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Paper

Knowledge Dependency Estimation for Reliable Question Answering

by Chaodong Tong arxiv/2605.28047

Free2AITools Nexus Index

38.5

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0

P: Popularity 0

R: Recency 93

Q: Quality 60

Tech Context

Vital Performance —

Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In realistic LLM-based QA, this knowledge may come from context, retrieval, decomposition, or intermediate reasoning, forming a noisy and redundant candidate space rather than a clean gold evidence set. We study \emph{knowledge dependency estimation}: estimating the sensitivity of a fixed black-box QA model to different candidate knowledge units...

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Paper Information Summary
Entity Passport
Registry ID	2605.28047
License	arXiv
Provider	arxiv

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2605_28047,
  author = {Chaodong Tong},
  title = {Knowledge Dependency Estimation for Reliable Question Answering Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2605.28047}},
  note = {Accessed via Free2AITools.}
}

APA Style

Chaodong Tong. (2026). Knowledge Dependency Estimation for Reliable Question Answering [Paper]. Free2AITools. https://arxiv.org/abs/2605.28047

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0

Popularity (P) 0

Recency (R) 93

Quality (Q) 60

💬 Index Insight

FNI V2.0 for Knowledge Dependency Estimation for Reliable Question Answering: Authority (A:0), Popularity (P:0), Recency (R:93), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In realistic LLM-based QA, this knowledge may come from context, retrieval, decomposition, or intermediate reasoning, forming a noisy and redundant candidate space rather than a clean gold evidence set. We study \emph{knowledge dependency estimation}: estimating the sensitivity of a fixed black-box QA model to different candidate knowledge units..."

❝ Cite Node

@article{Tong2026Knowledge,
  title={Knowledge Dependency Estimation for Reliable Question Answering},
  author={Chaodong Tong},
  journal={arXiv preprint arXiv:2605.28047},
  year={2026}
}

👥 Collaborating Minds

Chaodong Tong

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📅1970Published

⏱️93RecencyFNI pillar

✅60QualityFNI pillar

🗂️cs.CLField

🏷️ Research Topics

rag retrieval

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Source summary: Based on arXiv metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: 2605.28047
slug: 2605.28047
source: arxiv
author: Chaodong Tong
license: arXiv
tags: arxiv:cs.CL

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

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stars: null
forks: null

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