Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages

This paper proposes a framework to address the issue of data scarcity in Document-Grounded Dialogue Systems(DGDS). Our model leverages high-resource languages to enhance the capability of dialogue generation in low-resource languages. Specifically, We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) including adversarial training retrieval (Retriever and Re-ranker), and Fid (fusion-in-decoder) generator. To further leverage high-resource language, we also propose an innovative architecture to conduct alignment across different languages with translated training. Extensive experiment results demonstrate the effectiveness of our model and we achieved 4th place in the DialDoc 2023 Competition. Therefore, CLEM can serve as a solution to resource scarcity in DGDS and provide useful guidance for multi-lingual alignment tasks

A Multilingual Chat System with Image Presentation for Detecting Mistranslation

We have designed and developed a multilingual chat system, MCHI (Multilingual Chat with Hint Images), which is based on machine translation and equipped with a presentation function of images related to the contents of the messages by utterers so that listeners are able to notice mistranslation. MCHI accepts English, French, Chinese, Japanese, Korean and Vietnamese languages. It uses the Google API to retrieve related images from the image posting site Flickr. As a result of evaluation experiment, we have observed that participants detected the mismatch of a translated message with its related image. According to the answers of participants for a questionnaire, it turned out that the usability of the MCHI system is good enough though the related images are not satisfactory

The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie

In the digital era, the integration of artificial intelligence (AI) in education has ushered in transformative changes, redefining teaching methodologies, curriculum planning, and student engagement. This review paper delves deep into the rapidly evolving landscape of digital education by contrasting the capabilities and impact of OpenAI's pioneering text generation tools like Bing Chat, Bard, Ernie with a keen focus on the novel ChatGPT. Grounded in a typology that views education through the lenses of system, process, and result, the paper navigates the multifaceted applications of AI. From decentralizing global education and personalizing curriculums to digitally documenting competence-based outcomes, AI stands at the forefront of educational modernization. Highlighting ChatGPT's meteoric rise to one million users in just five days, the study underscores its role in democratizing education, fostering autodidacticism, and magnifying student engagement. However, with such transformative power comes the potential for misuse, as text-generation tools can inadvertently challenge academic integrity. By juxtaposing the promise and pitfalls of AI in education, this paper advocates for a harmonized synergy between AI tools and the educational community, emphasizing the urgent need for ethical guidelines, pedagogical adaptations, and strategic collaborations

Can ChatGPT pass the Vietnamese National High School Graduation Examination?

This research article highlights the potential of AI-powered chatbots in education and presents the results of using ChatGPT, a large language model, to complete the Vietnamese National High School Graduation Examination (VNHSGE). The study dataset included 30 essays in the literature test case and 1,700 multiple-choice questions designed for other subjects. The results showed that ChatGPT was able to pass the examination with an average score of 6-7, demonstrating the technology's potential to revolutionize the educational landscape. The analysis of ChatGPT performance revealed its proficiency in a range of subjects, including mathematics, English, physics, chemistry, biology, history, geography, civic education, and literature, which suggests its potential to provide effective support for learners. However, further research is needed to assess ChatGPT performance on more complex exam questions and its potential to support learners in different contexts. As technology continues to evolve and improve, we can expect to see the use of AI tools like ChatGPT become increasingly common in educational settings, ultimately enhancing the educational experience for both students and educators.Comment: 9 pages, 13 figures, 4 table

Typological parameters of genericity

Different languages employ different morphosyntactic devices for expressing genericity. And, of course, they also make use of different morphosyntactic and semantic or pragmatic cues which may contribute to the interpretation of a sentence as generic rather than episodic. [...] We will advance the strong hypo thesis that it is a fundamental property of lexical elements in natural language that they are neutral with respect to different modes of reference or non-reference. That is, we reject the idea that a certain use of a lexical element, e.g. a use which allows reference to particular spatio-temporally bounded objects in the world, should be linguistically prior to all other possible uses, e.g. to generic and non-specific uses. From this it follows that we do not consider generic uses as derived from non-generic uses as it is occasionally assumed in the literature. Rather, we regard these two possibilities of use as equivalent alternative uses of lexical elements. The typological differences to be noted therefore concern the formal and semantic relationship of generic and non-generic uses to each other; they do not pertain to the question of whether lexical elements are predetermined for one of these two uses. Even supposing we found a language where generic uses are always zero-marked and identical to lexical sterns, we would still not assume that lexical elements in this language primarily have a generic use from which the non-generic uses are derived. (Incidentally, none of the languages examined, not even Vietnamese, meets this criterion.

Modality markers and politeness strategies in British and American ambassadorial speeches: A corpus-based approach

This study investigates modality markers used as expressions of politeness in British and American ambassadorial speeches via a corpus-based method. Results of the research reflect the semantic and pragmatic perspectives of modality markers on the theories of modality and politeness. Although modality and politeness are the central topics in a wide range of studies, the two domains have been discussed separately and their relationship has not been empirically investigated. Moreover, there has been no study on modality markers in British and American discourse, nor has the use of modality markers in British and American ambassadorial speeches been examined. Therefore, this research examines the relation of modality to politeness via the use of British and American ambassadorial speeches. The research contributes to the practice of the discourse community with the analysis of modality markers as politeness strategies in ambassadorial speeches. The results of a comparative analysis of modality markers as speakers’ politeness strategies collected in ambassadorial speeches reveal that American and British ambassadors are strikingly different in their frequency of modality markers expressing particular politeness categories. American ambassadors use more modality markers expressing positive politeness strategies such as paying attention to hearers, expressing strong commitment, hedging on hearers’ positive face, expressing optimism, complimenting to mitigate the force of comments, making claims and minimising the imposition of face-threatening acts. British ambassadors, however, employ more modality markers expressing negative politeness strategies such as hedging on negative face-threatening acts, expressing hypotheses, expressing humbleness and mitigating the force of obligation. Therefore, this thesis claims that American ambassadors use more modality markers expressing positive politeness in terms of personal emotions and directness, while British ambassadors prefer modality markers expressing negative politeness such as tentativeness, indirectness and mitigation. It is noted that modality is represented in a range of syntactic structures and patterns other than single modal auxiliary verbs. In addition, since modality markers as expressions of politeness are culture-specific, the use of modality markers differs from one culture to another. Moreover, modality markers cannot totally be treated as hedges in intercultural communication since some modality markers which seem to be semantically similar between languages are actually different in their pragmatic functions among different cultures

GAIA: a benchmark for General AI Assistants

We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92\% vs. 15\% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA's philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system's capability to exhibit similar robustness as the average human does on such questions. Using GAIA's methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board available at https://huggingface.co/gaia-benchmark

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with agent personas. In this research, we investigated how users customize agent personas and their impact on interaction quality, diversity, and dynamics. To this end, we developed CloChat, an interface supporting easy and accurate customization of agent personas in LLMs. We conducted a study comparing how participants interact with CloChat and ChatGPT. The results indicate that participants formed emotional bonds with the customized agents, engaged in more dynamic dialogues, and showed interest in sustaining interactions. These findings contribute to design implications for future systems with conversational agents using LLMs.Comment: In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '24