---
language:
- id
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
tags:
- telecommunications
- agentic-ai
- bss
- tool-calling
- function-calling
- indonesian
- benchmark
- qlora
- evaluation
pretty_name: 'TelAgentBench-ID: Indonesian Telecommunications BSS Agent Benchmark'
size_categories:
- 1K<n<10K
configs:
- config_name: bss_action_single_turn
  data_files: "TelAgent_Action/telco_simple.json"
  default: true
- config_name: bss_action_multiple_candidates
  data_files: "TelAgent_Action/telco_multiple.json"
- config_name: bss_action_multi_turn
  data_files: "TelAgent_Action/telco_multi_turn_stateless_base.json"
- config_name: travel_planning_roaming
  data_files: "TelAgent_Plan/validation_dataset_1111.json"
- config_name: instruction_following
  data_files: "TelAgent_IF/telif_general_ko.json"
---

# TelAgentBench-ID: A Comprehensive Benchmark for Evaluating Autonomous LLM Agents in Telecommunications Business Support Systems

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
[![Language: Indonesian](https://img.shields.io/badge/Language-Indonesian%20(Code--Mixed)-blue.svg)](https://huggingface.co/languages)
[![Domain: Telecommunications BSS](https://img.shields.io/badge/Domain-Telecom%20BSS%20APIs-orange.svg)](https://www.gsma.com/solutions-and-impact/technologies/networks/gsma-open-gateway/)
[![Framework: BFCL Protocol](https://img.shields.io/badge/Framework-Berkeley%20Function%20Calling-green.svg)](https://gorilla.cs.berkeley.edu/leaderboard.html)

---

## 📌 Dataset Summary

**TelAgentBench-ID** is the first comprehensive, multi-faceted benchmark specifically constructed to evaluate the **Action Execution Fidelity** and **Epistemic Calibration** of Large Language Models (LLMs) and Small Language Models (SLMs) within the **Telecommunications Business Support Systems (BSS)** domain in **Indonesian**.

Adapted and localized from the international benchmark *TelAgentBench* (Lee et al., EMNLP 2025 Industry Track), **TelAgentBench-ID** standardizes 23 realistic enterprise BSS API endpoints based on **GSMA Open Gateway** and **TM Forum Open Digital Architecture (ODA)** specifications, mapped to the **Berkeley Function Calling Leaderboard (BFCL)** evaluation format.

The benchmark spans **757 test scenarios** in tool-calling action evaluation (complemented by instruction-following and multi-city travel planning modules, totaling **2,137 evaluation instances** across 40 JSON/JSONL assets). It is explicitly engineered to diagnose both **Execution Fidelity** (strict parameter extraction and syntactic JSON compliance) and **Epistemic Calibration** (mitigation of *Compulsive Action Bias* / Over-Triggering on ambiguous or incomplete transactions).

---

## 🏛️ Background & Motivation

In mission-critical enterprise environments like telecommunications BSS (governing billing settlement, real-time quota deduction, and supplementary add-on management):
1. **Customer Data Sovereignty:** Under statutes such as the **Indonesian Personal Data Protection Law (UU PDP No. 27/2022)** and the **GDPR**, cross-border transmission of sensitive telecommunications payloads (MSISDN, billing records, identity tokens) to multi-tenant public cloud APIs is legally constrained. Evaluating parameter-efficient Small Language Models (SLMs, $\le$ 7B) capable of **on-premise deployment** is paramount.
2. **Deterministic Execution vs. Free-Form Generation:** Telecommunications transactions necessitate strictly valid, deterministic API payloads. Textual generation metrics (such as BLEU or ROUGE) are inadequate for measuring functional correctness.
3. **Epistemic Calibration:** Production agents must strike a calibrated equilibrium: executing tools with 0.00% premature refusal on valid queries, while actively refraining (*calibrated abstention*) when customer inputs lack necessary credentials, preventing database corruption.

---

## 🧱 Benchmark Architecture & API Testbed

The evaluation suite models **23 production-grade BSS API endpoints** categorized across six functional service clusters:

| No | Service Cluster | Endpoints (APIs) | Function Examples | Key Parameters |
|:--:|:---|:--:|:---|:---|
| 1 | **Bill Management** | 3 | `GET__rate_unpaid-bills`, `GET__rate_current-bills`, `GET__rate_fixed-bills` | `svcMgmtNum` (MSISDN), `startDt`, `endDt` |
| 2 | **Payment & Micro-Billing** | 3 | `GET__rate_content-purchases`, `GET__rate_mobile-payments`, `GET__rate_auto-payments` | `svcMgmtNum`, `startDt`, `endDt` |
| 3 | **Services & Add-ons** | 5 | `GET__rate_add-on-subscriptions`, `DELETE__rate_add-on-subscriptions`, `POST__call_spam-blocking`, `GET__call_coloring` | `svcMgmtNum`, `prodId` |
| 4 | **Roaming Governance** | 4 | `GET__plan_roaming`, `POST__plan_roaming_subscriptions`, `DELETE__plan_roaming_subscriptions`, `GET__plan_roaming_subscriptions` | `svcMgmtNum`, `countryCode`, `countryName` |
| 5 | **Refill & Data Coupons** | 4 | `GET__rate_refill-coupons`, `POST__rate_refill-coupons_register`, `POST__rate_refill-coupons_gift` | `svcMgmtNum`, `couponNum`, `receiverSvcMgmtNum` |
| 6 | **Family BSS Plans** | 4 | `GET__rate_family_real-time-bill`, `GET__rate_family-representatives`, `GET__rate_family-network-groups` | `svcMgmtNum`, `familySvcMgmtNum` |

---

## 📂 Directory Structure & Modularity

The benchmark repository contains 40 verified, valid JSON/JSONL evaluation assets:

```text
TelAgentBench-ID/
├── README.md                                             # Comprehensive Dataset Card
├── validation.json                                       # Multi-City Travel Planning & Roaming (200 instances)
├── TelAgent_Plan/
│   └── validation_dataset_1111.json                      # Detailed Plan Evaluation Corpus (200 instances)
├── TelAgent_IF/
│   └── telif_general_ko.json                             # Multi-Turn Instruction Following (200 instances, 3-turn)
└── TelAgent_Action/
    ├── 250918_telcoFC_tools.json                         # Master Tool Schemas (23 BSS APIs)
    ├── telco_simple.json                                 # Single-Turn 1-Candidate Tool Evaluation (55 items)
    ├── telco_multiple.json                               # Single-Turn 7-Candidate Tool Evaluation (55 items)
    ├── telco_parallel.json                               # Parallel Multi-Tool Invocations (19 items)
    ├── telco_parallel_multiple.json                      # Parallel Multi-Tool with Distractor Schemas (18 items)
    ├── telco_item_recommendation_parallel_multiple.json # Multi-Constraint Plan Recommendations (26 items)
    ├── telco_multi_step_stateless_partial.json           # Multi-Step Tool Execution with Partial Schemas (23 items)
    ├── telco_multi_step_stateless_whole.json             # Multi-Step Tool Execution with Full Schemas (23 items)
    ├── telco_multi_turn_stateless_base.json              # Conversational Multi-Turn State Tracking (46 items, 3 turns)
    ├── telco_multi_turn_stateless_miss_param.json        # Conversational Multi-Turn Missing Parameter Slot-Filling (38 items)
    ├── telco_live_simple.json                            # Live Persona-Prompted Single-Turn Simple (55 items)
    ├── telco_live_multiple.json                          # Live Persona-Prompted Single-Turn Multiple (55 items)
    ├── telco_live_parallel.json                          # Live Persona-Prompted Parallel Execution (19 items)
    ├── telco_live_parallel_multiple.json                 # Live Persona-Prompted Parallel Multi-Tool (18 items)
    ├── telco_live_item_recommendation_parallel_multiple.json # Live Recommendation Evaluation (26 items)
    ├── telco_live_multi_step_stateless_partial.json      # Live Multi-Step Execution Partial (23 items)
    ├── telco_live_multi_step_stateless_whole.json        # Live Multi-Step Execution Whole (23 items)
    ├── telco_live_relevance.json                         # Live Relevance Discrimination (117 items)
    ├── telco_live_irrelevance.json                       # Calibrated Abstention / Unanswerable Queries (118 items)
    └── possible_answer/                                  # 18 Matching Ground-Truth AST Formatted Files
        ├── telco_simple.json
        ├── telco_multiple.json
        ├── telco_multi_turn_stateless_base.json
        └── ... (15 other ground-truth files)
```

---

## 🧪 Evaluation Metrics & Protocols

All models evaluated against **TelAgentBench-ID** are assessed across standardized AST execution metrics:

1. **Tool Selection Accuracy:**  
   $$\text{Acc}_{\text{tool}} = \frac{\sum \mathbb{I}(f_{\text{pred}} = f_{\text{gt}})}{N}$$  
   Measures intent recognition fidelity (identifying the correct target endpoint among candidates).
2. **Strict AST Accuracy:**  
   $$\text{Acc}_{\text{AST}} = \frac{\sum \mathbb{I}(f_{\text{pred}} = f_{\text{gt}} \land \Theta_{\text{pred}} \equiv \Theta_{\text{gt}})}{N}$$  
   Evaluates full functional determinism: verifies that function name, argument keys, data types, and extracted parameter values (MSISDNs, formatted date ranges, product IDs) match the ground-truth Abstract Syntax Tree exactly.
3. **Format Validity:**  
   $$\text{Acc}_{\text{format}} = \frac{\sum \mathbb{I}(\text{is\_valid\_json}(y))}{N}$$  
   Measures the structural compliance of model outputs (100.00% indicates zero JSON parser crashes).
4. **Epistemic Calibration Metrics:**
   - **Premature Abstention:** False refusal rate on fully-specified, answerable queries (ideal target: $0.00\%$).
   - **Calibrated Abstention:** Correct refusal rate on underspecified, ambiguous, or out-of-domain queries (suppressing *Compulsive Action Bias*).

---

## 🔬 Benchmark Baseline Results

Benchmark results from the accompanying study (*Tristansyah & Ichsan, 2026*) under deterministic decoding ($\text{Temperature} = 0.0$):

| Model Architecture | Scale / Regime | Single-Turn Tool Acc (%) | Single-Turn Strict AST (%) | Multi-Turn Strict AST (%) | Overall Strict AST (%) | Format Validity (%) | Latency (s) | Hosting Footprint |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---|
| **Claude-Opus-5** | Frontier / B | 89.09% | 87.27% | 64.42% | 75.85% | 99.01% | 3.94 s | Cloud API |
| **Gemma4-31B** | Frontier / A | 94.55% | 85.45% | 64.17% | 74.81% | 100.00% | 1.35 s | >28 GB VRAM |
| **GPT-OSS-120B** | Frontier / A | 85.45% | 80.00% | 59.82% | 69.91% | 98.72% | 1.52 s | >75 GB VRAM |
| **GPT-OSS-20B** | Mid-Scale / A | 89.09% | 87.27% | 57.10% | 72.19% | 97.44% | 2.65 s | ~18 GB VRAM |
| **MiniMax-M3** | Mid-Scale / B | 87.27% | 85.45% | 21.88% | 53.67% | 97.03% | 5.43 s | Cloud API |
| **Nemotron-30B** | Mid-Scale / A | 61.82% | 61.82% | 14.24% | 38.03% | 68.59% | 3.58 s | >26 GB VRAM |
| **TelkomNusa-LLM-7B (SFT)** | 7B / A | **89.09%** | **83.64%** | **33.04%** | **58.34%** | **100.00%** | **11.02 s** | **~9.2 GB (NVIDIA L4)** |
| **TelkomNusa-SLM-3B (SFT)** | 3B / A | **85.45%** | **72.73%** | **16.38%** | **44.55%** | **100.00%** | **10.30 s** | **~5.8 GB (NVIDIA T4)** |
| **Llama-3.2-3B** | Open / B | 81.82% | 78.18% | 3.99% | 41.09% | 100.00% | 22.03 s | ~6.1 GB (NVIDIA T4) |
| **Qwen2.5-3B-Base** | Open / A | 67.27% | 61.82% | 4.13% | 32.98% | 99.01% | 7.65 s | ~5.8 GB (NVIDIA T4) |
| **Phi-4-mini** | Open / B | 10.91% | 10.91% | 0.00% | 5.46% | 97.03% | 6.68 s | ~6.8 GB (NVIDIA T4) |

*Key Insights:*
- **Domain Weight Specialization Outperforms Scale:** `TelkomNusa-LLM-7B` achieves **83.64%** Single-Turn AST, surpassing the massive **GPT-OSS-120B (80.00%)**.
- **Edge Viability:** `TelkomNusa-SLM-3B` achieves **44.55%** Overall AST, outperforming `Llama-3.2-3B` (41.09%) with over $53\%$ lower latency on a commodity NVIDIA Tesla T4 GPU.

---

## 🔒 Data Hygiene & Contamination Safeguards

To prevent artificial metric inflation and data leakage:
1. **Strictly Disjoint Testbed:** The 757 evaluation scenarios are strictly quarantined (*held-out*) from the 3,510 training pairs of the TelkomNusa SFT corpus.
2. **Decontamination Protocol:** N-gram overlap and AST similarity audits were executed following the contamination protocols established by Deng et al. (NAACL 2024) and Lee et al. (ACL 2022).
3. **Synthetic PII Masking:** All MSISDNs utilize synthetic Indonesian telecommunications number allocations (`0812xxxx`, `0811xxxx`). No real customer Personally Identifiable Information (PII) is included, ensuring compliance with **Indonesian Law No. 27/2022 (UU PDP)**.

---

## 📜 How to Cite

If you utilize **TelAgentBench-ID** in your research, please cite both the accompanying study and the original foundational benchmark:

```bibtex
@article{tristansyah2026benchmarking,
  title={Benchmarking Small Language Models Against Frontier Giants for Telecommunications BSS Tool-Calling: Execution Fidelity and Epistemic Calibration},
  author={Tristansyah, M Rislan and Ichsan, Ichwan Nul},
  journal={JUTI: Jurnal Ilmiah Teknologi Informasi},
  volume={x},
  number={x},
  year={2026},
  publisher={Universitas Pendidikan Indonesia \& Institut Teknologi Sepuluh Nopember}
}

@inproceedings{lee2025telagentbench,
  title={TelAgentBench: A Multi-Faceted Benchmark for Evaluating LLM-Based Agents in Telecommunications},
  author={Lee, S. and Potdar, S. and Rojas-Barahona, L. and Montella, S. and others},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP): Industry Track},
  pages={1173--1211},
  year={2025},
  publisher={Association for Computational Linguistics},
  doi={10.18653/v1/2025.emnlp-industry.83}
}
```

---

## 📄 License & Attribution

This dataset is distributed under the **Creative Commons Attribution 4.0 International License ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))**.  
You are free to share, copy, modify, and build upon this benchmark for academic and commercial purposes, provided appropriate attribution is given to the original authors.