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SimXAI: A Simple Drop-in Layer Increases Interaction Quality and Understanding in Conversational XAI — arXiv:2610.41856

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41856
License (as archived): CC0-1.0
Archived: 2026-11-02T04:45:27.675Z
SHA-256 of the archived text: 367ba7adf70c0208016fd583fa6080c3ba8e05e4b0b7ea61b2baee897e15431c
Stable URL of this page: https://metraipolis.com/documento/dc_fc7bfda3cefb

How to cite

SimXAI: A Simple Drop-in Layer Increases Interaction Quality and Understanding in Conversational XAI — arXiv:2610.41856 — arXiv (en), https://arxiv.org/abs/2610.41856 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc7bfda3cefb (Metraipolis archive dc_fc7bfda3cefb, sha256 367ba7adf70c0208016fd583fa6080c3ba8e05e4b0b7ea61b2baee897e15431c)

BibTeX

@misc{dcfc7bfda3cefb,
  title = {SimXAI: A Simple Drop-in Layer Increases Interaction Quality and Understanding in Conversational XAI — arXiv:2610.41856},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41856},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc7bfda3cefb; sha256 367ba7adf70c0208016fd583fa6080c3ba8e05e4b0b7ea61b2baee897e15431c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41856. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conversational explainable AI (ConvXAI) agents often use GUS-style architectures with separate modules for intent recognition, dialogue management, and template-based response generation. While faithful to the underlying XAI methods, these pipelines are hard to adapt and often struggle with context-dependent follow-up questions. We present SimXAI, a lightweight LLM-centric conversational layer that replaces these modules with a single tool-calling LLM that bridges access to existing XAI resources. Rather than a standalone agent, SimXAI serves as a modular interface that enables conversational access to external XAI resources. We further introduce an evaluation suite for single- and multi-turn parsing, faithfulness, and contextualization. Across existing and new datasets, SimXAI achieves strong function-selection accuracy and high faithfulness while better handling context-dependent requests. In a user study replacing only the conversational layer of TalkToModel, a representative GUS-based agent, users completed tasks more accurately and in fewer turn. Overall, SimXAI improves GUS-based design while preserving faithfulness and improving conversational quality.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2610.41856 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T04:42:12.481Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).