We study how large language models (LLMs) perform at assigning ACL Rolling Review (ARR) areas from paper titles/abstracts. Using 558 papers (ACL/EACL/NAACL, 2020 to 2025), we compare multiple LLMs and prompting schemes (zero/few-shot; with/without ARR keywords; each-category variants) and analyze per-area scores, error overlap, and confusion matrices. One-shot prompting (with OpenAI-gpt-oss-20b) tends to perform best, while injecting ARR keywords often lowers accuracy. Task-bounded areas (e.g., MT, IE, QA, Summarization) are predicted more reliably, whereas broad, cross-cutting labels (e.g., Resources and Evaluation, NLP Applications) are frequently conflated, indicating taxonomy ambiguity rather than solely model limitations. We recommend hierarchical or primary-plus-secondary labels to reduce ambiguity and improve reviewer matching. Our dataset, methods, and findings offer a reproducible baseline for area selection support in ACL workflows.

Robust, faithful, and harm-free pronoun use for individuals is an important goal for language model development as their use increases, but prior work tends to study only one or two of these characteristics at a time. To measure progress towards the combined goal, we introduce the task of pronoun fidelity: Given a context introducing a co-referring entity and pronoun, the task is to reuse the correct pronoun later. We present RUFF, a carefully designed dataset of over 5 million instances to measure robust pronoun fidelity in English, and we evaluate 37 model variants from nine popular families, across architectures (encoder-only, decoder-only, and encoder-decoder) and scales (11M-70B parameters). When an individual is introduced with a pronoun, models can mostly faithfully reuse this pronoun in the next sentence, but they are significantly worse with she/her/her, singular they, and neopronouns. Moreover, models are easily distracted by non-adversarial sentences discussing other people; even one sentence with a distractor pronoun causes accuracy to drop on average by 34 percentage points. Our results show that pronoun fidelity is not robust, in a simple, naturalistic setting where humans achieve nearly 100% accuracy. We encourage researchers to bridge the gaps we find and to carefully evaluate reasoning in settings where superficial repetition might inflate perceptions of model performance.

While measuring bias and robustness in coreference resolution are important goals, such measurements are only as good as the tools we use to measure them. Winogender Schemas (Rudinger et al., 2018) are an influential dataset proposed to evaluate gender bias in coreference resolution, but a closer look reveals issues with the data that compromise its use for reliable evaluation, including treating different pronominal forms as equivalent, violations of template constraints, and typographical errors. We identify these issues and fix them, contributing a new dataset: WinoPron. Using WinoPron, we evaluate two state-of-the-art supervised coreference resolution systems, SpanBERT, and five sizes of FLAN-T5, and demonstrate that accusative pronouns are harder to resolve for all models. We also propose a new method to evaluate pronominal bias in coreference resolution that goes beyond the binary. With this method, we also show that bias characteristics vary not just across pronoun sets (e.g., he vs. she), but also across surface forms of those sets (e.g., him vs. his).