ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval

• URL: http://arxiv.org/abs/2505.08130v1
• Date: Tue, 13 May 2025 00:01:03 GMT
• Title: ALOHA: Empowering Multilingual Agent for University Orientation with Hierarchical Retrieval
• Authors: Mingxu Tao, Bowen Tang, Mingxuan Ma, Yining Zhang, Hourun Li, Feifan Wen, Hao Ma, Jia Yang,
• Abstract summary: We introduce ALOHA, a multilingual agent enhanced by hierarchical retrieval for university orientation.<n>The system has been deployed and has provided service for more than 12,000 people.
• Score: 7.016945185385475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of Large Language Models~(LLMs) revolutionizes information retrieval, allowing users to obtain required answers through complex instructions within conversations. However, publicly available services remain inadequate in addressing the needs of faculty and students to search campus-specific information. It is primarily due to the LLM's lack of domain-specific knowledge and the limitation of search engines in supporting multilingual and timely scenarios. To tackle these challenges, we introduce ALOHA, a multilingual agent enhanced by hierarchical retrieval for university orientation. We also integrate external APIs into the front-end interface to provide interactive service. The human evaluation and case study show our proposed system has strong capabilities to yield correct, timely, and user-friendly responses to the queries in multiple languages, surpassing commercial chatbots and search engines. The system has been deployed and has provided service for more than 12,000 people.

Related papers

• Text-to-SPARQL Goes Beyond English: Multilingual Question Answering Over Knowledge Graphs through Human-Inspired Reasoning [51.203811759364925]
  mKGQAgent breaks down the task of converting natural language questions into SPARQL queries into modular, interpretable subtasks.<n> Evaluated on the DBpedia- and Corporate-based KGQA benchmarks within the Text2SPARQL challenge 2025, our approach took first place among the other participants.
  arXiv  Detail & Related papers  (2025-07-22T19:23:03Z)
• Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support [2.1763238176533037]
  The system aims to provide fast and personalized responses, while reducing workload of university staff.<n>We employ retrieval-augmented generation to ground answers in university resources and to provide users with verifiable, contextually relevant information.
  arXiv  Detail & Related papers  (2025-07-18T14:09:45Z)
• Query Understanding in LLM-based Conversational Information Seeking [12.823070040084943]
  Large Language Models (LLMs) enhance this process by interpreting nuanced language and adapting dynamically.<n>This tutorial explores advanced techniques to enhance query understanding in LLM-based CIS systems.
  arXiv  Detail & Related papers  (2025-04-08T18:04:43Z)
• mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval [61.17793165194077]
  We introduce mFollowIR, a benchmark for measuring instruction-following ability in retrieval models.<n>We present results for both multilingual (XX-XX) and cross-lingual (En-XX) performance.<n>We see strong cross-lingual performance with English-based retrievers that trained using instructions, but find a notable drop in performance in the multilingual setting.
  arXiv  Detail & Related papers  (2025-01-31T16:24:46Z)
• A Survey of Conversational Search [44.09402706387407]
  We explore the recent advancements and potential future directions in conversational search. We highlight the integration of large language models (LLMs) in enhancing these systems. We provide insights into real-world applications and robust evaluations of current conversational search systems.
  arXiv  Detail & Related papers  (2024-10-21T01:54:46Z)
• MST5 -- Multilingual Question Answering over Knowledge Graphs [1.6470999044938401]
  Knowledge Graph Question Answering (KGQA) simplifies querying vast amounts of knowledge stored in a graph-based model using natural language. Existing multilingual KGQA systems face challenges in achieving performance comparable to English systems. We propose a simplified approach to enhance multilingual KGQA systems by incorporating linguistic context and entity information directly into the processing pipeline of a language model.
  arXiv  Detail & Related papers  (2024-07-08T15:37:51Z)
• CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval [52.134133938779776]
  We present CLARINET, a system that asks informative clarification questions by choosing questions whose answers would maximize certainty in the correct candidate. Our approach works by augmenting a large language model (LLM) to condition on a retrieval distribution, finetuning end-to-end to generate the question that would have maximized the rank of the true candidate at each turn.
  arXiv  Detail & Related papers  (2024-04-28T18:21:31Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Interpreting User Requests in the Context of Natural Language Standing Instructions [89.12540932734476]
  We develop NLSI, a language-to-program dataset consisting of over 2.4K dialogues spanning 17 domains. A key challenge in NLSI is to identify which subset of the standing instructions is applicable to a given dialogue.
  arXiv  Detail & Related papers  (2023-11-16T11:19:26Z)
• Cache & Distil: Optimising API Calls to Large Language Models [82.32065572907125]
  Large-scale deployment of generative AI tools often depends on costly API calls to a Large Language Model (LLM) to fulfil user queries. To curtail the frequency of these calls, one can employ a smaller language model -- a student. This student gradually gains proficiency in independently handling an increasing number of user requests.
  arXiv  Detail & Related papers  (2023-10-20T15:01:55Z)
• Query Understanding in the Age of Large Language Models [6.630482733703617]
  We describe a generic framework for interactive query-rewriting using large-language models (LLM) A key aspect of our framework is the ability of the rewriter to fully specify the machine intent by the search engine in natural language. We detail the concept, backed by initial experiments, along with open questions for this interactive query understanding framework.
  arXiv  Detail & Related papers  (2023-06-28T08:24:14Z)
• Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language [121.56329458876655]
  We introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld. We propose the "Asking for Knowledge" (AFK) agent, which learns to generate language commands to query for meaningful knowledge.
  arXiv  Detail & Related papers  (2022-05-12T14:20:31Z)
• Efficient Deployment of Conversational Natural Language Interfaces over Databases [45.52672694140881]
  We propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models. Our system allows one to generate conversational multi-term data, where multiple turns define a dialogue session.
  arXiv  Detail & Related papers  (2020-05-31T19:16:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.