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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 8601–8650 of 17610 papers

TitleStatusHype
Performance Characterization of Expert Router for Scalable LLM Inference—0
Automated Text Mining of Experimental Methodologies from Biomedical Literature—0
Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following—0
LASER: Tuning-Free LLM-Driven Attention Control for Efficient Text-conditioned Image-to-Animation—0
Intrusion Detection at Scale with the Assistance of a Command-line Language Model—0
F5C-finder: An Explainable and Ensemble Biological Language Model for Predicting 5-Formylcytidine Modifications on mRNACode0
Generating Daylight-driven Architectural Design via Diffusion Models—0
Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging—0
Exploring Interactive Semantic Alignment for Efficient HOI Detection with Vision-language Model—0
Heterogeneous Subgraph Transformer for Fake News DetectionCode0
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media AnalysisCode0
LiMe: a Latin Corpus of Late Medieval Criminal Sentences—0
Skeleton: A New Framework for Accelerating Language Models via Task Neuron Localized Prompt Tuning—0
Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
Aligning Language Models to Explicitly Handle AmbiguityCode0
From Form(s) to Meaning: Probing the Semantic Depths of Language Models Using Multisense ConsistencyCode0
From r to Q^*: Your Language Model is Secretly a Q-Function—0
Concept Induction using LLMs: a user experiment for assessment—0
MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM RankersCode0
Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewriting—0
AccidentBlip: Agent of Accident Warning based on MA-former—0
DeepLocalization: Using change point detection for Temporal Action Localization—0
Augmenting emotion features in irony detection with Large language modeling—0
Enhance Robustness of Language Models Against Variation Attack through Graph Integration—0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
Characterizing and modeling harms from interactions with design patterns in AI interfaces—0
ViLLM-Eval: A Comprehensive Evaluation Suite for Vietnamese Large Language Models—0
Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language ModelsCode0
Language Models Still Struggle to Zero-shot Reason about Time SeriesCode0
Stepwise Alignment for Constrained Language Model Policy OptimizationCode0
Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model—0
Towards Data-Centric Automatic R&D—0
LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM—0
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in JapaneseCode0
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model—0
On the Scalability of GNNs for Molecular Graphs—0
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language ModelsCode0
More Room for Language: Investigating the Effect of Retrieval on Language ModelsCode0
Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional TrainingCode0
Grounded Language Agent for Product Search via Intelligent Web InteractionsCode0
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering—0
Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training—0
Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language ModelCode0
Future Language Modeling from Temporal Document HistoryCode0
Exact and Efficient Unlearning for Large Language Model-based Recommendation—0
Autoregressive Pre-Training on Pixels and TextsCode0
Fewer Truncations Improve Language Modeling—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