Summary of Tpp-llm: Modeling Temporal Point Processes by Efficiently Fine-tuning Large Language Models, By Zefang Liu et al.
TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models
by Zefang Liu, Yinzhu Quan
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, TPP-LLM, integrates large language models (LLMs) with temporal point processes (TPPs) to model the timing and occurrence of events in various domains. Unlike traditional methods, TPP-LLM uses textual descriptions of event types instead of categorical representations, allowing it to capture rich semantic information. To address the limitations of LLMs in capturing temporal patterns, TPP-LLM incorporates temporal embeddings and employs parameter-efficient fine-tuning (PEFT) methods, resulting in improved predictive accuracy and computational efficiency. Experimental results demonstrate that TPP-LLM outperforms state-of-the-art baselines in sequence modeling and event prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to model events by combining large language models with temporal point processes. It’s like using a map to understand the timing of things happening, and also understanding what those things mean. This helps the model make better predictions about when and why certain events will happen. The results show that this approach is better than others at predicting sequences of events. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
