Bytes to Ideas: Language Modeling with Autoregressive U-NetsCode7
DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion—0
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM—0
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue AgentsCode0
Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees—0
Sampling from Your Language Model One Byte at a TimeCode1
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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