SOTAVerified

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Showing 1280112850 of 17610 papers

TitleStatusHype
Deep Neural Representations for Multiword Expressions DetectionCode0
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization0
Domain-specific knowledge distillation yields smaller and better models for conversational commerce0
Composing Structure-Aware Batches for Pairwise Sentence ClassificationCode0
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level0
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction0
Answer-level Calibration for Free-form Multiple Choice Question AnsweringCode0
A Knowledge storage and semantic space alignment Method for Multi-documents dialogue generation0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification0
CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders0
Adaptive Differential Privacy for Language Model Training0
Cross-Lingual UMLS Named Entity Linking using UMLS Dictionary Fine-TuningCode0
Combining Extraction and Generation for Constructing Belief-Consequence Causal Links0
Graph-combined Coreference Resolution Methods on Conversational Machine Reading Comprehension with Pre-trained Language Model0
Cue-bot: A Conversational Agent for Assistive Technology0
Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking0
ANNA”:" Enhanced Language Representation for Question Answering0
DS-TOD: Efficient Domain Specialization for Task-Oriented DialogCode0
Debiasing Pre-Trained Language Models via Efficient Fine-TuningCode0
ChipSong: A Controllable Lyric Generation System for Chinese Popular SongCode0
Cross-Modal Cloze Task: A New Task to Brain-to-Word DecodingCode0
Improving Controllable Text Generation with Position-Aware Weighted Decoding0
Dataset Debt in Biomedical Language Modeling0
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task0
Challenges in including extra-linguistic context in pre-trained language models0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Domain Knowledge Transferring for Pre-trained Language Model via Calibrated Activation Boundary DistillationCode0
A Holistic Assessment of the Carbon Footprint of Noor, a Very Large Arabic Language Model0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation0
Using Cross-Lingual Part of Speech Tagging for Partially Reconstructing the Classic Language Family Tree Model0
Using ASR-Generated Text for Spoken Language Modeling0
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings0
Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 20200
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks0
Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of DataCode0
Multi-Granularity Structural Knowledge Distillation for Language Model CompressionCode0
Syntax-guided Contrastive Learning for Pre-trained Language Model0
KIQA: Knowledge-Infused Question Answering Model for Financial Table-Text Data0
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 20220
Stylistic Response Generation by Controlling Personality Traits and IntentCode0
Phrase-aware Unsupervised Constituency Parsing0
SSN_ARMM@ LT-EDI -ACL2022: Hope Speech Detection for Equality, Diversity, and Inclusion Using ALBERT model0
Tagging Without Rewriting: A Probabilistic Model for Unpaired Sentiment and Style Transfer0
The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada0
Understanding BERT’s Mood: The Role of Contextual-Embeddings as User-Representations for Depression Assessment0
Query Generation with External Knowledge for Dense Retrieval0
Large-Scale Multi-Document Summarization with Information Extraction and Compression0
MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation0
Language Models as Context-sensitive Word Search EnginesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Decay RNNValidation perplexity76.67Unverified
2GRUValidation perplexity53.78Unverified
3LSTMValidation perplexity52.73Unverified
4LSTMTest perplexity48.7Unverified
5Temporal CNNTest perplexity45.2Unverified
6TCNTest perplexity45.19Unverified
7GCNN-8Test perplexity44.9Unverified
8Neural cache model (size = 100)Test perplexity44.8Unverified
9Neural cache model (size = 2,000)Test perplexity40.8Unverified
10GPT-2 SmallTest perplexity37.5Unverified
#ModelMetricClaimedVerifiedStatus
1TCNTest perplexity108.47Unverified
2Seq-U-NetTest perplexity107.95Unverified
3GRU (Bai et al., 2018)Test perplexity92.48Unverified
4R-TransformerTest perplexity84.38Unverified
5Zaremba et al. (2014) - LSTM (medium)Test perplexity82.7Unverified
6Gal & Ghahramani (2016) - Variational LSTM (medium)Test perplexity79.7Unverified
7LSTM (Bai et al., 2018)Test perplexity78.93Unverified
8Zaremba et al. (2014) - LSTM (large)Test perplexity78.4Unverified
9Gal & Ghahramani (2016) - Variational LSTM (large)Test perplexity75.2Unverified
10Inan et al. (2016) - Variational RHNTest perplexity66Unverified
#ModelMetricClaimedVerifiedStatus
1LSTM (7 layers)Bit per Character (BPC)1.67Unverified
2HypernetworksBit per Character (BPC)1.34Unverified
3SHA-LSTM (4 layers, h=1024, no attention head)Bit per Character (BPC)1.33Unverified
4LN HM-LSTMBit per Character (BPC)1.32Unverified
5ByteNetBit per Character (BPC)1.31Unverified
6Recurrent Highway NetworksBit per Character (BPC)1.27Unverified
7Large FS-LSTM-4Bit per Character (BPC)1.25Unverified
8Large mLSTMBit per Character (BPC)1.24Unverified
9AWD-LSTM (3 layers)Bit per Character (BPC)1.23Unverified
10Cluster-Former (#C=512)Bit per Character (BPC)1.22Unverified
#ModelMetricClaimedVerifiedStatus
1Smaller Transformer 126M (pre-trained)Test perplexity33Unverified
2OPT 125MTest perplexity32.26Unverified
3Larger Transformer 771M (pre-trained)Test perplexity28.1Unverified
4OPT 1.3BTest perplexity19.55Unverified
5GPT-Neo 125MTest perplexity17.83Unverified
6OPT 2.7BTest perplexity17.81Unverified
7Smaller Transformer 126M (fine-tuned)Test perplexity12Unverified
8GPT-Neo 1.3BTest perplexity11.46Unverified
9Transformer 125MTest perplexity10.7Unverified
10GPT-Neo 2.7BTest perplexity10.44Unverified