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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 6451–6500 of 17610 papers

TitleStatusHype
Application of Quantum Tensor Networks for Protein Classification—0
Knowledge-aware Alert Aggregation in Large-scale Cloud Systems: a Hybrid Approach—0
Development of a Reliable and Accessible Caregiving Language Model (CaLM)—0
FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font ApplicationsCode1
ACT-MNMT Auto-Constriction Turning for Multilingual Neural Machine Translation—0
Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal DocumentsCode0
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context LearningCode0
Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning—0
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video GenerationCode3
CLEAR: Cross-Transformers with Pre-trained Language Model is All you need for Person Attribute Recognition and Retrieval—0
Personalized LoRA for Human-Centered Text UnderstandingCode1
In-context Prompt Learning for Test-time Vision Recognition with Frozen Vision-language Model—0
Identifying and interpreting non-aligned human conceptual representations using language modeling—0
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned DecisionCode1
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification—0
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance—0
Aligning Speech to Languages to Enhance Code-switching Speech Recognition—0
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical NotesCode1
Thread Detection and Response Generation using Transformers with Prompt Optimisation—0
Algorithmic progress in language modelsCode1
High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models—0
CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System—0
VLM-PL: Advanced Pseudo Labeling Approach for Class Incremental Object Detection via Vision-Language Model—0
CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model—0
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic AlignmentCode5
WatChat: Explaining perplexing programs by debugging mental modelsCode0
Are Human Conversations Special? A Large Language Model Perspective—0
Will GPT-4 Run DOOM?—0
Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis AgentsCode1
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-ThoughtCode1
DeepSeek-VL: Towards Real-World Vision-Language UnderstandingCode7
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answeringCode0
Evaluating Biases in Context-Dependent Health QuestionsCode0
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised PretrainingCode4
Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach—0
Yi: Open Foundation Models by 01.AICode9
Self-Evaluation of Large Language Model based on Glass-box FeaturesCode0
Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy—0
Classist Tools: Social Class Correlates with Performance in NLP—0
Preference optimization of protein language models as a multi-objective binder design paradigm—0
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual ScenariosCode2
Embodied Understanding of Driving ScenariosCode3
Do Large Language Model Understand Multi-Intent Spoken Language ?Code0
Online Adaptation of Language Models with a Memory of Amortized ContextsCode2
Advancing Chinese biomedical text mining with community challenges—0
TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning—0
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification—0
Can Large Language Models do Analytical Reasoning?—0
Backtracing: Retrieving the Cause of the QueryCode2
On the Origins of Linear Representations in Large Language Models—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