Academic

LLMs can construct powerful representations and streamline sample-efficient supervised learning

arXiv:2603.11679v1 Announce Type: new Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data for downstream tasks, such as time-series, free text, and structured records, often requires non-trivial domain-specific engineering. We propose an agentic pipeline to streamline this process. First, an LLM analyzes a small but diverse subset of text-serialized input examples in-context to synthesize a global rubric, which acts as a programmatic specification for extracting and organizing evidence. This rubric is then used to transform naive text-serializations of inputs into a more standardized format for downstream models. We also describe local rubrics, which are task-conditioned summaries generated by an LLM. Across 15 clinical tasks from the EHRSHOT benchmark, our rubric-based approaches significantly outperform traditional count-feature models, naive text-serializat

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Ilker Demirel, Larry Shi, Zeshan Hussain, David Sontag · · 1 min read · 10 views
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arXiv:2603.11679v1 Announce Type: new Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data for downstream tasks, such as time-series, free text, and structured records, often requires non-trivial domain-specific engineering. We propose an agentic pipeline to streamline this process. First, an LLM analyzes a small but diverse subset of text-serialized input examples in-context to synthesize a global rubric, which acts as a programmatic specification for extracting and organizing evidence. This rubric is then used to transform naive text-serializations of inputs into a more standardized format for downstream models. We also describe local rubrics, which are task-conditioned summaries generated by an LLM. Across 15 clinical tasks from the EHRSHOT benchmark, our rubric-based approaches significantly outperform traditional count-feature models, naive text-serialization-based LLM baselines, and a clinical foundation model, which is pretrained on orders of magnitude more data. Beyond performance, rubrics offer several advantages for operational healthcare settings such as being easy to audit, cost-effectiveness to deploy at scale, and they can be converted to tabular representations that unlock a swath of machine learning techniques.

Executive Summary

This article proposes an innovative agentic pipeline that leverages Large Language Models (LLMs) to streamline supervised learning by constructing powerful representations of complex and heterogeneous data. The pipeline involves an LLM synthesizing a global rubric from a diverse subset of input examples, which is then used to transform naive text-serializations into a standardized format for downstream models. The authors demonstrate significant performance improvements over traditional count-feature models, naive text-serialization-based LLM baselines, and a clinical foundation model. The proposed approach offers several advantages, including ease of auditing, cost-effectiveness, and the ability to convert to tabular representations that unlock various machine learning techniques.

Key Points

  • ▸ The proposed agentic pipeline leverages LLMs to streamline supervised learning for complex and heterogeneous data.
  • ▸ The pipeline involves synthesizing a global rubric from a diverse subset of input examples.
  • ▸ The approach offers significant performance improvements over traditional models and LLM baselines.

Merits

Improves Supervised Learning Efficiency

The proposed pipeline enables the construction of powerful representations of complex data, streamlining supervised learning and reducing the need for non-trivial domain-specific engineering.

Enhances Model Performance

The approach demonstrates significant performance improvements over traditional models and LLM baselines, making it a promising solution for real-world applications.

Offers Operational Advantages

The proposed pipeline offers several operational advantages, including ease of auditing, cost-effectiveness, and the ability to convert to tabular representations that unlock various machine learning techniques.

Demerits

Limited Generalizability

The proposed approach may not generalize well to domains with fundamentally different data structures or requirements, limiting its applicability.

Dependence on LLM Quality

The performance of the proposed pipeline is heavily dependent on the quality and capabilities of the LLM used, which may not be universally available or reliable.

Expert Commentary

The proposed agentic pipeline represents a significant innovation in the field of machine learning, particularly in the area of supervised learning. The approach leverages the capabilities of LLMs to construct powerful representations of complex data, which can be used to streamline supervised learning and improve model performance. However, the limitations of the approach, such as limited generalizability and dependence on LLM quality, must be carefully considered. Furthermore, the operational advantages of the proposed pipeline, including ease of auditing and cost-effectiveness, make it an attractive solution for real-world applications. As the field of machine learning continues to evolve, it is essential to explore and develop innovative approaches like the proposed pipeline to tackle the complex challenges of real-world data.

Recommendations

  • ✓ Further research is needed to explore the generalizability of the proposed pipeline to domains with fundamentally different data structures or requirements.
  • ✓ Investigating the use of the proposed pipeline in various real-world applications, including healthcare, finance, and social media, would be beneficial to understand its practical implications.

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