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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6601–6650 of 17610 papers

TitleStatusHype
De-jargonizing Science for Journalists with GPT-4: A Pilot StudyCode0
A Survey of Low-shot Vision-Language Model Adaptation via Representer Theorem—0
EmotionCaps: Enhancing Audio Captioning Through Emotion-Augmented Data Generation—0
RATE: Causal Explainability of Reward Models with Imperfect CounterfactualsCode0
Retrieval Augmented Spelling Correction for E-Commerce Applications—0
Leveraging LLM Embeddings for Cross Dataset Label Alignment and Zero Shot Music Emotion PredictionCode0
Synthetic Interlocutors. Experiments with Generative AI to Prolong Ethnographic Encounters—0
Sabiá-3 Technical Report—0
O-Edit: Orthogonal Subspace Editing for Language Model Sequential Editing—0
The Moral Case for Using Language Model Agents for Recommendation—0
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router—0
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices—0
Pixology: Probing the Linguistic and Visual Capabilities of Pixel-based Language ModelsCode0
Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models—0
SHAKTI: A 2.5 Billion Parameter Small Language Model Optimized for Edge AI and Low-Resource Environments—0
SGEdit: Bridging LLM with Text2Image Generative Model for Scene Graph-based Image Editing—0
MoChat: Joints-Grouped Spatio-Temporal Grounding LLM for Multi-Turn Motion Comprehension and Description—0
The Fair Language Model Paradox—0
Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothing—0
ATTNChecker: Highly-Optimized Fault Tolerant Attention for Large Language Model Training—0
PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model—0
Sequential LLM Framework for Fashion Recommendation—0
Tokenization and Morphology in Multilingual Language Models: A Comparative Analysis of mT5 and ByT5—0
LargePiG: Your Large Language Model is Secretly a Pointer Generator—0
Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development—0
Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning—0
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets—0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
Character-aware audio-visual subtitling in context—0
Fine-tuning the ESM2 protein language model to understand the functional impact of missense variantsCode0
ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization—0
A Simple Baseline for Predicting Events with Auto-Regressive Tabular TransformersCode0
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing—0
LG-CAV: Train Any Concept Activation Vector with Language GuidanceCode0
LOBG:Less Overfitting for Better Generalization in Vision-Language Model—0
Recipe for Zero-shot POS Tagging: Is It Useful in Realistic Scenarios?—0
Large Language Model Evaluation via Matrix Nuclear-NormCode0
Model-based Large Language Model Customization as Service—0
Skill Learning Using Process Mining for Large Language Model Plan Generation—0
Local and Global Decoding in Text GenerationCode0
Language Model Preference Evaluation with Multiple Weak EvaluatorsCode0
Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies—0
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries—0
Learning to Ground VLMs without Forgetting—0
LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering—0
MoIN: Mixture of Introvert Experts to Upcycle an LLM—0
Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization—0
MisinfoEval: Generative AI in the Era of "Alternative Facts"—0
Collu-Bench: A Benchmark for Predicting Language Model Hallucinations in Code—0
Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles—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