Natural language explanations (NLEs) are vital for elucidating the reasoning behind large language model (LLM) decisions. Many techniques have been developed to generate NLEs using LLMs. However, like humans, LLMs might not always produce optimal NLEs on first attempt. Inspired by human learning processes, we introduce Cross-Refine, which employs role modeling by deploying two LLMs as generator and critic, respectively. The generator outputs a first NLE and then refines this initial explanation using feedback and suggestions provided by the critic. Cross-Refine does not require any supervised training data or additional training. We validate Cross-Refine across three NLP tasks using three state-of-the-art open-source LLMs through automatic and human evaluation. We select Self-Refine (Madaan et al., 2023) as the baseline, which only utilizes self-feedback to refine the explanations. Our findings from automatic evaluation and a user study indicate that Cross-Refine outperforms Self-Refine. Meanwhile, Cross-Refine can perform effectively with less powerful LLMs, whereas Self-Refine only yields strong results with ChatGPT. Additionally, we conduct an ablation study to assess the importance of feedback and suggestions. Both of them play an important role in refining explanations. We further evaluate Cross-Refine on a bilingual dataset in English and German.

Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models.However, a comparison of various generation strategies for low-resource language settings is lacking.While various prompting strategies have been proposed-such as demonstrations, labelbased summaries, and self-revision-their comparative effectiveness remains unclear, especially for low-resource languages.In this paper, we systematically evaluate the performance of these generation strategies and their combinations across 11 typologically diverse languages, including several extremely low-resource ones.Using three NLP tasks and four open-source LLMs, we assess downstream model performance on generated versus gold-standard data.Our results show that strategic combinations of generation methods-particularly targetlanguage demonstrations with LLM-based revisions-yield strong performance, narrowing the gap with real data to as little as 5% in some settings.We also find that smart prompting techniques can reduce the advantage of larger LLMs, highlighting efficient generation strategies for synthetic data generation in lowresource scenarios with smaller models.

We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response.We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as several neural architectures, including feed-forward, recurrent and convolutional neural models with different types of embeddings, context and attention mechanism.The best performance was achieved with a "Divide & Merge" architecture presented in the paper, using trainable GloVe embeddings and a structured dialogue history.This model learns from the current utterance and the preceding context separately and then combines the two generated representations.Average accuracy of 10fold cross-validation is 79.8%, F-score 71.8%.