arXiv Machine Learning

Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility

The paper introduces CorpFam, a public benchmark for corporate-family resolution comprising 54,864 candidate pairs across 10,307 families derived from 6.6 million US federal award records. It shows that traditional entity-matching methods perform poorly on pairs where name visibility is low, with the best matcher recovering only 4.2% of such invisible links and blocking schemes failing to identify most of them. The study argues that the real challenge lies in candidate generation rather than ranking, and validates the benchmark’s links against SEC subsidiary schedules, confirming their authenticity.

arXiv AI
Jul 1

RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora

arXiv:2604. 19047v2 Announce Type: replace-cross Abstract: Existing QA benchmarks typically assume distinct documents with minimal overlap, yet real-world retrieval-augmented generation (RAG) systems operate on corpora such as financial reports, legal codes, and patents, where information is highly redundant and documents exhibit strong inter-document similarity.

By Hanjun Cho, Jay-Yoon Lee
arXiv AI
Sep 7

Does the Selected Object Reach the Reader? Auditing Identity Handoffs in Grounded Language-Model Pipelines

The paper investigates whether the object selected in a grounded language‑model pipeline actually reaches the reader, a failure that can break the handoff between stages. By auditing 600 HybridQA questions across three selector families, the authors find that exact key lookup and title matching recover the selected object in all 1,463 resolvable records, but body‑only BM25 omits it in 26.6% of cases at cutoff five, while hybrid retrieval with reranking omits it only 1.0%. The study also shows that misalignment between selected and retrieved objects can reduce exact match scores by up to 31 points, and introduces the Returned‑Object Profile (ROP) as a tool for reproducible auditing.

By Siddharth Vohra, Runmin Jiang, Xiaomo Li, Min Xu
arXiv Machine Learning
Aug 27

OpenSanctions Pairs: Large-Scale Entity Matching with LLMs

OpenSanctions Pairs is the first large‑scale public benchmark for entity matching on sanctions and OSINT data, comprising 755,540 expert‑labeled pairs drawn from over 1 million entities across 293 source datasets and 45 jurisdictions. The dataset spans multiple languages and writing systems, inconsistent structures, and time‑varying provenance, making it far more heterogeneous than prior benchmarks. Baseline experiments show a rule‑based matcher achieving 91.3 % F1, GPT‑4o reaching 99.0 % F1, and a locally deployable open‑source model scoring 98.2 % F1, with complementary failure modes that highlight the need to focus on downstream pipeline components.

By Chandler Smith, Magnus Sesodia, Friedrich Lindenberg, Christian Schroeder de Witt
Hugging Face Trending Papers
Jun 29

How Far Do On-Prem Open LLMs Get on Text-to-SQL? A Cross-Family Size x Technique Frontier on BIRD

Organizations that cannot send data to a cloud API increasingly ask: how good is Text-to-SQL if the model must run on-premises on open weights, and which popular accuracy "recipes" are worth their compute? We answer with an honest, fully reproducible benchmark on the BIRD development split (n=1534, Execution Accuracy), evaluating three open model families across two generations -- Qwen2.

arXiv Computation and Language
Sep 3

Privacy Washing: Detecting Internal Contradictions in Privacy Policies

The study introduces a four‑stage pipeline to detect internal contradictions—termed privacy washing—in privacy policies. Applied to two corpora (123 policies from 2026 and 115 from 2015), the pipeline identifies contradictions in 12.2% of the newer policies and 36.5% of the older ones, with third‑party sharing conflicts being the most common. A stability re‑run confirms similar prevalence rates and shows that the majority of contradictions are consistent across different model configurations.

By Thomas Brackin