ThinkTwice: A New Approach to Document-Level Info Extraction

In the meticulous world of document-level information extraction (DocIE), the standard practice of using decoder-only large language models (LLMs) with greedy decoding has held sway for its ability to offer consistent templates. However, a new framework called ThinkTwice is poised to disrupt this norm, exploiting the very variability that was once seen as a hindrance.

Sampling Over Greedy Decoding

ThinkTwice flips conventional wisdom on its head by embracing sampling instead of shunning it. While greedy decoding avoids the unpredictability of varied outputs, ThinkTwice demonstrates that sampling can indeed yield superior solutions, particularly when paired with reasoning models. Rather than sticking to a single path, this framework generates multiple candidate templates for each document. A selection module then discerns the most fitting choice, giving rise to more nuanced and accurate outputs.

Why does this matter? Simply put, the ability to harness variation could dramatically improve the adaptability and precision of information extraction across diverse documents. In a world flooded with information, the ability to accurately and flexibly extract entities, relations, and events is invaluable.

Innovative Selection Methods

ThinkTwice's methodology isn't just about generating varied outputs. it's about choosing wisely. The framework introduces both unsupervised and supervised selection methods. The unsupervised approach leverages agreement across generated outputs, a clever tactic that uses consensus as a proxy for accuracy. Meanwhile, the supervised method employs reward models trained on labeled DocIE data, offering a more structured path to precision.

What they're not telling you: this isn't just about better document processing. It's about redefining the potential of AI to understand and interpret complex information. The proposed rejection-sampling method to generate 'silver' training data, output templates matched with reasoning traces, addresses the common challenge of scarce golden reasoning trajectories, making the model more reliable for real-world applications.

Outperforming the Status Quo

Let's apply some rigor here. The ThinkTwice framework consistently outperforms not only greedy baselines but also the current supervised state-of-the-art methods. That's not just progress. it's a significant leap forward. For an industry constantly pushing for more accurate and efficient AI, ThinkTwice represents a bold step in evolving from static, predictable outputs to dynamic, contextually aware processing.

Color me skeptical, but could this be the beginning of the end for the dominance of greedy decoding in DocIE? As more models embrace the variability inherent in large datasets, the potential for extracting richer, more accurate information becomes tantalizingly possible.