The study of historical languages presents unique challenges due to their complex ortho-graphic systems, fragmentary textual evidence, and the absence of standardized digital repre-sentations of text in those languages. Tack-ling these challenges needs special NLP digi-tal tools to handle phonetic transcriptions and analyze ancient texts. This work introduces ParsiPy1, an NLP toolkit designed to facili-tate the analysis of historical Persian languages by offering modules for tokenization, lemma-tization, part-of-speech tagging, phoneme-to-transliteration conversion, and word embed-ding. We demonstrate the utility of our toolkit through the processing of Parsig (Middle Per-sian) texts, highlighting its potential for ex-panding computational methods in the study of historical languages. Through this work, we contribute to the field of computational philol-ogy, offering tools that can be adapted for the broader study of ancient texts and their digital preservation.

This study introduces a novel framework for evaluating Large Language Models (LLMs) and Vision-Language Models (VLMs) in Persian, a low-resource language. We develop comprehensive datasets to assess reasoning, linguistic understanding, and multimodal capabilities. Our datasets include Persian-OCR-QA for optical character recognition, Persian-VQA for visual question answering, Persian world-image puzzle for multimodal integration, Visual-Abstraction-Reasoning for abstract reasoning, and Iran-places for visual knowledge of Iranian figures and locations. We evaluate models like GPT-4o, Claude 3.5 Sonnet, and Llama 3.2 90B Vision, revealing their strengths and weaknesses in processing Persian. This research contributes to inclusive language processing by addressing the unique challenges of low-resource language evaluation.

Language models today are widely used in education, yet their ability to tailor responses for learners with varied informational needs and knowledge backgrounds remains under-explored. To this end, we introduce ELI-Why, a benchmark of 13.4K “Why” questions to evaluate the pedagogical capabilities of language models. We then conduct two extensive human studies to assess the utility of language model-generated explanatory answers (explanations) on our benchmark, tailored to three distinct educational grades: elementary, high-school and graduate school. In our first study, human raters assume the role of an “educator” to assess model explanations’ fit to different educational grades. We find that GPT-4-generated explanations match their intended educational background only 50% of the time, compared to 79% for lay human-curated explanations. In our second study, human raters assume the role of a learner to assess if an explanation fits their own informational needs. Across all educational backgrounds, users deemed GPT-4-generated explanations 20% less suited on average to their informational needs, when compared to explanations curated by lay people. Additionally, automated evaluation metrics reveal that explanations generated across different language model families for different informational needs remain indistinguishable in their grade-level, limiting their pedagogical effectiveness.

The Pahlavi language, aka Middle Persian, is a critical part of Persian cultural and historical heritage which bridges the Old Persian and Modern Persian (Farsi). However, due to its limited digital presence and the scarcity of comprehensive linguistic resources, Pahlavi is at risk of extinction. As an early attempt to preserve this language, this study introduces a framework to translate English text into Pahlavi. Our approach combines grammar-guided term extraction with zero-shot translation, leveraging large language models (LLMs) to generate syntactically and semantically accurate Pahlavi sentences.This framework aims to preserve the Pahlavi language and serves as a model for reviving other endangered languages with similar characteristics. Finally using our framework, we generate a novel dataset of 360 expert-validated parallel English-Pahlavi texts.

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users’ needs. This paper discusses our proposed approach, Docalog, for the DialDoc-22 (MultiDoc2Dial) shared task. Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.