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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 10551–10600 of 17610 papers

TitleStatusHype
Prompt Tuning for Zero-shot Compositional Learning—0
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases—0
PROMT DeepHybrid system for WMT12 shared translation task—0
PronouncUR: An Urdu Pronunciation Lexicon Generator—0
Pronoun Language Model and Grammatical Heuristics for Aiding Pronoun Prediction—0
Pronoun Prediction with Latent Anaphora Resolution—0
Pronoun Prediction with Linguistic Features and Example Weighing—0
Proof Flow: Preliminary Study on Generative Flow Network Language Model Tuning for Formal Reasoning—0
ProPath: Disease-Specific Protein Language Model for Variant Pathogenicity—0
ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-like Language Models—0
ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering—0
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit—0
ProsperAMnet at FinCausal 2020, Task 1 & 2: Modeling causality in financial texts using multi-headed transformers—0
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses—0
Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL—0
DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction—0
Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence—0
ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Models—0
ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddings—0
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
Protum: A New Method For Prompt Tuning Based on "[MASK]"—0
Provably Confidential Language Modelling—0
Provably Robust Watermarks for Open-Source Language Models—0
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model—0
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation—0
Providing Insights for Open-Response Surveys via End-to-End Context-Aware Clustering—0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
PRP: Propagating Universal Perturbations to Attack Large Language Model Guard-Rails—0
PRS-Med: Position Reasoning Segmentation with Vision-Language Model in Medical Imaging—0
Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning—0
Pseudo-Autoregressive Neural Codec Language Models for Efficient Zero-Shot Text-to-Speech Synthesis—0
Pseudointelligence: A Unifying Framework for Language Model Evaluation—0
Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings—0
Pseudo-Masked Language Models for Unified Language Model Pre-Training—0
Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation—0
PSG: Prompt-based Sequence Generation for Acronym Extraction—0
PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems—0
Psych-E: Configurable Response Generation using Personality Traits and Pragmatics—0
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method—0
PTab: Using the Pre-trained Language Model for Modeling Tabular Data—0
PTR: A Pre-trained Language Model for Trajectory Recovery—0
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks—0
Pulling Out the Stops: Rethinking Stopword Removal for Topic Models—0
PUMGPT: A Large Vision-Language Model for Product Understanding—0
Punctuation Prediction with Transition-based Parsing—0
Punctuation restoration in Swedish through fine-tuned KB-BERT—0
Purely sequence-trained neural networks for ASR based on lattice-free MMI—0
Purifying Large Language Models by Ensembling a Small Language Model—0
Purple Llama CyberSecEval: A Secure Coding Benchmark for 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