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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8501–8550 of 17610 papers

TitleStatusHype
Understanding prompt engineering may not require rethinking generalization—0
Understanding Recurrent Neural Architectures by Analyzing and Synthesizing Long Distance Dependencies in Benchmark Sequential Datasets—0
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment—0
Understanding Semantics from Speech Through Pre-training—0
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples—0
Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models—0
Understanding the Dataset Practitioners Behind Large Language Model Development—0
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention—0
Understanding the Inner Workings of Language Models Through Representation Dissimilarity—0
Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERT—0
Understanding the Logical and Semantic Structure of Large Documents—0
Understanding the Multi-modal Prompts of the Pre-trained Vision-Language Model—0
Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision—0
Understanding the performance gap between online and offline alignment algorithms—0
Understanding Sarcoidosis Using Large Language Models and Social Media Data—0
Understanding the role of FFNs in driving multilingual behaviour in LLMs—0
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology—0
Understanding Token Probability Encoding in Output Embeddings—0
Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation—0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration—0
Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information—0
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition—0
Iterative Translation Refinement with Large Language Models—0
Iterative Value Function Optimization for Guided Decoding—0
"I think this is the most disruptive technology": Exploring Sentiments of ChatGPT Early Adopters using Twitter Data—0
It's About Time: Incorporating Temporality in Retrieval Augmented Language Models—0
"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents—0
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization—0
It’s Basically the Same Language Anyway: the Case for a Nordic Language Model—0
It's High Time: A Survey of Temporal Information Retrieval and Question Answering—0
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization—0
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning—0
IUCL: Combining Information Sources for SemEval Task 5—0
IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation—0
I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection—0
IXA Biomedical Translation System at WMT16 Biomedical Translation Task—0
JABER and SABER: Junior and Senior Arabic BERt—0
Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model—0
Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model—0
JaFIn: Japanese Financial Instruction Dataset—0
Jailbreak and Guard Aligned Language Models with Only Few In-Context Demonstrations—0
Jailbreaking Safeguarded Text-to-Image Models via Large Language Models—0
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding—0
Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2—0
Jamo Pair Encoding: Subcharacter Representation-based Extreme Korean Vocabulary Compression for Efficient Subword Tokenization—0
Japanese Lexical Simplification for Non-Native Speakers—0
Japanese Realistic Textual Entailment Corpus—0
Japanese to English Machine Translation using Preordering and Compositional Distributed Semantics—0
Japanese Zero Anaphora Resolution Can Benefit from Parallel Texts Through Neural Transfer Learning—0
JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts—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