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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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 1751–1800 of 17610 papers

TitleStatusHype
Large Language Model as Universal Retriever in Industrial-Scale Recommender System—0
Intent Representation Learning with Large Language Model for RecommendationCode1
Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search—0
Fine-grained Preference Optimization Improves Zero-shot Text-to-Speech—0
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning—0
GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling—0
Large Language Model Guided Self-Debugging Code Generation—0
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific ApplicationsCode1
Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models SolutionsCode0
Control Search Rankings, Control the World: What is a Good Search Engine?—0
HACK: Homomorphic Acceleration via Compression of the Key-Value Cache for Disaggregated LLM Inference—0
Simplifying Formal Proof-Generating Models with ChatGPT and Basic Searching Techniques—0
Automating Mathematical Proof Generation Using Large Language Model Agents and Knowledge Graphs—0
FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data—0
Position: Stop Acting Like Language Model Agents Are Normal Agents—0
Prompt-based Depth Pruning of Large Language Models—0
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model—0
Reviving The Classics: Active Reward Modeling in Large Language Model AlignmentCode2
Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUsCode2
JingFang: A Traditional Chinese Medicine Large Language Model of Expert-Level Medical Diagnosis and Syndrome Differentiation-Based Treatment—0
Analyzing Similarity Metrics for Data Selection for Language Model Pretraining—0
Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD VariantsCode0
Rethinking Homogeneity of Vision and Text Tokens in Large Vision-and-Language Models—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
MPIC: Position-Independent Multimodal Context Caching System for Efficient MLLM Serving—0
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators—0
LLM-USO: Large Language Model-based Universal Sizing Optimizer—0
ComplexDec: A Domain-robust High-fidelity Neural Audio Codec with Complex Spectrum Modeling—0
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level RoutingCode1
EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues—0
When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression TasksCode0
Knowledge Synthesis of Photosynthesis Research Using a Large Language Model—0
Eliciting Language Model Behaviors with Investigator Agents—0
InfoBridge: Mutual Information estimation via Bridge Matching—0
Scaling Embedding Layers in Language Models—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Scalable Language Models with Posterior Inference of Latent Thought Vectors—0
The Differences Between Direct Alignment Algorithms are a Blur—0
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging—0
Explaining Context Length Scaling and Bounds for Language ModelsCode0
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-TuningCode0
FALCON: Fine-grained Activation Manipulation by Contrastive Orthogonal Unalignment for Large Language Model—0
Latent Lexical Projection in Large Language Models: A Novel Approach to Implicit Representation Refinement—0
Fine-Tuning Discrete Diffusion Models with Policy Gradient MethodsCode1
Position: Towards a Responsible LLM-empowered Multi-Agent Systems—0
Polynomial, trigonometric, and tropical activationsCode1
Simulating Rumor Spreading in Social Networks using LLM AgentsCode1
ConditionNET: Learning Preconditions and Effects for Execution Monitoring—0
An Inquiry into Datacenter TCO for LLM Inference with FP8—0
Language Models Use Trigonometry to Do Addition—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