Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions

• URL: http://arxiv.org/abs/2502.17715v1
• Date: Mon, 24 Feb 2025 23:14:59 GMT
• Title: Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions
• Authors: Zhe Liu, Taekyu Kang, Haoyu Wang, Seyed Hossein Alavi, Vered Shwartz,
• Abstract summary: We propose a method that generates diverse and informative questions based on targeting unanswered information.<n>Our method is applied to augment an existing follow-up questions dataset.<n>The experimental results demonstrate that language models fine-tuned on the augmented datasets produce follow-up questions of significantly higher quality and diversity.
• Score: 16.46658818885097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective conversational systems are expected to dynamically generate contextual follow-up questions to elicit new information while maintaining the conversation flow. While humans excel at asking diverse and informative questions by intuitively assessing both obtained and missing information, existing models often fall short of human performance on this task. To mitigate this, we propose a method that generates diverse and informative questions based on targeting unanswered information using a hypothetical LLM-generated "comprehensive answer". Our method is applied to augment an existing follow-up questions dataset. The experimental results demonstrate that language models fine-tuned on the augmented datasets produce follow-up questions of significantly higher quality and diversity. This promising approach could be effectively adopted to future work to augment information-seeking dialogues for reducing ambiguities and improving the accuracy of LLM answers.

Related papers

• AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs [53.6200736559742]
  AGENT-CQ consists of two stages: a generation stage and an evaluation stage. CrowdLLM simulates human crowdsourcing judgments to assess generated questions and answers. Experiments on the ClariQ dataset demonstrate CrowdLLM's effectiveness in evaluating question and answer quality.
  arXiv  Detail & Related papers  (2024-10-25T17:06:27Z)
• The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models [0.0]
  Large language models (LLMs) and generative AI have revolutionized natural language processing (NLP) This chapter explores the transformative potential of LLMs in automated question generation and answer assessment.
  arXiv  Detail & Related papers  (2024-10-12T15:54:53Z)
• Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain [5.229155918347321]
  Large language models (LLMs) often perform poorly in generating informative questions. We propose a method to enhance the informativeness of LLM-generated questions in 20-question game dialogues.
  arXiv  Detail & Related papers  (2024-06-25T10:44:01Z)
• 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)
• Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search [89.1772985740272]
  In mixed-initiative conversational search systems, clarifying questions are used to help users who struggle to express their intentions in a single query. We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. We collect a dataset named Melon that contains over 4k multimodal clarifying questions, enriched with over 14k images. Several analyses are conducted to understand the importance of multimodal contents during the query clarification phase.
  arXiv  Detail & Related papers  (2024-02-12T16:04:01Z)
• FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation [92.43001160060376]
  We study the factuality of large language models (LLMs) in the context of answering questions that test current world knowledge. We introduce FreshQA, a novel dynamic QA benchmark encompassing a diverse range of question and answer types. We benchmark a diverse array of both closed and open-source LLMs under a two-mode evaluation procedure that allows us to measure both correctness and hallucination. Motivated by these results, we present FreshPrompt, a simple few-shot prompting method that substantially boosts the performance of an LLM on FreshQA.
  arXiv  Detail & Related papers  (2023-10-05T00:04:12Z)
• FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation [38.78216651059955]
  We introduce the task of real-world information-seeking follow-up question generation (FQG) We construct FOLLOWUPQG, a dataset of over 3K real-world (initial question, answer, follow-up question)s collected from a forum layman providing Reddit-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills.
  arXiv  Detail & Related papers  (2023-09-10T11:58:29Z)
• What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys [63.51903260461746]
  We propose a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions.
  arXiv  Detail & Related papers  (2022-05-23T00:57:33Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)
• Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations [41.74162467619795]
  We investigate the problem of generating informative questions in information-asymmetric conversations. To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric. We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model.
  arXiv  Detail & Related papers  (2020-04-30T00:49:14Z)

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.