arXiv (All)
open access
Anonymization and Information Loss
Anonymizing financial texts prevents large language models (LLMs) from exploiting look-ahead bias, but inadvertently weakens the extracted signal. We propose a framework disentangling this information loss from bias removal. Predicting S&P credit downgrades, raw texts significantly outperform anonym
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation#1048329
- MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting#1076844
- To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech#1078298
- I Am No One: Style-Aware Paraphrasing for Text Anonymization#1000298
- A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings#1083236
Record · ID 972992
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive