arXiv Computation and Language
Sep 1

WildSEEK: Evaluating Language Models for Information-Seeking

WildSEEK is a new dataset of 3,000 real user information‑seeking queries, manually annotated for risk‑sensitive domains and whether the query is factoid or analytical. The accompanying evaluation framework tests LLM responses against four failure criteria—sycophantic behavior, overreliance, a default US‑centric perspective, and poor handling of vulnerable populations—finding higher failure rates for analytical queries. The authors also train classifiers on WildSEEK to analyze over 1.8 million realistic queries, revealing that more than a third are high‑risk and often analytical.

By Tanise Ceron, Joachim Baumann, Elisa Bassignana, Berat Cabuk, Dirk Hovy, Debora Nozza
arXiv AI
Sep 3

PolERo: Studying Political Evasion in Romanian

PolERo presents a new dataset of 3,574 Romanian question‑answer pairs from presidential transcripts, annotated for political evasion using a two‑level taxonomy of response clarity and fine‑grained evasion strategies. The study evaluates various classification methods—including TF‑IDF baselines, fine‑tuned encoders, a sliding‑window encoder, and zero/few‑shot LLM prompting—under matched conditions. Cross‑lingual transfer experiments via joint bilingual training and machine‑translation augmentation reveal that fine‑tuned encoders perform competitively, transfer is asymmetric, and ambivalent evasion categories with pragmatic cues remain the most challenging across all models.

By Gabriel Stefan, Sergiu Nisioi
arXiv AI
Sep 17

From a River in Gilead to the Inference Distributions of Large Language Models: Covert Dialect Bias and Linguistic Profiling at Scale

The paper investigates covert dialect bias in large language models (LLMs) by analyzing how internal probability distributions associate different English varieties—Standard American English, African American Vernacular English, Nigerian Standard English, and Nigerian Pidgin—with housing-related adjectives. Using 260 meaning‑matched sentence quadruples and log‑probability scoring across ten open‑weight LLMs, the study finds that AAVE and NP are consistently linked to more negative adjectives than SAE, with NP experiencing the greatest penalty. The bias varies by context and stereotype cluster, and Nigerian Standard English shows a context‑dependent shift, being favored in formal tenant screening but penalized in more socially proximate scenarios.

By Chowdhury Mohammad Abdullah, Rita Orji
arXiv Computation and Language
Sep 2

Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

arXiv:2609.00319v1 Announce Type: cross Abstract: Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compos...

By Phuong Anh Nguyen, Jill Noorily, Matthew Flathers, Haruka Notsu, Laura Ospina-Pinillos, Tommy Nguyen, Samantha Clark, Aoife Keane, Grace Thompson, John Torous