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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6051–6100 of 17610 papers

TitleStatusHype
Establishing Task Scaling Laws via Compute-Efficient Model Ladders—0
EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios—0
ALMA: Alignment with Minimal Annotation—0
Aligned Music Notation and Lyrics TranscriptionCode0
LL-ICM: Image Compression for Low-level Machine Vision via Large Vision-Language Model—0
Understanding Hidden Computations in Chain-of-Thought ReasoningCode0
Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challenges—0
PERL: Pinyin Enhanced Rephrasing Language Model for Chinese ASR N-best Error Correction—0
Training-Free Mitigation of Language Reasoning Degradation After Multimodal Instruction Tuning—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis—0
Video LLMs for Temporal Reasoning in Long Videos—0
Intent-driven In-context Learning for Few-shot Dialogue State Tracking—0
FANAL -- Financial Activity News Alerting Language Modeling Framework—0
Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies—0
Fine-Grained Behavior Simulation with Role-Playing Large Language Model on Social MediaCode0
From Language Models over Tokens to Language Models over Characters—0
A surprisal oracle for when every layer countsCode0
Benchmarking Harmonized Tariff Schedule Classification Models—0
AntLM: Bridging Causal and Masked Language Models—0
Controlling the Mutation in Large Language Models for the Efficient Evolution of Algorithms—0
Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset—0
Enhancing Trust in Large Language Models with Uncertainty-Aware Fine-Tuning—0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models—0
Flattering to Deceive: The Impact of Sycophantic Behavior on User Trust in Large Language Model—0
An Evolutionary Large Language Model for Hallucination Mitigation—0
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?—0
Explainable CTR Prediction via LLM Reasoning—0
MRP-LLM: Multitask Reflective Large Language Models for Privacy-Preserving Next POI Recommendation—0
Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models—0
Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language ModelCode0
Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training—0
MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News Headlines—0
WSI-LLaVA: A Multimodal Large Language Model for Whole Slide Image—0
Unlocking Video-LLM via Agent-of-Thoughts Distillation—0
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking—0
Improved Large Language Model Jailbreak Detection via Pretrained Embeddings—0
FD-LLM: Large Language Model for Fault Diagnosis of Machines—0
Enhancing Perception Capabilities of Multimodal LLMs with Training-Free Fusion—0
FlexSP: Accelerating Large Language Model Training via Flexible Sequence Parallelism—0
First numerical observation of the Berezinskii-Kosterlitz-Thouless transition in language models—0
Automated Extraction of Acronym-Expansion Pairs from Scientific Papers—0
SeqAfford: Sequential 3D Affordance Reasoning via Multimodal Large Language Model—0
The Vulnerability of Language Model Benchmarks: Do They Accurately Reflect True LLM Performance?—0
RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model AccuracyCode0
MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models—0
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks—0
Medchain: Bridging the Gap Between LLM Agents and Clinical Practice through Interactive Sequential Benchmarking—0
Query Performance Explanation through Large Language Model for HTAP Systems—0
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry—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