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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 9301–9350 of 17610 papers

TitleStatusHype
A Side-by-side Comparison of Transformers for English Implicit Discourse Relation Classification—0
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
S2vNTM: Semi-supervised vMF Neural Topic Modeling—0
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language—0
Vision Language Transformers: A Survey—0
Can ChatGPT's Responses Boost Traditional Natural Language Processing?Code0
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
Agentività e telicità in GilBERTo: implicazioni cognitive—0
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence—0
PRD: Peer Rank and Discussion Improve Large Language Model based EvaluationsCode1
Lost in the Middle: How Language Models Use Long ContextsCode2
RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language ModelsCode0
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical RecordsCode0
Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment—0
Evade ChatGPT Detectors via A Single Space—0
Generative Job Recommendations with Large Language Model—0
Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-TuningCode1
Emoji Prediction in Tweets using BERTCode0
Causal Discovery with Language Models as Imperfect ExpertsCode1
Deductive Additivity for Planning of Natural Language ProofsCode0
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual TasksCode1
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?Code2
Garbage in, garbage out: Zero-shot detection of crime using Large Language ModelsCode0
A Language Model for Grammatical Error Correction in L2 Russian—0
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding—0
On Conditional and Compositional Language Model Differentiable Prompting—0
Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners—0
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases—0
ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis—0
ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience—0
Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking—0
Trainable Transformer in TransformerCode1
Large Language Models Enable Few-Shot ClusteringCode1
GenRec: Large Language Model for Generative RecommendationCode1
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation—0
Conformer LLMs -- Convolution Augmented Large Language Models—0
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information RetrievalCode2
CephGPT-4: An Interactive Multimodal Cephalometric Measurement and Diagnostic System with Visual Large Language Model—0
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained TransformerCode2
How far is Language Model from 100% Few-shot Named Entity Recognition in Medical DomainCode1
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment—0
InstructEval: Systematic Evaluation of Instruction Selection Methods—0
THUIR2 at NTCIR-16 Session Search (SS) Task—0
Provable Robust Watermarking for AI-Generated TextCode2
Biomedical Language Models are Robust to Sub-optimal TokenizationCode0
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot DetectionCode1
Ticket-BERT: Labeling Incident Management Tickets with Language Models—0
Stay on topic with Classifier-Free Guidance—0
Towards Open-Domain Topic Classification—0
HPC-Coder: Modeling Parallel Programs using Large Language Models—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