We introduce GRAMMAMT, a grammaticallyaware prompting approach for machine translation that uses Interlinear Glossed Text (IGT), a common form of linguistic description providing morphological and lexical annotations for source sentences.GRAMMAMT proposes three prompting strategies: gloss-shot, chaingloss and model-gloss.All are training-free, requiring only a few examples that involve minimal effort to collect, and making them wellsuited for low-resource setups.Experiments show that GRAMMAMT enhances translation performance on open-source instruction-tuned LLMs for various low-to high-resource languages across three benchmarks: (1) the largest IGT corpus, (2) the challenging 2023 SIGMOR-PHON Shared Task data over endangered languages, and (3) even in an out-of-domain setting with FLORES.Moreover, ablation studies reveal that leveraging gloss resources could substantially boost MT performance (by over 17 BLEU points) if LLMs accurately generate or access input sentence glosses. Gloss-shotHere are some examples of {Swahili} sentences and their corresponding {English} translations: Swahili sentence: (yeye) alimwona (yeye).Gloss: 3SG -

We introduce a set of training-free ABX-style discrimination tasks to evaluate how multilingual language models represent language identity (form) and semantic content (meaning).Inspired by speech processing, these zero-shot tasks measure whether minimal differences in representation can be reliably detected.This offers a flexible and interpretable alternative to probing.Applied to XLM-R (Conneau et al., 2020a) across pretraining checkpoints and layers, we find that language discrimination declines over training and becomes concentrated in lower layers, while meaning discrimination strengthens over time and stabilizes in deeper layers.We then explore probing tasks, showing some alignment between our metrics and linguistic learning performance.Our results position ABX tasks as a lightweight framework for analyzing the structure of multilingual representations.

Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for interpreting predictions from GNNs have primarily focused on generating subgraphs that are especially relevant for a particular prediction. However, such methods are not counterfactual (CF) in nature: given a prediction, we want to understand how the prediction can be changed in order to achieve an alternative outcome. In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes. Using only edge deletions, we find that our method, CF-GNNExplainer, can generate CF explanations for the majority of instances across three widely used datasets for GNN explanations, while removing less than 3 edges on average, with at least $94%$ accuracy. This indicates that CF-GNNExplainer primarily removes edges that are crucial for the original predictions, resulting in minimal CF explanations.

Automatic text summarization has enjoyed great progress over the years and is used in numerous applications, impacting the lives of many.Despite this development, there is little research that meaningfully investigates how the current research focus in automatic summarization aligns with users' needs.To bridge this gap, we propose a survey methodology that can be used to investigate the needs of users of automatically generated summaries.Importantly, these needs are dependent on the target group.Hence, we design our survey in such a way that it can be easily adjusted to investigate different user groups.In this work we focus on university students, who make extensive use of summaries during their studies.We find that the current research directions of the automatic summarization community do not fully align with students' needs.Motivated by our findings, we present ways to mitigate this mismatch in future research on automatic summarization: we propose research directions that impact the design, the development and the evaluation of automatically generated summaries.

The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.