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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6151–6200 of 17610 papers

TitleStatusHype
When Babies Teach Babies: Can student knowledge sharing outperform Teacher-Guided Distillation on small datasets?Code0
Towards Agentic Schema Refinement—0
Tree Transformers are an Ineffective Model of Syntactic Constituency—0
StructFormer: Document Structure-based Masked Attention and its Impact on Language Model Pre-Training—0
SAGEval: The frontiers of Satisfactory Agent based NLG Evaluation for reference-free open-ended text—0
Is Training Data Quality or Quantity More Impactful to Small Language Model Performance?Code0
Generative Prompt InternalizationCode0
Can a Large Language Model Learn Matrix Functions In Context?Code0
Ensuring Fair LLM Serving Amid Diverse Applications—0
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset—0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
Enabling Efficient Serverless Inference Serving for LLM (Large Language Model) in the Cloud—0
Automatic High-quality Verilog Assertion Generation through Subtask-Focused Fine-Tuned LLMs and Iterative Prompting—0
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language ModelCode0
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge AlignmentCode0
The BS-meter: A ChatGPT-Trained Instrument to Detect Sloppy Language-Games—0
Astro-HEP-BERT: A bidirectional language model for studying the meanings of concepts in astrophysics and high energy physics—0
Effective SAM Combination for Open-Vocabulary Semantic Segmentation—0
ElastiFormer: Learned Redundancy Reduction in Transformer via Self-Distillation—0
A Framework for Evaluating LLMs Under Task Indeterminacy—0
DRPruning: Efficient Large Language Model Pruning through Distributionally Robust OptimizationCode0
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge—0
Memory Backdoor Attacks on Neural Networks—0
Schemato -- An LLM for Netlist-to-Schematic Conversion—0
PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario SimulationCode0
Patience Is The Key to Large Language Model Reasoning—0
S^2ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning—0
LightLLM: A Versatile Large Language Model for Predictive Light Sensing—0
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and ChemistryCode0
MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification—0
Multimodal large language model for wheat breeding: a new exploration of smart breeding—0
Advancing Complex Medical Communication in Arabic with Sporo AraSum: Surpassing Existing Large Language Models—0
Beyond Visual Understanding: Introducing PARROT-360V for Vision Language Model Benchmarking—0
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders—0
Existential Conversations with Large Language Models: Content, Community, and Culture—0
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding—0
Watermark under Fire: A Robustness Evaluation of LLM WatermarkingCode0
RadPhi-3: Small Language Models for Radiology—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model—0
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI CombatCode0
Strengthening Fake News Detection: Leveraging SVM and Sophisticated Text Vectorization Techniques. Defying BERT?—0
Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite DistractionCode0
HouseLLM: LLM-Assisted Two-Phase Text-to-Floorplan Generation—0
CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model—0
A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems—0
Generative Timelines for Instructed Visual Assembly—0
CATCH: Complementary Adaptive Token-level Contrastive Decoding to Mitigate Hallucinations in LVLMs—0
PSA-VLM: Enhancing Vision-Language Model Safety through Progressive Concept-Bottleneck-Driven Alignment—0
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality—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