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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10301–10350 of 17610 papers

TitleStatusHype
Large Language Model Instruction Following: A Survey of Progresses and ChallengesCode2
Can AI-Generated Text be Reliably Detected?Code1
Trained on 100 million words and still in shape: BERT meets British National CorpusCode1
DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing—0
CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web VideosCode1
Towards the Scalable Evaluation of Cooperativeness in Language Models—0
TypeT5: Seq2seq Type Inference using Static AnalysisCode1
Translating Radiology Reports into Plain Language using ChatGPT and GPT-4 with Prompt Learning: Promising Results, Limitations, and Potential—0
Jump to Conclusions: Short-Cutting Transformers With Linear TransformationsCode1
Rethinking Model Ensemble in Transfer-based Adversarial AttacksCode1
SmartBERT: A Promotion of Dynamic Early Exiting Mechanism for Accelerating BERT Inference—0
Logical Implications for Visual Question Answering ConsistencyCode0
LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactionsCode0
ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark—0
DeltaScore: Fine-Grained Story Evaluation with PerturbationsCode0
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!Code1
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs—0
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language DescriptionsCode1
Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers—0
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures—0
Do Transformers Parse while Predicting the Masked Word?—0
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM FamilyCode1
Eliciting Latent Predictions from Transformers with the Tuned LensCode4
AMOM: Adaptive Masking over Masking for Conditional Masked Language ModelCode0
Generating multiple-choice questions for medical question answering with distractors and cue-masking—0
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUCode5
Model-tuning Via Prompts Makes NLP Models Adversarially RobustCode0
ODIN: On-demand Data Formulation to Mitigate Dataset Lock-in—0
A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilityCode1
Accommodating Audio Modality in CLIP for Multimodal ProcessingCode0
Consistency Analysis of ChatGPT—0
Stabilizing Transformer Training by Preventing Attention Entropy CollapseCode2
Learning Combinatorial Prompts for Universal Controllable Image Captioning—0
Susceptibility to Influence of Large Language Models—0
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference—0
Algorithmic Ghost in the Research Shell: Large Language Models and Academic Knowledge Creation in Management Research—0
An Overview on Language Models: Recent Developments and Outlook—0
Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language ModelsCode1
Tag2Text: Guiding Vision-Language Model via Image TaggingCode4
Rewarding Chatbots for Real-World Engagement with Millions of Users—0
Iterative Few-shot Semantic Segmentation from Image Label TextCode1
Knowledge-augmented Few-shot Visual Relation Detection—0
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training—0
Planning with Large Language Models for Code Generation—0
Can a Frozen Pretrained Language Model be used for Zero-shot Neural Retrieval on Entity-centric Questions?—0
Weakly-Supervised HOI Detection from Interaction Labels Only and Language/Vision-Language Priors—0
Magnushammer: A Transformer-Based Approach to Premise Selection—0
Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results—0
Cost-Effective Hyperparameter Optimization for Large Language Model Generation InferenceCode4
German BERT Model for Legal Named Entity Recognition—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