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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6801–6850 of 17610 papers

TitleStatusHype
Boosting Lossless Speculative Decoding via Feature Sampling and Partial Alignment Distillation—0
Boosting Medical Image-based Cancer Detection via Text-guided Supervision from Reports—0
RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment—0
Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation—0
Boosting Unsupervised Machine Translation with Pseudo-Parallel Data—0
Bootstrapping a Unified Model of Lexical and Phonetic Acquisition—0
Bootstrapping Cognitive Agents with a Large Language Model—0
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration—0
Bootstrapping Text Anonymization Models with Distant Supervision—0
Bootstrap Your Own Context Length—0
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance—0
Borges and AI—0
BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline—0
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle—0
Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts—0
BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue Generation—0
BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning—0
BPoMP: The Benchmark of Poetic Minimal Pairs – Limericks, Rhyme, and Narrative Coherence—0
BrAIcht, a theatrical agent that speaks like Bertolt Brecht's characters—0
Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings—0
BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback—0
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected—0
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity—0
BrainTransformers: SNN-LLM—0
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation—0
BRAVE: Broadening the visual encoding of vision-language models—0
BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges—0
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification—0
Breaking Chains: Unraveling the Links in Multi-Hop Knowledge Unlearning—0
Breaking Character: Are Subwords Good Enough for MRLs After All?—0
Breaking Character: Are Subwords Good Enough for MRLs After All?—0
Breaking Down Word Semantics from Pre-trained Language Models through Layer-wise Dimension Selection—0
Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph—0
Breaking the Encoder Barrier for Seamless Video-Language Understanding—0
Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decoupling—0
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities—0
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models—0
Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm—0
Breeze-7B Technical Report—0
BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights—0
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
Bridge the Gap between Language models and Tabular Understanding—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing—0
Bridging Dictionary: AI-Generated Dictionary of Partisan Language Use—0
Bridging HMMs and RNNs through Architectural Transformations—0
Bridging Large Language Models and Single-Cell Transcriptomics in Dissecting Selective Motor Neuron Vulnerability—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling—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