Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning (2305.16646v2)

Abstract: LLMs have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design LAMP, a framework that integrates a LLM in event prediction. Particularly, the LLM performs abductive reasoning to assist an event sequence model: the event model proposes predictions on future events given the past; instructed by a few expert-annotated demonstrations, the LLM learns to suggest possible causes for each proposal; a search module finds out the previous events that match the causes; a scoring function learns to examine whether the retrieved events could actually cause the proposal. Through extensive experiments on several challenging real-world datasets, we demonstrate that our framework -- thanks to the reasoning capabilities of LLMs -- could significantly outperform the state-of-the-art event sequence models.

Overview of the Paper: LLMs Can Improve Event Prediction by Few-Shot Abductive Reasoning

The research paper titled "LLMs Can Improve Event Prediction by Few-Shot Abductive Reasoning" investigates the role of LLMs in enhancing the prediction capabilities of event sequence models. The paper presents the LAMP framework as a methodological integration of LLMs into event prediction tasks, specifically leveraging these models' ability to perform abductive reasoning. This paper positions itself within the field of event sequence modeling, a domain concerned with forecasting future events based on historical data.

Summary

The paper delineates a novel approach to event prediction, utilizing a LLM to augment traditional event sequence models. The authors introduce LAMP (LLM Assistance in Event Prediction), a framework designed to collaborate with an event sequence model in predicting the type and timing of future occurrences. The framework leverages LLMs to conduct abductive reasoning, whereby the LLM provides possible causal explanations for predicted events, employing few-shot learning to extrapolate beyond the specific demonstrations provided.

Key Components of the Approach:

1. Prediction Proposals: An event sequence model is first used to generate candidate predictions. This involves proposing multiple potential future events based on the model's understanding of past occurrences.
2. Abductive Reasoning via LLMs: Given the proposed events, an LLM is tasked with generating plausible causes for these predictions. The reasoning component of the LLM incorporates few-shot learning, which is fed by a concise set of expert-annotated demonstrations.
3. Retrieval and Scoring: The framework includes a retrieval mechanism to identify historical events that correspond with the LLM-generated causal explanations, and a scoring function evaluates the plausibility of the proposed events with respect to the retrieved data.
4. Ranking and Decision Making: The final predictions are ranked by their compatibility scores, allowing the framework to revise the initial estimates made by the base event sequence model, thereby improving accuracy.

Empirical Evaluation

The empirical results presented demonstrate LAMP's superior performance across several challenging datasets, including real-world political event data (GDELT and ICEWS) and user review sequences (Amazon Review). Performance metrics, such as mean rank (MR) and mean reciprocal rank (MRR), consistently favor the LAMP-enhanced models over baseline sequence-only models. Notably, the framework's efficacy increases with a greater number of candidate predictions and queries, indicating the value added by abductive reasoning in refining event forecasts.

Implications and Future Directions

The incorporation of LLMs into event sequence prediction introduces a promising avenue for augmenting model performance with rich contextual reasoning. Practically, this means better-informed forecasts in domains like healthcare, politics, and finance, where understanding the causal interplay of events is crucial. Theoretically, the paper suggests further potential in exploring diverse reasoning tasks that LLMs can support, raising questions about the integration of other reasoning paradigms, such as deductive and inductive reasoning.

In terms of future developments, ongoing advancements in LLM capabilities, particularly within open-source environments like Llama-2, suggest a path toward more accessible and adaptable implementations of similar frameworks. The exploration of end-to-end training frameworks that jointly optimize the event sequence models and LLM reasoning may offer additional gains in accuracy and efficiency. Furthermore, adapting this approach to address incomplete datasets or to dynamically learn from streaming events presents another frontier for research.

Overall, this research provides a substantive basis for the observation that LLMs, when strategically deployed, can significantly extend the predictive capabilities of event sequence models through sophisticated reasoning processes.