Teaching Language Models To Gather Information Proactively

• URL: http://arxiv.org/abs/2507.21389v1
• Date: Mon, 28 Jul 2025 23:50:09 GMT
• Title: Teaching Language Models To Gather Information Proactively
• Authors: Tenghao Huang, Sihao Chen, Muhao Chen, Jonathan May, Longqi Yang, Mengting Wan, Pei Zhou,
• Abstract summary: Large language models (LLMs) are increasingly expected to function as collaborative partners.<n>In this work, we introduce a new task paradigm: proactive information gathering.<n>We design a scalable framework that generates partially specified, real-world tasks, masking key information.<n>Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information.
• Score: 53.85419549904644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are increasingly expected to function as collaborative partners, engaging in back-and-forth dialogue to solve complex, ambiguous problems. However, current LLMs often falter in real-world settings, defaulting to passive responses or narrow clarifications when faced with incomplete or under-specified prompts, falling short of proactively gathering the missing information that is crucial for high-quality solutions. In this work, we introduce a new task paradigm: proactive information gathering, where LLMs must identify gaps in the provided context and strategically elicit implicit user knowledge through targeted questions. To systematically study and train this capability, we design a scalable framework that generates partially specified, real-world tasks, masking key information and simulating authentic ambiguity. Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information -- such as hidden domain expertise or fine-grained requirements -- that would otherwise remain unspoken. Experiments demonstrate that our trained Qwen-2.5-7B model significantly outperforms o3-mini by 18% on automatic evaluation metrics. More importantly, human evaluation reveals that clarification questions and final outlines generated by our model are favored by human annotators by 42% and 28% respectively. Together, these results highlight the value of proactive clarification in elevating LLMs from passive text generators to genuinely collaborative thought partners.

Related papers

• From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information? [34.959850282872594]
  We present AR-Bench, a novel benchmark designed explicitly to evaluate an LLM's active reasoning skills.<n>AR-Bench comprises three task families-detective cases, situation puzzles, and guessing numbers.<n> Empirical evaluation on AR-Bench demonstrates that contemporary LLMs exhibit pronounced difficulties with active reasoning.
  arXiv  Detail & Related papers  (2025-06-09T23:56:41Z)
• Evaluating LLM-corrupted Crowdsourcing Data Without Ground Truth [21.672923905771576]
  Large language models (LLMs) by crowdsourcing workers pose a challenge to datasets intended to reflect human input.<n>We propose a training-free scoring mechanism with theoretical guarantees under a crowdsourcing model that accounts for LLM collusion.
  arXiv  Detail & Related papers  (2025-06-08T04:38:39Z)
• Adaptive Elicitation of Latent Information Using Natural Language [6.162198958758635]
  We propose an adaptive elicitation framework that actively reduces uncertainty on the latent entity.<n>Our framework adopts a predictive view of uncertainty, using a meta-learned language model to simulate future observations.<n>In experiments on the 20 questions game, dynamic opinion polling, and adaptive student assessment, our method consistently outperforms baselines in identifying critical unknowns.
  arXiv  Detail & Related papers  (2025-04-05T15:18:55Z)
• Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
  Large Language Models (LLMs) are increasingly recognized for their practical applications. Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs. By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models [55.332004960574004]
  Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established.<n>This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt.<n>We propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty.
  arXiv  Detail & Related papers  (2024-07-20T11:19:58Z)
• MACAROON: Training Vision-Language Models To Be Your Engaged Partners [95.32771929749514]
  Large vision-language models (LVLMs) generate detailed responses even when questions are ambiguous or unlabeled. In this study, we aim to shift LVLMs from passive answer providers to proactive engaged partners. We introduce MACAROON, self-iMaginAtion for ContrAstive pReference OptimizatiON, which instructs LVLMs to autonomously generate contrastive response pairs for unlabeled questions.
  arXiv  Detail & Related papers  (2024-06-20T09:27:33Z)
• Empowering Language Models with Active Inquiry for Deeper Understanding [31.11672018840381]
  We introduce LaMAI (Language Model with Active Inquiry), designed to endow large language models with interactive engagement. LaMAI uses active learning techniques to raise the most informative questions, fostering a dynamic bidirectional dialogue. Our empirical studies, across a variety of complex datasets, demonstrate the effectiveness of LaMAI.
  arXiv  Detail & Related papers  (2024-02-06T05:24:16Z)
• 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)
• Re-Reading Improves Reasoning in Large Language Models [87.46256176508376]
  We introduce a simple, yet general and effective prompting method, Re2, to enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs) Unlike most thought-eliciting prompting methods, such as Chain-of-Thought (CoT), Re2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. We evaluate Re2 on extensive reasoning benchmarks across 14 datasets, spanning 112 experiments, to validate its effectiveness and generality.
  arXiv  Detail & Related papers  (2023-09-12T14:36:23Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis [4.055489363682198]
  Large language models (LLMs) offer significant promise as a knowledge source for task learning. Prompt engineering has been shown to be effective for eliciting knowledge from an LLM, but alone it is insufficient for acquiring relevant, situationally grounded knowledge for an embodied agent learning novel tasks. We describe a cognitive-agent approach, STARS, that extends and complements prompt engineering, mitigating its limitations and thus enabling an agent to acquire new task knowledge matched to its native language capabilities, embodiment, environment, and user preferences.
  arXiv  Detail & Related papers  (2023-06-11T20:50:14Z)

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.