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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8951–9000 of 17610 papers

TitleStatusHype
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
Evaluating Deep Learning Approaches for Covid19 Fake News Detection—0
Evaluating distributed word representations for capturing semantics of biomedical concepts—0
Evaluating Distributional Distortion in Neural Language Modeling—0
Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks—0
Evaluating Generative Patent Language Models—0
Evaluating GPT-4 with Vision on Detection of Radiological Findings on Chest Radiographs—0
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness—0
Evaluating Language-Model Agents on Realistic Autonomous Tasks—0
Evaluating Large Language Model Capabilities in Assessing Spatial Econometrics Research—0
Evaluating Large Language Model Capability in Vietnamese Fact-Checking Data Generation—0
Evaluating Large Language Model Creativity from a Literary Perspective—0
Evaluating LLaMA 3.2 for Software Vulnerability Detection—0
Evaluating morphological typology in zero-shot cross-lingual transfer—0
Evaluating Nuanced Bias in Large Language Model Free Response Answers—0
Evaluating Persian Tokenizers—0
Evaluating Pragmatic Abilities of Image Captioners on A3DS—0
Evaluating Pre-Trained Language Models for Focused Terminology Extraction from Swedish Medical Records—0
Evaluating Pretraining Strategies for Clinical BERT Models—0
Evaluating Self-Generated Documents for Enhancing Retrieval-Augmented Generation with Large Language Models—0
Evaluating Semantic Rationality of a Sentence: A Sememe-Word-Matching Neural Network based on HowNet—0
Evaluating Steering Techniques using Human Similarity Judgments—0
Evaluating Text Creativity across Diverse Domains: A Dataset and Large Language Model Evaluator—0
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model—0
Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks—0
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning—0
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing—0
Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi'kmaq Language Modelling—0
Evaluating the Impact of Using a Domain-specific Bilingual Lexicon on the Performance of a Hybrid Machine Translation Approach—0
Evaluating the performance of state-of-the-art esg domain-specific pre-trained large language models in text classification against existing models and traditional machine learning techniques—0
Measuring the Quality of Answers in Political Q&As with Large Language Models—0
Evaluating Transformer-Based Multilingual Text Classification—0
Evaluating the Text-to-SQL Capabilities of Large Language Models—0
Evaluating the Text-to-SQL Capabilities of Large Language Models—0
Evaluating Unsupervised Approaches to Morphological Segmentation for Wolastoqey—0
Evaluating Unsupervised Language Model Adaptation Methods for Speaking Assessment—0
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric—0
Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languages—0
Leveraging VLM-Based Pipelines to Annotate 3D Objects—0
Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks—0
Evaluation of AI Chatbots for Patient-Specific EHR Questions—0
Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks—0
Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers—0
Evaluation of Finite State Morphological Analyzers Based on Paradigm Extraction from Wiktionary—0
Evaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark—0
Evaluation of Morphological Embeddings for English and Russian Languages—0
Evaluation of the Automated Labeling Method for Taxonomic Nomenclature Through Prompt-Optimized Large Language Model—0
Evaluation of Transfer Learning for Adverse Drug Event (ADE) and Medication Entity Extraction—0
EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models—0
Event-Centered Information Retrieval Using Kernels on Event Graphs—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