Influcoder: Distilling Decoders' Gradient Influence Rankings into an Encoder for Data Attribution

2026-06-11 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

Influcoder is a novel method designed for scalable, cost-effective influence-based Data Attribution (DA) in Large Language Models (LLMs). Developed to address the growing need for high-quality dataset curation, Influcoder aims to identify how individual training samples precondition a model's outputs, such as contributing to toxic behavior. While traditional influence function methods are effective for DA, they are often impractical for large datasets due to their slow processing speed and significant storage requirements. Influcoder distills decoders' gradient influence rankings into an encoder, offering a quicker and more compact approach to perform data attribution at scale, thereby facilitating the filtering of problematic samples from LLM training data.

Key takeaway

For Machine Learning Engineers curating large language model datasets, Influcoder offers a critical solution to the scalability challenges of data attribution. You should consider integrating Influcoder to quickly and cost-effectively identify problematic training samples, such as those contributing to toxic outputs. This approach allows you to efficiently filter your datasets, ensuring higher quality and more reliable LLM performance without the prohibitive computational overhead of traditional influence functions.

Key insights

Influcoder enables scalable, cost-effective data attribution for LLMs by distilling decoder influence into an encoder.

Principles

• Data Attribution estimates training sample conditioning.
• Influence functions quantify model conditioning.
• Scalability is crucial for large dataset attribution.

Method

Influcoder distills decoders' gradient influence rankings into an encoder to achieve quick and cost-effective influence-based Data Attribution at scale.

In practice

• Filter training data for quality.
• Identify sources of toxic model behavior.
• Curate high-quality LLM datasets.

Topics

• Data Attribution
• Large Language Models
• Influence Functions
• Dataset Curation
• Gradient Influence
• Encoder Models

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.