Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs
This study evaluates seven LLMs (including Gemini, Claude, and GPT families) on inferring individual domain knowledge from long-term Slack logs. Using 27,188 messages from 43 users, zero-shot estimates were compared with self-reported skill ratings from 27 participants. Gemini 2.5 Flash achieved the lowest error (MAE 21.13%), while GPT models showed larger discrepancies. Accuracy depends weakly on message volume, highlighting limits and the need for privacy-aware deployments and richer knowledge representations.
Article intelligence
Key points
- Employees often struggle to identify expertise, causing productivity loss
- Gemini 2.5 Flash achieved lowest MAE of 21.13% in zero-shot inference
- Estimation accuracy only weakly correlated with message quantity
- Findings underscore feasibility and need for privacy-preserving methods
Why it matters
This matters because employees often struggle to identify expertise, causing productivity loss.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.22971] Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs
[Submitted on 21 May 2026]
Title:Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs
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Abstract:Employees often struggle to identify ``who knows what,'' leading to organizational productivity losses. We investigate whether Large Language Models (LLMs) can infer individual domain knowledge directly from long-term Slack logs. Analyzing 27,188 messages from 43 users, we evaluated seven models (including Gemini, Claude, and GPT families) by comparing their zero-shot estimates against self-reported skill ratings from 27 participants. Gemini 2.5 Flash achieved the lowest error (MAE 21.13%), while GPT models showed significantly larger discrepancies. Notably, estimation accuracy depended only weakly on message volume, indicating that more text alone does not guarantee better inference. These findings demonstrate the feasibility and current limits of automated expertise mapping, highlighting the need for privacy-preserving deployments and richer, structure-aware representations of human knowledge.
Subjects:
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2605.22971 [cs.CL]
(or arXiv:2605.22971v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.22971
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ko Watanabe [view email] [v1] Thu, 21 May 2026 19:01:16 UTC (3,790 KB)
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