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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 79017950 of 17610 papers

TitleStatusHype
Identifying and interpreting non-aligned human conceptual representations using language modeling0
Identifying and Manipulating the Personality Traits of Language Models0
Identifying and Reducing Gender Bias in Word-Level Language Models0
Identifying Comparable Corpora Using LDA0
Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy0
Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses0
Identifying Humor in Reviews using Background Text Sources0
Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling0
Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating0
Identifying Planetary Names in Astronomy Papers: A Multi-Step Approach0
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing0
Identifying the L1 of non-native writers: the CMU-Haifa system0
I-Design: Personalized LLM Interior Designer0
IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification0
IDPG: An Instance-Dependent Prompt Generation Method0
IDPG: An Instance-Dependent Prompt Generation Method0
"I'd rather just go to bed": Understanding Indirect Answers0
``I'd rather just go to bed'': Understanding Indirect Answers0
IDS at SemEval-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?0
IGA: An Intent-Guided Authoring Assistant0
IG Captioner: Information Gain Captioners are Strong Zero-shot Classifiers0
IGDA: Interactive Graph Discovery through Large Language Model Agents0
Igea: a Decoder-Only Language Model for Biomedical Text Generation in Italian0
Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain0
IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks by Efficient Human Preference Alignment0
IIT Bombay's English-Indonesian submission at WAT: Integrating Neural Language Models with SMT0
IITP English-Hindi Machine Translation System at WAT 20160
I Know What I Don't Know: Improving Model Cascades Through Confidence Tuning0
Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information0
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance0
ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations0
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval0
Image and Data Mining in Reticular Chemistry Using GPT-4V0
CLIPPO: Image-and-Language Understanding from Pixels Only0
Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks0
ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data0
Image BERT Pre-training with Online Tokenizer0
Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks0
Image is All You Need: Towards Efficient and Effective Large Language Model-Based Recommender Systems0
Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective0
Image-Text Relation Prediction for Multilingual Tweets0
Image-to-Text Logic Jailbreak: Your Imagination can Help You Do Anything0
Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems0
Imagine to Hear: Auditory Knowledge Generation can be an Effective Assistant for Language Models0
IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction0
imec-ETRO-VUB at W-NUT 2020 Shared Task-3: A multilabel BERT-based system for predicting COVID-19 events0
Imitating Language via Scalable Inverse Reinforcement Learning0
Immersive Text Game and Personality Classification0
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding0
Impact du degr\'e de supervision sur l'adaptation \`a un domaine d'un mod\`ele de langage \`a partir du Web (Impact of the level of supervision on Web-based language model domain adaptation) [in French]0
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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