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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 3001–3050 of 17610 papers

TitleStatusHype
CPE-Pro: A Structure-Sensitive Deep Learning Method for Protein Representation and Origin EvaluationCode0
Building A Coding Assistant via the Retrieval-Augmented Language ModelCode1
Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model—0
SeisLM: a Foundation Model for Seismic WaveformsCode1
Extracting Spatiotemporal Data from Gradients with Large Language Models—0
Large Body Language Models—0
ComPO: Community Preferences for Language Model Personalization—0
LSCodec: Low-Bitrate and Speaker-Decoupled Discrete Speech Codec—0
AutoTrain: No-code training for state-of-the-art modelsCode7
Deep Learning and Data Augmentation for Detecting Self-Admitted Technical DebtCode0
xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs—0
Improve Vision Language Model Chain-of-thought ReasoningCode2
Are Language Model Logits Calibrated?—0
The effect of fine-tuning on language model toxicityCode0
Opportunities and Challenges of Generative-AI in Finance—0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare DiseasesCode0
BERTtime Stories: Investigating the Role of Synthetic Story Data in Language pre-trainingCode0
M-RewardBench: Evaluating Reward Models in Multilingual SettingsCode1
MMDS: A Multimodal Medical Diagnosis System Integrating Image Analysis and Knowledge-based Departmental Consultation—0
Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training—0
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering—0
EVA: An Embodied World Model for Future Video Anticipation—0
ChronoFact: Timeline-based Temporal Fact Verification—0
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer LearningCode0
MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized CollaborationCode0
Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models—0
SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent EvaluationCode2
AutoFPDesigner: Automated Flight Procedure Design Based on Multi-Agent Large Language Model—0
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA—0
CLIPtortionist: Zero-shot Text-driven Deformation for Manufactured 3D Shapes—0
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions—0
A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers—0
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials ScienceCode0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning—0
Reasoning, Memorization, and Fine-Tuning Language Models for Non-Cooperative Games—0
Joint Verification and Refinement of Language Models for Safety-Constrained Planning—0
Automated Genre-Aware Article Scoring and Feedback Using Large Language Models—0
PTR: A Pre-trained Language Model for Trajectory Recovery—0
A Systematic Study of Cross-Layer KV Sharing for Efficient LLM InferenceCode2
Good Parenting is all you need -- Multi-agentic LLM Hallucination Mitigation—0
SPRIG: Improving Large Language Model Performance by System Prompt OptimizationCode0
DFlow: Diverse Dialogue Flow Simulation with Large Language Models—0
SNAC: Multi-Scale Neural Audio CodecCode4
Montessori-Instruct: Generate Influential Training Data Tailored for Student LearningCode2
SudoLM: Learning Access Control of Parametric Knowledge with Authorization Alignment—0
Paths-over-Graph: Knowledge Graph Empowered Large Language Model ReasoningCode1
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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