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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 4551–4600 of 17610 papers

TitleStatusHype
Efficient Controlled Language Generation with Low-Rank Autoregressive Reward Models—0
Testing learning hypotheses using neural networks by manipulating learning data—0
PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit PostsCode0
Spontaneous Reward Hacking in Iterative Self-Refinement—0
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMsCode1
EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME context—0
When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn SettingsCode1
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR—0
Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR CorrectionCode0
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection SystemCode1
Improving Self Consistency in LLMs through Probabilistic Tokenization—0
Chain-of-Thought Augmentation with Logit Contrast for Enhanced Reasoning in Language Models—0
ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations—0
Generative Technology for Human Emotion Recognition: A Scope Review—0
MRIR: Integrating Multimodal Insights for Diffusion-based Realistic Image Restoration—0
MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation LearningCode0
The Mysterious Case of Neuron 1512: Injectable Realignment Architectures Reveal Internal Characteristics of Meta's Llama 2 ModelCode0
Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration—0
Uncertainty-Guided Optimization on Large Language Model Search TreesCode0
Mixture of A Million ExpertsCode2
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization—0
Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time CorrectionCode1
Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant ContentCode0
Unlocking the Potential of Model Merging for Low-Resource Languages—0
Narrow Transformer: StarCoder-Based Java-LM For Desktop—0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
MiniGPT-Med: Large Language Model as a General Interface for Radiology DiagnosisCode2
RDBE: Reasoning Distillation-Based Evaluation Enhances Automatic Essay Scoring—0
Large Language Model Agents for Improving Engagement with Behavior Change Interventions: Application to Digital Mindfulness—0
LLMcap: Large Language Model for Unsupervised PCAP Failure Detection—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
Fine-Tuning with Divergent Chains of Thought Boosts Reasoning Through Self-Correction in Language ModelsCode1
CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics—0
Towards Federated RLHF with Aggregated Client Preference for LLMs—0
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data—0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Efficient Training of Language Models with Compact and Consistent Next Token Distributions—0
Croppable Knowledge Graph Embedding—0
SAFT: Towards Out-of-Distribution Generalization in Fine-Tuning—0
Model-Enhanced LLM-Driven VUI Testing of VPA Apps—0
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output—0
Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering—0
Let the Code LLM Edit Itself When You Edit the Code—0
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning LanguagesCode2
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model—0
Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective—0
Lightweight Large Language Model for Medication Enquiry: Med-Pal—0
Assessing the Effectiveness of GPT-4o in Climate Change Evidence Synthesis and Systematic Assessments: Preliminary Insights—0
SeqMate: A Novel Large Language Model Pipeline for Automating RNA Sequencing—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