Summary of Filtered Not Mixed: Stochastic Filtering-based Online Gating For Mixture Of Large Language Models, by Raeid Saqur et al.
Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models
by Raeid Saqur, Anastasis Kratsios, Florian Krach, Yannick Limmer, Jacob-Junqi Tian, John Willes, Blanka Horvath, Frank Rudzicz
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF)
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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 MoE-F is a novel mechanism for combining multiple pre-trained Large Language Models (LLMs) for online time-series prediction. It leverages conditional information to forecast the best combination of LLMs at every time step, utilizing adaptive stochastic filtering techniques and framing the expert selection problem as a finite state-space Hidden Markov model (HMM). The approach constructs parallel filters for each LLM, proposes optimal combinations based on accessible information, and aggregates outputs to maximize robust predictive power. MoE-F attains a 17% absolute and 48.5% relative F1 measure improvement over individual LLMs in short-horizon financial market movement prediction using streaming news. This paper also provides empirical evidence of performance gains in long-horizon time-series forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to combine many artificial intelligence models (LLMs) for predicting future events. It’s like choosing the best team members based on their past performance, and then combining their efforts for better results. The method uses special mathematical techniques to select the right combination of LLMs at every moment, making predictions more accurate. In a test with financial market data, this approach performed 17% better than individual models in predicting short-term market movements. This shows that combining multiple AI models can be very effective in certain situations. |
Keywords
» Artificial intelligence » Hidden markov model » Time series
