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LLMs struggle with multi-turn evidence gathering for reasoning, study finds

By PulseAugur Editorial · [1 sources] ·

A new paper titled "Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference" introduces the "Alien Abduction game" to test large language models' (LLMs) ability to acquire evidence and refine hypotheses in multi-turn abductive reasoning scenarios. The study found that LLMs perform better when all evidence is provided upfront rather than across multiple turns. Additionally, models that receive oracle-provided examples achieve higher success rates than those that select their own queries, though their final hypotheses are more consistent with self-selected evidence. AI

IMPACT This research highlights limitations in LLMs' ability to actively seek and integrate information over time, suggesting potential issues for complex reasoning tasks.

RANK_REASON The cluster contains an academic paper detailing research findings on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with multi-turn evidence gathering for reasoning, study finds

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The cluster contains an academic paper detailing research findings on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shahrukh Mohiuddin, Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen ·

    Don't Let Me Ask for It: LLMs Show Deficiencies in Active Multi-Turn Information Acquisition for Abductive Inference

    arXiv:2608.03388v1 Announce Type: new Abstract: Abductive reasoning requires forming hypotheses that explain observed evidence and revising them as new evidence becomes available. While large language models (LLMs) are often evaluated on whether they solve abductive reasoning tas…

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