Critical Questions Generation: Motivation and Challenges

• URL: http://arxiv.org/abs/2410.14335v1
• Date: Fri, 18 Oct 2024 09:46:38 GMT
• Title: Critical Questions Generation: Motivation and Challenges
• Authors: Blanca Calvo Figueras, Rodrigo Agerri,
• Abstract summary: We propose a new task, consisting of processing an argumentative text to generate the critical questions raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be missing. Research on CQs Generation using LLMs requires a reference dataset for large scale experimentation.
• Score: 6.0158981171030685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of Large Language Models (LLMs) has brought impressive performances on mitigation strategies against misinformation, such as counterargument generation. However, LLMs are still seriously hindered by outdated knowledge and by their tendency to generate hallucinated content. In order to circumvent these issues, we propose a new task, namely, Critical Questions Generation, consisting of processing an argumentative text to generate the critical questions (CQs) raised by it. In argumentation theory CQs are tools designed to lay bare the blind spots of an argument by pointing at the information it could be missing. Thus, instead of trying to deploy LLMs to produce knowledgeable and relevant counterarguments, we use them to question arguments, without requiring any external knowledge. Research on CQs Generation using LLMs requires a reference dataset for large scale experimentation. Thus, in this work we investigate two complementary methods to create such a resource: (i) instantiating CQs templates as defined by Walton's argumentation theory and (ii), using LLMs as CQs generators. By doing so, we contribute with a procedure to establish what is a valid CQ and conclude that, while LLMs are reasonable CQ generators, they still have a wide margin for improvement in this task.

Related papers

• SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injection [81.78173888579941]
  Large Language Models (LLMs) are considered a well-suited method to increase the quality of the question-answering functionality.<n>LLMs are trained on web data, where researchers have no control over whether the benchmark or the knowledge graph was already included in the training data.<n>This paper introduces a novel method that evaluates the quality of LLMs by generating a SPARQL query from a natural-language question.
  arXiv  Detail & Related papers  (2025-07-18T12:28:08Z)
• Decompositional Reasoning for Graph Retrieval with Large Language Models [1.034893617526558]
  Large Language Models (LLMs) excel at many NLP tasks, but struggle with multi-hop reasoning and factual consistency.<n>We propose a novel retrieval approach that integrates textual knowledge graphs into the LLM reasoning process via query decomposition.<n>Our method decomposes complex questions into sub-questions, retrieves relevant textual subgraphs, and composes a question-specific knowledge graph to guide answer generation.
  arXiv  Detail & Related papers  (2025-06-16T11:44:28Z)
• DayDreamer at CQs-Gen 2025: Generating Critical Questions through Argument Scheme Completion [3.740062413173584]
  We present our system for the Critical Questions Generation (CQs-Gen) Shared Task at ArgMining 2025.<n>Our approach leverages large language models (LLMs) with chain-of-thought prompting to generate critical questions guided by Walton's argumentation schemes.<n>Our pipeline achieves competitive performance in the final test set, showing its potential to foster critical thinking given argumentative text and detect missing or uninformed claims.
  arXiv  Detail & Related papers  (2025-05-21T14:15:49Z)
• Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering [6.745948705869626]
  We argue that prior methods do not sufficiently activate the capacities of Large Language Models (LLMs) We propose a framework called PLRH that Prompts LLMs with Rationale Heuristics for knowledge-based VQA.
  arXiv  Detail & Related papers  (2024-12-22T09:14:35Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Let's Ask AI About Their Programs: Exploring ChatGPT's Answers To Program Comprehension Questions [2.377308748205625]
  We explore the capability of the state-of-the-art LLMs in answering QLCs that are generated from code that the LLMs have created. Our results show that although the state-of-the-art LLMs can create programs and trace program execution when prompted, they easily succumb to similar errors that have previously been recorded for novice programmers.
  arXiv  Detail & Related papers  (2024-04-17T20:37:00Z)
• CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning [0.9295048974480845]
  We propose CuriousLLM, an enhancement that integrates a curiosity-driven reasoning mechanism into an LLM agent. This mechanism enables the agent to generate relevant follow-up questions, thereby guiding the information retrieval process more efficiently. Our experiments show that CuriousLLM significantly boosts LLM performance in multi-document question answering (MD-QA)
  arXiv  Detail & Related papers  (2024-04-13T20:43:46Z)
• Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs [52.42505579545893]
  Large language models (LLMs) demonstrate strong reasoning abilities when prompted to generate chain-of-thought explanations alongside answers. We propose a novel discriminative and generative CoT evaluation paradigm to assess LLMs' knowledge of reasoning and the accuracy of the generated CoT.
  arXiv  Detail & Related papers  (2024-02-17T05:22:56Z)
• keqing: knowledge-based question answering is a nature chain-of-thought mentor of LLM [27.76205400533089]
  Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. We present a novel framework to assist LLMs, such as ChatGPT, to retrieve question-related structured information on the knowledge graph. The experimental results on KBQA datasets show that Keqing can achieve competitive performance and illustrate the logic of answering each question.
  arXiv  Detail & Related papers  (2023-12-31T08:39:04Z)
• 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)
• Investigating Answerability of LLMs for Long-Form Question Answering [35.41413072729483]
  We focus on long-form question answering (LFQA) because it has several practical and impactful applications. We propose a question-generation method from abstractive summaries and show that generating follow-up questions from summaries of long documents can create a challenging setting.
  arXiv  Detail & Related papers  (2023-09-15T07:22:56Z)
• LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying [71.86163159193327]
  Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. We introduce LaGR, which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent.
  arXiv  Detail & Related papers  (2023-08-21T02:07:35Z)
• Prompting Large Language Models for Counterfactual Generation: An Empirical Study [13.506528217009507]
  Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks. We present a comprehensive evaluation framework on various types of NLU tasks, which covers all key factors in determining LLMs' capability of generating counterfactuals.
  arXiv  Detail & Related papers  (2023-05-24T06:44:32Z)
• Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks [121.74957524305283]
  This paper proposes a novel framework named textbfSearch-in-the-Chain (SearChain) for the interaction between Information Retrieval (IR) and Large Language Model (LLM) Experiments show that SearChain outperforms state-of-the-art baselines on complex knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2023-04-28T10:15:25Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.