发表机构
Charles Sturt University; Chittagong University of Engineering and Technology; Bangladesh Army University of Science and Technology（查尔斯斯特大学; 吉大港工程技术大学; 孟加拉国陆军理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究以孟加拉语新闻标题生成任务为对象，对比Gemini、Llama、GPT-4o，发现选定导语比全文输入更优，跨语言提示结合上下文丰富效果好，少样本提示对Gemini增益显著，为低资源多语言LLM应用提供启示。
AI 中文摘要
大语言模型（LLMs）在文本生成任务中表现出强大性能，但其在标题生成任务上的效果仍对输入上下文的选择与呈现方式敏感。本研究将孟加拉语新闻标题生成视为需要从长篇文章中有效选择和呈现显著上下文信息的文档级生成任务，使用Gemini-2.0-Flash、Llama-3.3-70B和GPT-4o，系统研究了上下文选择、提示策略及上下文学习（即少样本）对标题生成质量的影响。实验表明，提供完整文章未必能提升性能，相反，使用文章选定的导语段落可维持甚至在部分情况下提升标题生成质量。我们进一步比较了孟加拉语原生提示（BNaP）与跨语言提示（XLP），并考察二者与融入辅助上下文线索的上下文丰富提示模板的交互作用。结果显示，提示策略对生成质量有显著影响：XLP通常表现更强，尤其与上下文丰富结合时，但其优势依赖于模型；此外，少样本提示可显著提升Gemini的性能，大部分增益来自单个示例，而Llama从额外示例中获益有限。总体而言，本研究表明有效的孟加拉语新闻标题生成更依赖上下文相关性与提示设计，而非增加输入长度，为多语言及低资源LLM应用提供了实用见解。
英文摘要
Large language models (LLMs) have shown strong performance in text generation tasks, yet their effectiveness on headline generation remains sensitive to how input context is selected and presented. In this work, we investigate Bengali news headline generation as a document-level generation task that requires effective selection and presentation of salient contextual information from long-form articles. Using Gemini-2.0-Flash, Llama-3.3-70B, and GPT-4o, we systematically study the effects of context selection, prompting strategies, and in-context learning (i.e., few-shot) on the quality of headline generation. Our experiments show that providing the full article does not necessarily improve performance; instead, using selected lead paragraphs of the article can maintain, and in some cases improve, headline generation quality. We further compare Bengali Native Prompting (BNaP) and Cross-Lingual Prompting (XLP), and examine how each interacts with context-enriched prompt templates incorporating auxiliary contextual cues. Results demonstrate that prompting strategies substantially influence generation quality: XLP often yields stronger performance, particularly when combined with contextual enrichment, but its benefits are model-dependent. Additionally, few-shot prompting substantially improves Gemini, with most of the gain obtained from a single demonstration, whereas Llama shows limited benefit from additional examples. Overall, our findings highlight that effective Bengali news headline generation depends more on context relevance and prompt design than on increasing input length, offering practical insights for multilingual and low-resource LLM applications.
Comments11 pages
