PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance
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Abstract (arXiv)
To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task learning over all sub-tasks in PCQA and incorporates a simple ensemble strategy to alleviate the error propagation issue in the multi-task learning by cross-validating top-k sampled Seq2Seq outputs. We benchmark the PACIFIC dataset with extensive baselines and provide comprehensive evaluations on each sub-task of PCQA.
Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). Builds on 44 of its 61 references that are in the map. First posted on arXiv 17 Oct 2022.
Builds on
44 of 61 references.
References · 44
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Citing papers per year: 2022: 2; 2023: 6; 2024: 14; 2025: 17; 2026: 6.
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- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
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- 2023Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversationscited by 41
- 2025Structured Uncertainty guided Clarification for LLM Agentscited by 41
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- 2025Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational Historycited by 10
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- 2025Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Surveycited by 6
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- 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluationcited by 5
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- 2026When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gaincited by 0
- 2026When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain21 Sep 2026
- 2026FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings7 Aug 2026
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- 2026Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement25 Feb 2026
- 2026When Tables Go Crazy: Evaluating Multimodal Models on French Financial Documents11 Feb 2026
- 2026Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings7 Feb 2026
- 2025Learning Steerable Clarification Policies with Collaborative Self-play3 Dec 2025
- 2025Structured Uncertainty guided Clarification for LLM Agents11 Nov 2025
- 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation8 Oct 2025
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- 2025PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion8 Aug 2025
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- 2025Improving Instruct Models for Free: A Study on Partial Adaptation15 Apr 2025
- 2025Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action28 Mar 2025
- 2025ECLAIR: Enhanced Clarification for Interactive Responses19 Mar 2025
- 2025Synthetic Clarification and Correction Dialogues about Data-Centric Tasks -- A Teacher-Student Approach18 Mar 2025
- 2025Bridging Language Models and Financial Analysis14 Mar 2025
- 2025Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History7 Mar 2025
- 2025Zero-Shot and Efficient Clarification Need Prediction in Conversational Search28 Feb 2025
- 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond10 Jan 2025
- 2024SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis28 Oct 2024
- 2024A Survey of Conversational Search21 Oct 2024
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- 2024PCQPR: Proactive Conversational Question Planning with Reflection2 Oct 2024
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- 2024Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training31 May 2024
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- 2024A Survey of Large Language Models in Finance (FinLLMs)4 Feb 2024
- 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAG18 Jan 2024
- 2023Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents1 Nov 2023
- 2023Alexpaca: Learning Factual Clarification Question Generation Without Examples17 Oct 2023
- 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration23 May 2023
- 2023Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations17 May 2023
- 2023Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question5 May 2023
- 2023A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects4 May 2023
- 2022A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions27 Dec 2022
- 2022NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering7 Nov 2022
- 2023Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agentscited by 77
- 2024Ask-before-Plan: Proactive Language Agents for Real-World Planningcited by 60
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- 2025Structured Uncertainty guided Clarification for LLM Agentscited by 41
- 2024STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agentscited by 17
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- 2025Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Surveycited by 6
- 2026Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvementcited by 1
- 2026When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gaincited by 0
- 2026When and How Should an Agent Clarify? CIGAsk: Teaching LLMs to Clarify via Counterfactual Information Gain21 Sep 2026
- 2026Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement25 Feb 2026
- 2025Structured Uncertainty guided Clarification for LLM Agents11 Nov 2025
- 2025Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey18 May 2025
- 2025Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action28 Mar 2025
- 2024Ask-before-Plan: Proactive Language Agents for Real-World Planning18 Jun 2024
- 2024STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents20 May 2024
- 2024On the Multi-turn Instruction Following for Conversational Web Agents23 Feb 2024
- 2023Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents1 Nov 2023
- 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
- 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAG18 Jan 2024
- 2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
- 2023Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaborationcited by 116
- 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 79
- 2023A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospectscited by 66
- 2024Towards Human-centered Proactive Conversational Agentscited by 49
- 2023Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversationscited by 41
- 2024A Survey of Conversational Searchcited by 22
- 2025Teaching Language Models To Gather Information Proactivelycited by 14
- 2024cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Paperscited by 11
- 2023Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Questioncited by 10
- 2025Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational Historycited by 10
- 2024SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysiscited by 8
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- 2026FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings7 Aug 2026
- 2026Clarification Is Not Enough: Post-Clarification Answering Remains the Bottleneck in Multi-Turn QA24 May 2026
- 2026Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings7 Feb 2026
- 2025Learning Steerable Clarification Policies with Collaborative Self-play3 Dec 2025
- 2025Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation8 Oct 2025
- 2025Collaborative and Proactive Management of Task-Oriented Conversations26 Sep 2025
- 2025Teaching Language Models To Gather Information Proactively28 Jul 2025
- 2025DialogXpert: Driving Intelligent and Emotion-Aware Conversations through Online Value-Based Reinforcement Learning with LLM Priors23 May 2025
- 2025Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts19 Apr 2025
- 2025Improving Instruct Models for Free: A Study on Partial Adaptation15 Apr 2025
- 2025ECLAIR: Enhanced Clarification for Interactive Responses19 Mar 2025
- 2025Bridging Language Models and Financial Analysis14 Mar 2025
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