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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4501–4550 of 17610 papers

TitleStatusHype
CopyBench: Measuring Literal and Non-Literal Reproduction of Copyright-Protected Text in Language Model GenerationCode1
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training—0
CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object DetectionCode1
A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts—0
Chat-Edit-3D: Interactive 3D Scene Editing via Text PromptsCode3
LETS-C: Leveraging Text Embedding for Time Series Classification—0
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language ModelCode2
SOLO: A Single Transformer for Scalable Vision-Language ModelingCode2
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory—0
DebUnc: Improving Large Language Model Agent Communication With Uncertainty MetricsCode1
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing—0
Open-world Multi-label Text Classification with Extremely Weak SupervisionCode1
Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning—0
Large Language Model Recall Uncertainty is Modulated by the Fan EffectCode0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
PsycoLLM: Enhancing LLM for Psychological Understanding and EvaluationCode2
OneDiff: A Generalist Model for Image Difference Captioning—0
On the Power of Convolution Augmented Transformer—0
MST5 -- Multilingual Question Answering over Knowledge GraphsCode0
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation—0
Large Language Models for Judicial Entity Extraction: A Comparative Study—0
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementCode2
GenFollower: Enhancing Car-Following Prediction with Large Language Models—0
Using Grammar Masking to Ensure Syntactic Validity in LLM-based Modeling Tasks—0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian—0
Artificial Intuition: Efficient Classification of Scientific Abstracts—0
E^2CFD: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via Large Language Model—0
On Speeding Up Language Model Evaluation—0
Variational Best-of-N Alignment—0
Just read twice: closing the recall gap for recurrent language modelsCode2
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course—0
Biomedical Nested NER with Large Language Model and UMLS Heuristics—0
CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic TokensCode11
Language Representations Can be What Recommenders Need: Findings and PotentialsCode2
Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System ResponsesCode0
AI Safety in Generative AI Large Language Models: A Survey—0
Large language models are good medical coders, if provided with toolsCode1
SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal GroundingCode0
Leveraging Task-Specific Knowledge from LLM for Semi-Supervised 3D Medical Image Segmentation—0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Aligning Model Evaluations with Human Preferences: Mitigating Token Count Bias in Language Model Assessments—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
Statistical investigations into the geometry and homology of random programs—0
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based SpikingCode1
MobileFlow: A Multimodal LLM For Mobile GUI Agent—0
Crafting Large Language Models for Enhanced InterpretabilityCode1
Testing learning hypotheses using neural networks by manipulating learning data—0
Efficient Controlled Language Generation with Low-Rank Autoregressive Reward Models—0
Written Term Detection Improves Spoken Term DetectionCode0
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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