In recent years, pre-trained large language models (LLMs) have become a cornerstone for automatically generating answers in question-and-answer (Q A) communities, significantly reducing user wait times and improving response quality. However, these models require substantial computational resources and are prone to generating hallucinated or unreliable content. To overcome these limitations, we propose an advanced expert-oriented Retrieval-Augmented Generation (RAG) framework as a cost-effective and reliable alternative. Central to our approach is a user-aware question entailment recognition module, which leverages user modeling to identify archived questions with answers that fully or partially address the user’s new query. This user modeling significantly improves retrieval relevance, resulting in reduced hallucination and enhanced answer quality. The framework synthesizes expert-written answers from similar questions to generate unified responses. Experimental results on the CQADupStack and SE-PQA datasets show the superiority of our user-aware approach over its user-agnostic counterpart, with ROUGE-1 gains of 3.6% and 0.9%. Both human and AI evaluations confirm the effectiveness of incorporating user modeling in minimizing hallucination and delivering contextually appropriate answers, demonstrating its potential for real-world Q A systems. The code and data are available on a GitHub repository at https://anonymous.4open.science/r/User-Oriented-RAG-CQA.

While the popularity of large, versatile language models like ChatGPT continues to rise, the landscape shifts when considering open-source models tailored to specific domains. Moreover, many areas, such as clinical documents, suffer from a scarcity of training data, often amounting to only a few hundred instances. Additionally, in certain settings, such as hospitals, cloud-based solutions pose privacy concerns, necessitating the deployment of language models on traditional hardware, such as single GPUs or powerful CPUs. To address these complexities, we conduct extensive experiments on both clinical entity detection and relation extraction in clinical documents using 1B parameter models. Our study delves into traditional fine-tuning, continuous pre-training in the medical domain, and instruction-tuning methods, providing valuable insights into their effectiveness in a multilingual setting. Our results underscore the importance of domain-specific models and pre-training for clinical natural language processing tasks. Furthermore, data augmentation using cross-lingual information improves performance in most cases, highlighting the potential for multilingual enhancements.

Low-resource languages and computational expenses pose significant challenges in the domain of large language models (LLMs). Currently, researchers are actively involved in various efforts to tackle these challenges. Cross-lingual natural language processing (NLP) remains one of the most promising strategies to address these issues. In this paper, we introduce a novel approach that utilizes adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models, with potential applications in cross-lingual tasks. The study encompasses five different languages, including both Latin and non-Latin ones, in the context of two fundamental tasks in natural language understanding: intent detection and slot filling. The results primarily show that our current approach excels in zero-shot scenarios for Latin languages like Spanish. However, it encounters limitations when applied to languages distant from English, such as Thai and Persian. This highlights that while our approach effectively reduces the effect of language-specific information on the core meaning, it performs better for Latin languages that share language-specific nuances with English, as certain characteristics persist in the overall meaning within embeddings.