mlsanityData to 14 Oct 2026

Byte-token Enhanced Language Models for Temporal Point Processes Analysis

arXiv 2502.07139 ↗First posted 11 Feb 2025cs.CL cs.LGcited by 6

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Abstract (arXiv)

Temporal Point Processes (TPPs) have been widely used for modeling event sequences on the Web, such as user reviews, social media posts, and online transactions. However, traditional TPP models often struggle to effectively incorporate the rich textual descriptions that accompany these events, while Large Language Models (LLMs), despite their remarkable text processing capabilities, lack mechanisms for handling the temporal dynamics inherent in Web-based event sequences. To bridge this gap, we introduce Language-TPP, a unified framework that seamlessly integrates TPPs with LLMs for enhanced Web event sequence modeling. Our key innovation is a novel temporal encoding mechanism that converts continuous time intervals into specialized byte-tokens, enabling direct integration with standard language model architectures for TPP modeling without requiring TPP-specific modifications. This approach allows Language-TPP to achieve state-of-the-art performance across multiple TPP benchmarks, including event time prediction and type prediction, on real-world Web datasets spanning e-commerce reviews, social media and online Q&A platforms. More importantly, we demonstrate that our unified framework unlocks new capabilities for TPP research: incorporating temporal information improves the quality of generated event descriptions, as evidenced by enhanced ROUGE-L scores, and better aligned sentiment distributions. Through comprehensive experiments, including qualitative analysis of learned distributions and scalability evaluations on long sequences, we show that Language-TPP effectively captures both temporal dynamics and textual patterns in Web user behavior, with important implications for content generation, user behavior understanding, and Web platform applications. Code is available at https://github.com/qykong/Language-TPP.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 49 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

26 of 49 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2024Qwen2.5 Technical Reportcited by 4,942
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  5. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  6. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  7. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  8. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,079
  9. 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 740
  10. 2023ChemCrow: Augmenting large-language models with chemistry toolscited by 631
  11. 2021ByT5: Towards a token-free future with pre-trained byte-to-byte modelscited by 509
  12. 2023Any-to-Any Generation via Composable Diffusioncited by 204
14 more
  1. 2023One Transformer Fits All Distributions in Multi-Modal Diffusion at Scalecited by 187
  2. 2024Byte Latent Transformer: Patches Scale Better Than Tokenscited by 146
  3. 2019Intensity-Free Learning of Temporal Point Processescited by 140
  4. 2017Modeling The Intensity Function Of Point Process Via Recurrent Neural Networkscited by 93
  5. 2021Transformer Embeddings of Irregularly Spaced Events and Their Participantscited by 93
  6. 2022HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequencescited by 57
  7. 2023Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoningcited by 45
  8. 2018Lecture Notes: Temporal Point Processes and the Conditional Intensity Functioncited by 40
  9. 2015COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolutioncited by 37
  10. 2023Prompt-augmented Temporal Point Process for Streaming Event Sequencecited by 24
  11. 2016Feature Driven and Point Process Approaches for Popularity Predictioncited by 18
  12. 2022Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systemscited by 16
  13. 2024Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networkscited by 11
  14. 2018Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networkscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2025DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understandingcited by 7
  2. 2026MILM: Large Language Models for Multimodal Irregular Time Series with Informative Samplingcited by 2
  3. 2025Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Modelscited by 1
  4. 2026Long-range Modeling and Processing of Multimodal Event Sequencescited by 0
  5. 2026Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectoriescited by 0
  6. 2026Can LLMs Access their World Knowledge for Event Prediction?cited by 0