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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 5451–5500 of 17610 papers

TitleStatusHype
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents—0
Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment—0
Fennec: Fine-grained Language Model Evaluation and Correction Extended through Branching and BridgingCode0
Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Home—0
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs—0
Token-wise Influential Training Data Retrieval for Large Language ModelsCode1
KG-RAG: Bridging the Gap Between Knowledge and Creativity—0
Unveiling and Manipulating Prompt Influence in Large Language ModelsCode1
Human-Centered LLM-Agent User Interface: A Position PaperCode0
A Method on Searching Better Activation Functions—0
EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations—0
VR-GPT: Visual Language Model for Intelligent Virtual Reality Applications—0
CPS-LLM: Large Language Model based Safe Usage Plan Generator for Human-in-the-Loop Human-in-the-Plant Cyber-Physical System—0
SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch NormalizationCode2
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion—0
DocReLM: Mastering Document Retrieval with Language Model—0
LeaPformer: Enabling Linear Transformers for Autoregressive and Simultaneous Tasks via Learned ProportionsCode1
Automating PTSD Diagnostics in Clinical Interviews: Leveraging Large Language Models for Trauma Assessments—0
Transformer based neural networks for emotion recognition in conversationsCode0
Towards Modular LLMs by Building and Reusing a Library of LoRAs—0
The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving—0
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly DetectionCode2
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving—0
Evaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark—0
Surgical Feature-Space Decomposition of LLMs: Why, When and How?Code0
Multilingual Substitution-based Word Sense Induction—0
AudioSetMix: Enhancing Audio-Language Datasets with LLM-Assisted Augmentations—0
SignLLM: Sign Language Production Large Language Models—0
ActiveLLM: Large Language Model-based Active Learning for Textual Few-Shot Scenarios—0
Watermarking Language Models for Many Adaptive Users—0
CELA: Cost-Efficient Language Model Alignment for CTR PredictionCode0
The Future of Large Language Model Pre-training is Federated—0
Dynamic data sampler for cross-language transfer learning in large language modelsCode7
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities—0
RDRec: Rationale Distillation for LLM-based RecommendationCode1
Layer-Condensed KV Cache for Efficient Inference of Large Language ModelsCode2
COGNET-MD, an evaluation framework and dataset for Large Language Model benchmarks in the medical domain—0
Observational Scaling Laws and the Predictability of Language Model PerformanceCode2
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model—0
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection—0
Grounded 3D-LLM with Referent TokensCode2
Zero-Shot Hierarchical Classification on the Common Procurement Vocabulary Taxonomy—0
Keep It Private: Unsupervised Privatization of Online TextCode0
Semantic Gesticulator: Semantics-Aware Co-Speech Gesture SynthesisCode3
Conformal Alignment: Knowing When to Trust Foundation Models with GuaranteesCode1
When Large Language Model Meets Optimization—0
NIFTY Financial News Headlines Dataset—0
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language ModelsCode1
Libra: Building Decoupled Vision System on Large Language ModelsCode2
SynthesizRR: Generating Diverse Datasets with Retrieval AugmentationCode1
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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