Identifying Where Large Language Models Struggle in Answering Complex Questions

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Experiments designed by Xanh Ho et al. identify specific areas where Large Language Models struggle with complex questions, mirroring human QA processes. The study focuses on two stages: question decomposition and subproblem solving. Researchers preprocessed and expanded three multi-hop datasets, creating experimental sets with explicit and implicit multi-hop questions, crowdsourced and templated questions, and varying hop counts. Results indicate larger models like Llama 3.1 70B and o1 excel at decomposing explicit multi-hop questions but falter with implicit ones, while smaller models such as Llama 3.1 8B struggle with both. All models performed well on simple sub-questions when provided context. Crucially, no correlation was found between decomposition accuracy and final QA performance, highlighting a divergence from human reasoning.

Key takeaway

For NLP Engineers developing complex question-answering systems, these findings suggest a critical need to address implicit multi-hop reasoning. You should consider pre-processing complex questions to make decomposition steps explicit for LLMs, especially for smaller models. Additionally, focus on robust sub-question context provision, as LLMs perform well here, and recognize that improving decomposition alone may not directly enhance final answer accuracy.

Key insights

LLMs struggle with implicit question decomposition and lack correlation between decomposition and final answer accuracy, unlike humans.

Principles

• Larger LLMs decompose explicit multi-hop questions better.
• Implicit multi-hop decomposition is a general LLM weakness.
• LLMs handle simple contextual sub-questions well.

Method

Experiments designed to test LLM performance on question decomposition and subproblem solving using expanded multi-hop datasets with varying question types and hops.

In practice

• Prioritize explicit decomposition for complex LLM tasks.
• Provide context for LLM sub-questions.
• Evaluate LLM decomposition and final QA separately.

Topics

• Large Language Models
• Question Answering
• Question Decomposition
• Multi-hop Reasoning
• Llama 3.1
• LLM Evaluation

Best for: Research Scientist, AI Engineer, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.