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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6701–6750 of 17610 papers

TitleStatusHype
Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking—0
Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks—0
Beyond Multiple Choice: Evaluating Steering Vectors for Adaptive Free-Form Summarization—0
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey—0
Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models—0
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing—0
Beyond Segmentation: Road Network Generation with Multi-Modal LLMs—0
Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging—0
Beyond Text Compression: Evaluating Tokenizers Across Scales—0
Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform—0
Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks—0
Beyond Turing: A Comparative Analysis of Approaches for Detecting Machine-Generated Text—0
Beyond Visual Understanding: Introducing PARROT-360V for Vision Language Model Benchmarking—0
Beyond Word-based Language Model in Statistical Machine Translation—0
Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis—0
BFClass: A Backdoor-free Text Classification Framework—0
BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models—0
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads—0
Biases in Predicting the Human Language Model—0
Bias Neutralization Framework: Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ)—0
BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy—0
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach—0
Biber Redux: Reconsidering Dimensions of Variation in American English—0
Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR—0
Bidirectional Generative Adversarial Networks for Neural Machine Translation—0
Bidirectional Language Models Are Also Few-shot Learners—0
Bidirectional Long-Short Term Memory for Video Description—0
Bidirectional Representations for Low Resource Spoken Language Understanding—0
Bidirectional Trained Tree-Structured Decoder for Handwritten Mathematical Expression Recognition—0
Bielik 11B v2 Technical Report—0
Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation—0
Bielik v3 Small: Technical Report—0
BiFold: Bimanual Cloth Folding with Language Guidance—0
Bifurcated Attention: Accelerating Massively Parallel Decoding with Shared Prefixes in LLMs—0
Bigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners—0
BIG MOOD: Relating Transformers to Explicit Commonsense Knowledge—0
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
CodeSSM: Towards State Space Models for Code Understanding—0
Bilexical Embeddings for Quality Estimation—0
Bilingual Adaptation of Monolingual Foundation Models—0
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation—0
Bilingual Language Modeling, A transfer learning technique for Roman Urdu—0
Bilingually-constrained Phrase Embeddings for Machine Translation—0
Bilingual Structured Language Models for Statistical Machine Translation—0
Bilingual Word Embeddings from Non-Parallel Document-Aligned Data Applied to Bilingual Lexicon Induction—0
Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task—0
BiLMa: Bidirectional Local-Matching for Text-based Person Re-identification—0
Bi-Mamba: Towards Accurate 1-Bit State Space Models—0
BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling—0
Binarized LSTM Language Model—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