title: Invoke Interfaces Only When Needed: Adaptive Invocation for Large
    Language Models in Question Answering
summary: The collaborative paradigm of large and small language models (LMs)
  effectively balances performance and cost, yet its pivotal challenge lies in
  precisely pinpointing the moment of invocation when hallucinations arise in
  small LMs. Previous optimization efforts primarily focused on post-processing
  techniques, which were separate from the reasoning process of LMs, resulting in
  high computational costs and limited effectiveness. In this paper, we propose a
  practical invocation evaluation metric called AttenHScore, which calculates the
  accumulation and propagation of hallucinations during the generation process of
  small LMs, continuously amplifying potential reasoning errors. By dynamically
  adjusting the detection threshold, we achieve more accurate real-time
  invocation of large LMs. Additionally, considering the limited reasoning
  capacity of small LMs, we leverage uncertainty-aware knowledge reorganization
  to assist them better capture critical information from different text chunks.
  Extensive experiments reveal that our AttenHScore outperforms most baseline in
  enhancing real-time hallucination detection capabilities across multiple QA
  datasets, especially when addressing complex queries. Moreover, our strategies
  eliminate the need for additional model training and display flexibility in
  adapting to various transformer-based LMs.
paper_authors._id: 681a06459c99f4b5ce5f2838
paper_authors.name: Jihao Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-06T16:51:29.234Z
paper_authors._id: 658e85bb5b7553ca5c29ba89
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/658e85bb5b7553ca5c29ba89/KK6UpS9agtrxevvBoup5N.jpeg
paper_authors.fullname: Jihao Zhao
paper_authors.type: user
paper_authors.user: Robot2050
paper_authors._id: 681a06459c99f4b5ce5f2839
paper_authors.name: Chunlai Zhou
paper_authors._id: 681a06459c99f4b5ce5f283a
paper_authors.name: Biao Qin
paper_title: Invoke Interfaces Only When Needed: Adaptive Invocation for Large
    Language Models in Question Answering
paper_summary: The collaborative paradigm of large and small language models (LMs)
  effectively balances performance and cost, yet its pivotal challenge lies in
  precisely pinpointing the moment of invocation when hallucinations arise in
  small LMs. Previous optimization efforts primarily focused on post-processing
  techniques, which were separate from the reasoning process of LMs, resulting in
  high computational costs and limited effectiveness. In this paper, we propose a
  practical invocation evaluation metric called AttenHScore, which calculates the
  accumulation and propagation of hallucinations during the generation process of
  small LMs, continuously amplifying potential reasoning errors. By dynamically
  adjusting the detection threshold, we achieve more accurate real-time
  invocation of large LMs. Additionally, considering the limited reasoning
  capacity of small LMs, we leverage uncertainty-aware knowledge reorganization
  to assist them better capture critical information from different text chunks.
  Extensive experiments reveal that our AttenHScore outperforms most baseline in
  enhancing real-time hallucination detection capabilities across multiple QA
  datasets, especially when addressing complex queries. Moreover, our strategies
  eliminate the need for additional model training and display flexibility in
  adapting to various transformer-based LMs.
paper_ai_summary: A new metric called AttenHScore is proposed to dynamically detect hallucinations in small language models, enhancing real-time performance and reducing the need for additional training.
paper_ai_keywords: AttenHScore hallucinations large language models small language models reasoning process knowledge reorganization transformer-based LMs QA datasets