Tool retrieval is a necessary first step for tool-augmented LLMs once the set of available tools grows to thousands or more, and itscentral difficulty stems from two gaps. A user query states what the user wants to accomplish in task-centric language, whereas tooldocumentation describes functionality, parameters, and calling conditions in function-centric terms. This low lexical overlap produces aQuery-Documentation Mismatch. Complex queries also bundle several functional needs at once, while a single tool usually covers only one,producing a Multi-Intent Query Mismatch. We present ToolFuse, a training-free framework that addresses each gap on a different side ofretrieval. On the tool side, Complementarity-based Semantic Fusion treats tool documentation and LLM-generated synthetic intent as adocumentation view and an intent view of the same tool, encodes them separately, and assigns each tool a fusion weight from the geometriccomplementarity between its two views in vector space, without fine-tuning the frozen dense encoder. On the query side, QueryDecomposition splits a multi-intent query into focused sub-queries, retrieves for each, and aggregates the results with rank-aware round-robin. On ToolBench, the full ToolFuse raises the Per-Split average NDCG@5 of Qwen3-Embedding-0.6B from 64.72 to 75.49, with consistentgains across three additional dense encoder backbones.

Using large language models, this paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of (1) the presence of a datastore consisting of a limited number of parallel translation examples, (2) the inherent capabilities of LLMs like GPT-3.5, and (3) a word-level translation dictionary. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLM as universal translators for extremely low-resourced languages. Our methodology hinges on utilizing LLMs as language compilers for selected language pairs, hypothesizing that they could internalize syntactic structures to facilitate accurate translation. We introduce three techniques: KNN-Prompting with Retrieved Prompting Context, Chain-of-Thought Prompting, and Learning-from-Mistakes Prompting, with the last method addressing past errors. The evaluation results suggest that, even with limited corpora, LLMs, when paired with proper prompting, can effectively translate extremely low-resource languages.

Creating high-quality True-False (TF) multiple-choice questions (MCQs), with accurate distractors, is a challenging and time-consuming task in education. This paper introduces True-False Distractor Generation (TFDG), a pipeline that leverages pre-trained language models and sentence retrieval techniques to automate the generation of TF-type MCQ distractors. Furthermore, the evaluation of generated TF questions presents a challenge. Traditional metrics like BLEU and ROUGE are unsuitable for this task. To address this, we propose a new evaluation metric called Retrieval-based Accuracy Differential (RAD). RAD assesses the discriminative power of TF questions by comparing model accuracy with and without access to reference texts. It quantitatively evaluates how well questions differentiate between students with varying knowledge levels. This research benefits educators and assessment developers, facilitating the efficient automatic generation of high-quality TF-type MCQs and their reliable evaluation.