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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7751–7800 of 17610 papers

TitleStatusHype
An Empirical Comparison of Generative Approaches for Product Attribute-Value IdentificationCode0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization—0
First Place Solution of 2023 Global Artificial Intelligence Technology Innovation Competition Track 1—0
FoldGPT: Simple and Effective Large Language Model Compression Scheme—0
Image-to-Text Logic Jailbreak: Your Imagination can Help You Do Anything—0
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving—0
Large Language Model Enhanced Knowledge Representation Learning: A Survey—0
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented GenerationCode0
Large Language Models Are Involuntary Truth-Tellers: Exploiting Fallacy Failure for Jailbreak AttacksCode0
Explaining Length Bias in LLM-Based Preference Evaluations—0
Optimization of Retrieval-Augmented Generation Context with Outlier Detection—0
Memory^3: Language Modeling with Explicit Memory—0
Needle in the Haystack for Memory Based Large Language Models—0
Scaling Technology Acceptance Analysis with Large Language Model (LLM) Annotation Systems—0
Explaining Chest X-ray Pathology Models using Textual Concepts—0
Characterizing Stereotypical Bias from Privacy-preserving Pre-Training—0
Answering real-world clinical questions using large language model based systems—0
Financial Knowledge Large Language Model—0
A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model—0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
Potential Renovation of Information Search Process with the Power of Large Language Model for Healthcare—0
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization—0
Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability—0
Open-Source Conversational AI with SpeechBrain 1.0—0
The Qiyas Benchmark: Measuring ChatGPT Mathematical and Language Understanding in Arabic—0
Molecular Facts: Desiderata for Decontextualization in LLM Fact VerificationCode0
Simulating Financial Market via Large Language Model based Agents—0
Into the Unknown: Generating Geospatial Descriptions for New EnvironmentsCode0
BESTOW: Efficient and Streamable Speech Language Model with the Best of Two Worlds in GPT and T5—0
Designing and Evaluating Multi-Chatbot Interface for Human-AI Communication: Preliminary Findings from a Persuasion Task—0
Investigating the Timescales of Language Processing with EEG and Language Models—0
Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach—0
Adaptive Draft-Verification for Efficient Large Language Model Decoding—0
Efficacy of Language Model Self-Play in Non-Zero-Sum GamesCode0
IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language—0
LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models—0
LICO: Large Language Models for In-Context Molecular Optimization—0
LongLaMP: A Benchmark for Personalized Long-form Text Generation—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
Length Optimization in Conformal PredictionCode0
PathAlign: A vision-language model for whole slide images in histopathology—0
Meta Large Language Model Compiler: Foundation Models of Compiler Optimization—0
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors—0
Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs—0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Cascading Large Language Models for Salient Event Graph GenerationCode0
BADGE: BADminton report Generation and Evaluation with LLMCode0
Towards Large Language Model Aided Program Refinement—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