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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10101–10150 of 17610 papers

TitleStatusHype
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation—0
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition—0
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems—0
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object DetectionCode0
Zero-Shot Question Answering over Financial Documents using Large Language Models—0
Representing visual classification as a linear combination of wordsCode0
A Principled Framework for Knowledge-enhanced Large Language Model—0
Energy and Carbon Considerations of Fine-Tuning BERT—0
Flexible Model Interpretability through Natural Language Model Editing—0
Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing—0
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads—0
PEFT-MedAware: Large Language Model for Medical Awareness—0
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement—0
On Functional Activations in Deep Neural Networks—0
Testing Language Model Agents Safely in the Wild—0
Multi-Step Dialogue Workflow Action Prediction—0
Strings from the Library of Babel: Random Sampling as a Strong Baseline for Prompt OptimisationCode0
LLMs as Narcissistic Evaluators: When Ego Inflates Evaluation Scores—0
The Ups and Downs of Large Language Model Inference with Vocabulary Trimming by Language Heuristics—0
Downstream Trade-offs of a Family of Text WatermarksCode0
Leveraging LLMs in Scholarly Knowledge Graph Question AnsweringCode0
On Retrieval Augmentation and the Limitations of Language Model Training—0
Reducing Privacy Risks in Online Self-Disclosures with Language Models—0
Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis—0
WatME: Towards Lossless Watermarking Through Lexical Redundancy—0
Can Language Model Moderators Improve the Health of Online Discourse?—0
Crafting In-context Examples according to LMs' Parametric KnowledgeCode0
Effective Large Language Model Adaptation for Improved Grounding and Citation Generation—0
Improving the Generation Quality of Watermarked Large Language Models via Word Importance Scoring—0
DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback—0
Characterizing Tradeoffs in Language Model Decoding with Informational Interpretations—0
A Speed Odyssey for Deployable Quantization of LLMs—0
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination—0
Comparing Generalization in Learning with Limited Numbers of Exemplars: Transformer vs. RNN in Attractor Dynamics—0
Assessing Translation capabilities of Large Language Models involving English and Indian Languages—0
Data Similarity is Not Enough to Explain Language Model PerformanceCode0
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?—0
Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing—0
CLIMB: Curriculum Learning for Infant-inspired Model Building—0
GENEVA: GENErating and Visualizing branching narratives using LLMs—0
HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation—0
An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease PhenotypingCode0
Temperature-scaling surprisal estimates improve fit to human reading times -- but does it do so for the "right reasons"?Code0
German FinBERT: A German Pre-trained Language Model—0
Grounding Gaps in Language Model Generations—0
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?—0
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference—0
SiRA: Sparse Mixture of Low Rank Adaptation—0
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
Show:102550
← PrevPage 203 of 353Next →
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