arXiv AI By Srikar Kashyap Pulipaka

PSK at WMT 2026 MIST: Task-Specialized QLoRA Adapters for Multilingual Summarization and Question Answering

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The PSK submission to the WMT 2026 Multilingual Instruction Shared Task employs a 3.35B‑parameter Tiny Aya Global model enhanced with three QLoRA adapters, each dedicated to a specific task: multilingual summarization, passage‑based question answering, and filtered standalone question answering. The summarization adapter is trained on multilingual document‑summary pairs, including scientific papers with author‑written abstracts, and outperforms a multitask adapter trained solely on organizer data on a held‑out split. For open question answering, results vary with answer length and evaluation method, prompting the submission of three systems that share the same context and summarization adapters but differ in their open‑QA adapters.

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arXiv AI
Sep 3

Evaluating the Evaluator: Summarization Metrics and LLM-Judges beyond English

The paper introduces BASSE, a multilingual meta‑evaluation dataset containing 2,040 human‑rated abstractive summaries produced manually or by five LLMs with four prompts. Annotators scored each summary on coherence, consistency, fluency, relevance, and 5W1H using a 5‑point Likert scale. Benchmarking shows proprietary LLM‑judge models best align with human judgments, followed by criteria‑specific automatic metrics, while open‑source judge LLMs perform poorly.

By Jeremy Barnes, Naiara Perez, Alba Bonet-Jover, Bego\~na Altuna