Short and ambiguous queries in chat interfaces like Microsoft Copilot often lead to vague or irrelevant LLM responses, increasing task completion time. Hence, we introduce a novel problem: semi-automatically enhancing such queries/prompts into specific, well-formed ones with clear intent. Unlike prompt optimization, our approach adds relevant sub-intents or constraints rather than just rewording for brevity. We propose EnhanceMyPrompt, which uses small language models (SLMs) to enrich prompts by adding sub-intents/constraints, suggesting placeholders, and recommending popular values. We also introduce metrics to measure prompt improvement, user effort, and LLM response quality. Experiments on a proprietary Microsoft Copilot and LMSYS+NQ datasets with four SLMs show effectiveness: EnhanceMyPrompt predicts user intents up to 3 turns ahead in ~23% of conversations, enabling efficient sessions. Code, prompts, data, and models for LMSYS+NQ are publicly available.

Multiple business scenarios require an automated generation of descriptive human-readable long text from structured input data, where the source is typically a high-resource language and the target is a low or medium resource language. We define the Cross-Lingual Fact to Long Text Generation (XFLT) as a novel natural language generation (NLG) task that involves generating descriptive and human-readable long text in a target language from structured input data (such as fact triples) in a source language. XFLT is challenging because of (a) hallucinatory nature of the state-of-the-art NLG models, (b) lack of good quality training data, and (c) lack of a suitable cross-lingual NLG metric. Unfortunately previous work focuses on different related problem settings (cross-lingual facts to short text or monolingual graph to text) and has made no efforts to handle hallucinations. In this paper, we contribute a novel dataset, XLALIGN with over 64,000 paragraphs across 12 different languages, and English facts. We propose a novel solution to the XFLT task which addresses these challenges by training multilingual Transformer-based encoder-decoder models with coverage prompts and grounded decoding. Further, it improves on the XFLT quality by defining task-specific reward functions and training on them using reinforcement learning. On XLALIGN, we compare this novel solution with several strong baselines using a new metric, cross-lingual PARENT. We also make our code and data publicly available https://drive.google.com/file/d/1sHgcwXKribjrm2grbs-LzXUUqXQitD2N/.

Multiple business scenarios require an automated generation of descriptive humanreadable text from structured input data.This has resulted into substantial work on fact-totext generation systems recently.Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets.Only recently, the problem of crosslingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages.However, there has been no rigorous work on the actual XF2T generation problem.We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya.We conduct an extensive study using popular Transformer-based text generation models on our extended multilingual dataset, which we call XALIGNV2.Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding.Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results (30.90 BLEU, 55.12 METEOR and 59.17 chrF++) across the twelve languages.We make our code and dataset publicly available 1 , and hope that this will help advance further research in this critical area. https://github.com/blitzprecision/ XAlignV22 A fact is a triple composed of subject, relation and object.