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LLM decision models struggle with overconfidence in Urdu WhatsApp auto-replies

By PulseAugur Editorial · [1 sources] ·

A developer tested six decision models for an auto-reply system on WhatsApp, finding that while language was not a significant barrier for models processing Urdu and Roman Urdu, overconfidence in incorrect answers was a major issue. The models performed well on straightforward queries but struggled with nuanced messages, such as complaints or requests that required a degree of reasoning. This overconfidence led to inappropriate responses, potentially costing businesses customers. AI

IMPACT Highlights the need for improved nuance and confidence calibration in LLMs for customer-facing applications.

RANK_REASON Developer testing of an LLM-based tool for a specific application.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM decision models struggle with overconfidence in Urdu WhatsApp auto-replies

How we ranked this

Signal score
3 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
Developer testing of an LLM-based tool for a specific application.
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
product, other
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

Full methodology in our editorial standards.

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Danish Javed ·

    I compared six decision models using Urdu WhatsApp messages. The language wasn’t the issue; overconfidence was.

    <p>I'm building an auto-reply for small shops on WhatsApp. The owner writes a few answers once: prices, opening hours, the address. When a customer asks, the right one goes back. My first version matched keywords, the way most auto-reply tools do: a message containing "price" get…

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