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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4651–4700 of 17610 papers

TitleStatusHype
Potential Renovation of Information Search Process with the Power of Large Language Model for Healthcare—0
Self-Translate-Train: Enhancing Cross-Lingual Transfer of Large Language Models via Inherent Capability—0
Financial Knowledge Large Language Model—0
Teola: Towards End-to-End Optimization of LLM-based ApplicationsCode2
Open-Source Conversational AI with SpeechBrain 1.0—0
A Study on Effect of Reference Knowledge Choice in Generating Technical Content Relevant to SAPPhIRE Model Using Large Language Model—0
Answering real-world clinical questions using large language model based systems—0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
Can GPT-4 Help Detect Quit Vaping Intentions? An Exploration of Automatic Data Annotation Approach—0
The Qiyas Benchmark: Measuring ChatGPT Mathematical and Language Understanding in Arabic—0
Simulating Financial Market via Large Language Model based Agents—0
Scaling Synthetic Data Creation with 1,000,000,000 PersonasCode11
Investigating the Timescales of Language Processing with EEG and Language Models—0
YuLan: An Open-source Large Language ModelCode4
MM-Instruct: Generated Visual Instructions for Large Multimodal Model AlignmentCode1
Into the Unknown: Generating Geospatial Descriptions for New EnvironmentsCode0
Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language ModelCode1
Molecular Facts: Desiderata for Decontextualization in LLM Fact VerificationCode0
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything ModelCode3
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
Adaptive Draft-Verification for Efficient Large Language Model Decoding—0
LongLaMP: A Benchmark for Personalized Long-form Text Generation—0
Meta Large Language Model Compiler: Foundation Models of Compiler Optimization—0
LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models—0
PathAlign: A vision-language model for whole slide images in histopathology—0
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors—0
Decoding-Time Language Model Alignment with Multiple ObjectivesCode1
Length Optimization in Conformal PredictionCode0
IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language—0
Zero-shot Composed Image Retrieval Considering Query-target Relationship Leveraging Masked Image-text Pairs—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
Efficacy of Language Model Self-Play in Non-Zero-Sum GamesCode0
RoboUniView: Visual-Language Model with Unified View Representation for Robotic ManipulationCode2
LICO: Large Language Models for In-Context Molecular Optimization—0
Towards Large Language Model Aided Program Refinement—0
Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysisCode1
Themis: A Reference-free NLG Evaluation Language Model with Flexibility and InterpretabilityCode1
Llamipa: An Incremental Discourse Parser—0
Octo-planner: On-device Language Model for Planner-Action Agents—0
Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image TranslationCode1
A Refer-and-Ground Multimodal Large Language Model for BiomedicineCode1
MUMU: Bootstrapping Multimodal Image Generation from Text-to-Image Data—0
BADGE: BADminton report Generation and Evaluation with LLMCode0
MammothModa: Multi-Modal Large Language Model—0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Cascading Large Language Models for Salient Event Graph GenerationCode0
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry—0
S3: A Simple Strong Sample-effective Multimodal Dialog SystemCode0
The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data AnnotatorsCode1
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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