Papers for

government it teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

UAE GovTrust improves bilingual public service retrieval with scope filtering

UAE GovTrust Scope Filtering Gains and Routing Failures in Bilingual Public Service Retrieval

Abstract: Public-service queries can mention several locations while only one controls the requested service. We study this distinction in UAE GovTrust, a controlled English-Arabic retrieval diagnostic comprising 22 authored service summaries and 50 scenarios with 110 query variants. Twelve methods separate lexical and dense ranking, rank fusion, scope filtering, a multilingual cross-encoder, and direct service lookup. On 72 matched answerable queries with bilingual evidence, role-filtered reciprocal rank fusion retrieves a relevant first result for 69 queries (95.8%), versus 47 (65.3%) for unfiltered fusion; filtered dense retrieval and direct lookup each achieve 68 (94.4%). With English-only evidence, excluding zero-score lexical candidates raises filtered fusion from 52 to 64 correct first hits. However, the unchanged router selects the correct action and scope for only 2 of 168 post-audit perturbations covering paraphrases, irrelevant facts, negation, and corrections. These perturbations are deliberately authored diagnostics, not held-out evaluation. Fresh-environment execution reproduces all 1,320 original ranking orders and actions. The results show that correct scope metadata can improve ranking while brittle scope inference can remove the evidence needed to answer. The study evaluates retrieval and routing, not generated answers or legal correctness; independent Arabic and domain validation remains necessary.

Fri 23 OctInformation Retrieval
The gist
Public service questions often mention many locations, but only one location actually controls the service needed. The authors studied a system called UAE GovTrust that helps find the right public service information in both English and Arabic. Their methods showed that filtering by the correct location scope greatly improved search accuracy. However, when the system had to decide how to route queries with tricky changes or corrections, it mostly failed. This means the system works well for ranking results but struggles with understanding complex query variations.
Open → 2610.32608v1

Language models struggle to update numeric data in Norwegian laws over time

norregel: a preregistered benchmark of dated numeric values in Norwegian law

Abstract: norregel asks language models for numeric values in Norwegian statutes, regulations and parliamentary decisions (NOK amounts, percentages, multiples of the National Insurance basic amount (G) and age limits), each at a stated date. The 916 items were extracted mechanically from Lovdata's open data, and each carries the dated history of its provision, so an answer can be classed as correct, outdated, wrong or uninterpretable. We report a first and a second preregistered run on the same items. In the first run (five models, listed publicly only after local analysis), H1, that outdated answers are more frequent for values that took effect after a model's cutoff, could not be tested in substance, and H2 and H3 were inconclusive; Opus 5.5 and Sonnet 4.6 answered 46.7% and 40.2% of the 880 primary items correctly. The second run was preregistered and published before any of its calls, and eight models were analysed. Its pooled primary test supported H1 under the preregistered decision rule (d = 17.8 percentage points, 95% interval [7.7, 28.9], p = 0.0001). In the preregistered sensitivity analysis S8, which counts as outdated only the most recent value that differs from the key, d was 3.9 [-3.6, 12.7] (p = 0.1754), an interval that contains 0. H1 was supported in every other preregistered sensitivity analysis, but S8 is the only one that gives both groups one target value, so we do not regard the support for H1 as robust. The share of outdated answers equal to that most recent value (H2) was 56.1% [46.6, 65.0], an interval that contains 0.5, and the pooled test of a lower share correct in Nynorsk than in Bokmål (H3) was inconclusive. Several models, among them Haiku 4.5, gave mostly uninterpretable answers. Items and keys are withheld; item IDs, per-answer classes, code and results are on Zenodo.

Wed 21 OctComputation and Language
The gist
Many legal rules change their numbers, like age limits or payments, over time, but language AI models often give outdated answers if they learned old data. The authors collected 916 numeric points from Norwegian laws with dates showing when they changed, so they could check if model answers were correct or outdated. They found that models tend to copy old numbers more often when the numbers changed after the AI’s last training date, but this tendency depends on how you count outdated answers. Some models also often gave uninterpretable replies. This benchmark helps test how well language models know current legal numbers.
Open → 2610.29207v1

Citizen data standards clash reduces data value across institutions

Data Patching

Abstract: To provide data-driven quality services to their citizens, every institution determines the acceptability of the citizen data and datafied mechanisms through institutional protocols, including preset standards and policies. However, data meeting standards at one institution might fail to meet a different institution's standards in subsequent phases of the intended citizen services due to mismatched protocols, leading to data devaluation in the citizen service ecosystem. Terming this phenomenon cross-institutional data devaluation, we investigate its causes and workarounds through interviews with 41 Bangladeshi participants. We found that traditional data auditing mechanisms cannot solely address data devaluation; hence, we draw on our findings and theory of cross-institutional AI audits to propose the Citizen-centered Cross-institutional Data Audit (CCDA). We also discuss design and policy implications of CCDA in HCI and datafied citizen services.

Wed 23 SeptHuman-Computer Interaction
The gist
Sometimes data collected by one government office doesn't match the rules or standards of another office, making the data less useful. The authors call this problem cross-institutional data devaluation. They talked to 41 people in Bangladesh to understand why this happens and found that normal data checks don’t fully solve the issue. They suggest a new way called Citizen-centered Cross-institutional Data Audit (CCDA) to help fix these mismatches and improve how citizens receive services.
Open → 2609.28777v1