Dialectal Arabic dialogue translation requires modeling both regional variation and conversational context under uneven supervision. We introduce LahjaMT, a compact system for translating English dialogue into thirteen country-level Arabic varieties. LahjaMT adapts the 3.12B-parameter NileChat-3B-Base model with LoRA, conditions translation on dialogue history and metadata, and routes each dialect to a checkpoint–prompt expert selected on the held-out selection set. The constrained system shares one backbone across all varieties; the unconstrained system changes only the Libyan and Sudanese routes, replacing them with specialists trained on auxiliary data. LahjaMT ranked second in the constrained track with 28.30 spBLEU and third in the unconstrained track with 28.54 spBLEU.

We present HalluSearch, a multilingual pipeline designed to detect fabricated text spans in Large Language Model (LLM) outputs as part of Mu-SHROOM. HalluSearch couples retrieval-augmented verification with fine-grained factual splitting to identify and localize hallucinations in 14 different languages. Empirical evaluations show that HalluSearch performs competitively, placing fourth in both English (within the top 10%) and Czech. While the system’s retrieval-based strategy generally proves robust, it faces challenges in languages with limited online coverage, underscoring the need for further research to ensure consistent hallucination detection across diverse linguistic contexts.