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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 7051–7100 of 17610 papers

TitleStatusHype
Panacea: Pareto Alignment via Preference Adaptation for LLMs—0
AnthroScore: A Computational Linguistic Measure of AnthropomorphismCode1
Image Fusion via Vision-Language ModelCode4
Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive LearningCode1
Rethinking the Role of Proxy Rewards in Language Model AlignmentCode0
Cross-modality debiasing: using language to mitigate sub-population shifts in imaging—0
A Survey on Large Language Model Hallucination via a Creativity Perspective—0
Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue AbilitiesCode5
InferCept: Efficient Intercept Support for Augmented Large Language Model InferenceCode1
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning—0
Code Representation Learning At Scale—0
Large Language Model Agent for Hyper-Parameter Optimization—0
Natural language guidance of high-fidelity text-to-speech with synthetic annotationsCode9
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature—0
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement—0
Retrieval Augmented End-to-End Spoken Dialog Models—0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
BAT: Learning to Reason about Spatial Sounds with Large Language Models—0
Decoding Speculative DecodingCode1
Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning AttackCode1
Interpretation of Intracardiac Electrograms Through Textual RepresentationsCode1
Integrating Large Language Models in Causal Discovery: A Statistical Causal ApproachCode0
Style Vectors for Steering Generative Large Language ModelCode1
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language ModelsCode1
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks—0
Need a Small Specialized Language Model? Plan Early!—0
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning—0
Learning Semantic Information from Raw Audio Signal Using Both Contextual and Phonetic Representations—0
The Information of Large Language Model Geometry—0
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsCode0
HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA—0
BlackMamba: Mixture of Experts for State-Space ModelsCode3
Generation, Distillation and Evaluation of Motivational Interviewing-Style Reflections with a Foundational Language Model—0
Investigating Recurrent Transformers with Dynamic HaltCode0
Exploring Spatial Schema Intuitions in Large Language and Vision Models—0
Executable Code Actions Elicit Better LLM AgentsCode5
Institutional Platform for Secure Self-Service Large Language Model Exploration—0
MEIA: Multimodal Embodied Perception and Interaction in Unknown EnvironmentsCode5
Non-Exchangeable Conformal Language Generation with Nearest NeighborsCode1
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source SoftwareCode1
CroissantLLM: A Truly Bilingual French-English Language ModelCode0
Unlearnable Algorithms for In-context Learning—0
OLMo: Accelerating the Science of Language ModelsCode9
Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective—0
From PARIS to LE-PARIS: Toward Patent Response Automation with Recommender Systems and Collaborative Large Language Models—0
Transforming and Combining Rewards for Aligning Large Language Models—0
Human-mediated Large Language Models for Robotic Intervention in Children with Autism Spectrum Disorders—0
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System—0
PAP-REC: Personalized Automatic Prompt for Recommendation Language ModelCode1
Towards Efficient Exact Optimization of Language Model AlignmentCode2
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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