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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

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Showing 11351–11400 of 17610 papers

TitleStatusHype
Dr.ICL: Demonstration-Retrieved In-context Learning—0
AxomiyaBERTa: A Phonologically-aware Transformer Model for AssameseCode0
Cascaded Beam Search: Plug-and-Play Terminology-Forcing For Neural Machine Translation—0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
When your Cousin has the Right Connections: Unsupervised Bilingual Lexicon Induction for Related Data-Imbalanced LanguagesCode0
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language TasksCode0
Domain Private Transformers for Multi-Domain Dialog SystemsCode0
From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding—0
Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding—0
Error Detection for Text-to-SQL Semantic ParsingCode0
When the Music Stops: Tip-of-the-Tongue Retrieval for MusicCode0
Learning from Mistakes via Cooperative Study Assistant for Large Language ModelsCode0
Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain—0
Mitigating Test-Time Bias for Fair Image RetrievalCode0
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language ModelsCode0
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model—0
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization—0
Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger DetectionCode0
Robust Prompt Optimization for Large Language Models Against Distribution Shifts—0
On Robustness of Finetuned Transformer-based NLP ModelsCode0
Language Model Self-improvement by Reinforcement Learning Contemplation—0
R2H: Building Multimodal Navigation Helpers that Respond to Help Requests—0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
Natural Language Decompositions of Implicit Content Enable Better Text RepresentationsCode0
Query Rewriting for Retrieval-Augmented Large Language Models—0
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings—0
Towards Unsupervised Recognition of Token-level Semantic Differences in Related DocumentsCode0
Text-based Person Search without Parallel Image-Text Data—0
LMGQS: A Large-scale Dataset for Query-focused Summarization—0
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language ExplanationsCode0
PrOnto: Language Model Evaluations for 859 LanguagesCode0
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefixes—0
The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis—0
Observations on LLMs for Telecom Domain: Capabilities and Limitations—0
Bidirectional Transformer Reranker for Grammatical Error CorrectionCode0
Enhance Reasoning Ability of Visual-Language Models via Large Language Models—0
ConQueR: Contextualized Query Reduction using Search LogsCode0
Explaining Emergent In-Context Learning as Kernel Regression—0
GPT-SW3: An Autoregressive Language Model for the Nordic Languages—0
Can LLMs facilitate interpretation of pre-trained language models?—0
Extrapolating Multilingual Understanding Models as Multilingual Generators—0
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language ModelCode0
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation—0
Evaluating Pragmatic Abilities of Image Captioners on A3DS—0
Federated Learning of Medical Concepts Embedding using BEHRTCode0
Distilling ChatGPT for Explainable Automated Student Answer AssessmentCode0
A Pilot Study on Dialogue-Level Dependency Parsing for Chinese—0
Augmenting Autotelic Agents with Large Language Models—0
Direct Fact Retrieval from Knowledge Graphs without Entity Linking—0
DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection—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