Cost-Aware Abstention for LLM-Based IT Ticket Classification

Sachin Gupta

cs.LGOct 22, 2026

Cost-Aware Abstention for LLM-Based IT Ticket Classification

Authors: Sachin Gupta

Organizations: Independent Researcher United States

Abstract

Wrong automatic routing of an IT support ticket can cost more than human triage. We evaluate cost-aware abstention as an offline, cost-conditional readiness assessment: select a routing policy on calibration data under explicit costs, then evaluate that frozen policy once on held-out test data. On a public 47,837-ticket, eight-class corpus, we compare four trained non-LLM classifiers with four large language models under zero- and few-shot prompting. Under the illustrative cost model (c_ok, c_triage, c_wrong) = (0, 1, 5), TF-IDF with logistic regression and a threshold of 0.60 selected on calibration reduces test cost from 0.763 to 0.472, a 38.1% relative reduction (paired ratio-bootstrap 95% CI [35.7%, 40.4%]), while lowering wrong routes from 15.3% to 3.5%. Frozen calibration-selected policies also beat both always-route and always-defer on held-out test data in 14 of 16 settings in a post-hoc cost-parameter analysis. The strongest prompted configuration reaches 0.477 accuracy on a stratified 1,000-ticket test subset, versus 0.879 for fine-tuned DistilBERT on the same tickets. Isotonic lowers expected calibration error without reliably improving discrimination: GPT-4o-mini few-shot has one confidence value before and after recalibration and correctness AUROC 0.500. We therefore report correctness AUROC and tie-aware risk-coverage area alongside ECE. At the analytically derived break-even probability 0.80, seven prompted configurations defer all tickets; Haiku few-shot routes only 19/1,000. An LLM-only agreement control and margin/entropy rejection baselines further separate calibration from discrimination. The findings are scoped to this dataset, prompt design, model endpoints, and illustrative costs; the work is an evaluation framework, not a new abstention algorithm or universal deployment certification.

Figures & tables

Panel of Figure 1 — Fig. 1: Evaluation pipeline from ticket text to auto-routing or human triage, with prediction, confidence estimation, and abstention separated.

Property	Value
# tickets	47,837
# classes	8
Train / Calib. / Test	33,486 / 4,784 / 9,567
Median ticket length (chars)	175
Class imbalance (max/min)	$7.74\times$

TABLE I: Dataset summary (public IT Service Ticket Classification Dataset).

Baseline (on 1,000-ticket subsample)	Acc.	Macro-F1	ECE-raw
TF-IDF + LR	0.847	0.842	0.111
TF-IDF + Linear SVM	0.867	0.869	0.056
TF-IDF + Random Forest	0.804	0.791	0.172
DistilBERT (fine-tuned)	0.879	0.883	0.043

TABLE II: Non-LLM baselines on the LLM test subset (

n=1{,}000

Model	Setting	Acc.	Macro-F1	ECE r	ECE i	AUROC r	AUROC i	AURC r	AURC i
TF-IDF + LR	classical	0.847	0.842	0.107	—	—	—	—	—
TF-IDF + Linear SVM	classical	0.863	0.864	0.046	—	—	—	—	—
TF-IDF + Random Forest	classical	0.818	0.806	0.182	—	—	—	—	—
DistilBERT (fine-tuned)	classical	0.877	0.874	0.041	—	—	—	—	—
Claude Haiku 4.5	few-shot	0.477	0.459	0.386	0.035	0.664	0.659	0.417	0.430
GPT-4o-mini	few-shot	0.459	0.450	0.541	0.021	0.500	0.500	0.541	0.541

TABLE III: Classification and confidence results. Non-LLM: full test; LLM: common 1,000-ticket subset.

r/i

: raw/isotonic (fit on calibration); ECE and tie-group AURC

\downarrow

, AUROC

\uparrow

. Bold marks the best value per metric within each evaluation block.

Fig. 2: Class distribution of the 47,837-ticket corpus across the eight categories.

Model	Setting	ECE M9	ECE M8	AUROC M9	AUROC M8
Claude Haiku 4.5	few-shot	0.148	0.210	0.692	0.590
GPT-4o-mini	few-shot	0.205	0.239	0.707	0.593
Qwen2.5-7B-Instruct	few-shot	0.196	0.238	0.708	0.603
Llama-3-8B-Inst.-Lite	zero-shot	0.193	0.209	0.750	0.658
GPT-4o-mini	zero-shot	0.271	0.305	0.800	0.694
Claude Haiku 4.5	zero-shot	0.251	0.283	0.814	0.721

TABLE IV: Mixed and LLM-only agreement controls on the common 1,000-ticket test subset. The mixed

M{=}9

pool adds supervised TF-IDF + LR to the eight prompted conditions.

Policy	$\mathbb{E}[c]$	Reduction %	Cov. %	Acc. %
always_route	0.763	—	100.0	84.7
[email protected]	0.510	33.1	81.0	92.1
[email protected]	0.472	38.1	70.5	95.0
[email protected]	0.494	35.2	60.1	96.8
[email protected]	0.556	27.2	48.7	98.2
[email protected]	0.673	11.8	33.7	99.4

TABLE V: TF-IDF + LR test cost under

(0,1,5)

. Bold marks the calibration-selected operating point; reductions are relative to always_route .

Baseline	$\tau^{*}$	$\mathbb{E}[c]_{\text{a}}$	$\mathbb{E}[c]_{\tau^{*}}$	Reduction %	95 % CI	Wr. %
TF-IDF + LR	0.60	0.763	0.472	38.1	$[35.7,40.4]$	3.5
TF-IDF + Linear SVM	0.70	0.687	0.434	36.7	$[34.3,39.1]$	3.7
TF-IDF + Random Forest	0.60	0.910	0.571	37.3	$[35.0,39.5]$	2.1
DistilBERT (fine-tuned)	0.90	0.615	0.396	35.5	$[32.9,38.2]$	3.3

TABLE VI: Non-LLM test costs at calibration-selected thresholds under

(0,1,5)

\mathbb{E}[c]_{\mathrm{a}}

: always-route cost; Wr.: post-threshold wrong-route rate; ratio-bootstrap 95 % CIs.

$c_{\mathrm{triage}}\backslash c_{\mathrm{wrong}}$	2	5	10	20
0.5	[email protected] / 0.220 / 27.9%	[email protected] / 0.293 / 41.4%	[email protected] / 0.342 / 31.6%	[email protected] / 0.374 / 25.2%
1.0	[email protected] / 0.298 / 2.4%	[email protected] / 0.482 / 36.9%	[email protected] / 0.586 / 41.4%	[email protected] / 0.684 / 31.6%
1.5	[email protected] / 0.306 / $-0.2$ %	[email protected] / 0.606 / 20.6%	[email protected] / 0.789 / 47.4%	[email protected] / 0.934 / 37.7%
2.0	always_route † / 0.305 / 0.0%	[email protected] / 0.698 / 8.5%	[email protected] / 0.963 / 36.9%	[email protected] / 1.173 / 41.4%

TABLE VII: Post-hoc TF-IDF + LR cost-parameter sensitivity. For each setting, one policy is selected on calibration over always_route , always_defer , and thresholds 0.01–0.99, then evaluated once on test. Cells show policy / test

\mathbb{E}[c]

/ reduction relative to the lower-cost trivial test comparator. Negative reduction denotes higher cost. t@x abbreviates threshold@x ;

\dagger

marks the equal-cost boundary.

Score	$\tau^{*}$	$\mathbb{E}[c]$	Reduction %	95 % CI	Cov. %
Top probability	0.65	0.482	36.9	$[34.2,39.3]$	65.2
Top-two margin	0.43	0.467	38.8	$[36.5,41.1]$	68.5
Inverse entropy	0.44	0.507	33.6	$[30.8,36.2]$	68.3

TABLE VIII: TF-IDF + LR rejection scores under

(0,1,5)

. Thresholds are selected on calibration over a 0.01 grid and evaluated on test. Cost reductions and paired ratio-bootstrap 95 % CIs are relative to always-route.

Panel of Figure 11 — Fig. 3: TF-IDF + LR test cost over the predeclared threshold grid under $(0,1,5)$ . The calibration-selected $\tau=0.60$ yields a 38.1 % reduction (95 % CI $[35.7\,\%,40.4\,\%]$ ).

Panel of Figure 12 — Fig. 4: TF-IDF + LR coverage–accuracy curve. The marked calibration-selected point routes 70.5 % of test tickets at 95.0 % accuracy.

Setting	Prompt	Max tok.	Acc.	Macro-F1	Parse err.
zero-shot	v1	64	0.345	0.245	0.000
zero-shot	v2	128	0.250	0.197	0.000
few-shot	v1	64	0.245	0.271	0.185
few-shot	v2	128	0.320	0.289	0.000

TABLE IX: Joint prompt/budget sensitivity for Llama-3-8B on a 200-ticket subset. Template and token budget change together. Bold marks the higher accuracy within each zero-/few-shot pair.

Fig. 5: TF-IDF + LR confusion matrix without abstention on the full test set ( $n=9{,}567$ ). Color uses a square-root count scale; the three largest off-diagonal cells are labeled.

Selective Prediction

Selective prediction lets models answer only when sufficiently reliable, trading coverage for lower error through abstention, referral, or extra evidence gathering.

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Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signals

Lech Madeyski

Wroclaw University of Science and Technology, Poland

LLM Routing Cost-Aware Inference Software Engineering

Open paper AskPDF arXiv

Context: AI coding agents route every task to a single frontier large language model (LLM), paying premium inference cost even when many tasks are routine. Objectives: We propose Triage, a framework that uses code health metrics -- indicators of software maintainability -- as a routing signal to assign each task to the cheapest model tier whose output passes the same verification gate as the expensive model. Methods: Triage defines three capability tiers (light, standard, heavy -- mirroring, e.g., Haiku, Sonnet, Opus) and routes tasks based on pre-computed code health sub-factors and task metadata. We design an evaluation comparing three routing policies on SWE-bench Lite (300 tasks across three model tiers): heuristic thresholds, a trained ML classifier, and a perfect-hindsight oracle. Results: We analytically derived two falsifiable conditions under which the tier-dependent asymmetry (medium LLMs benefit from clean code while frontier models do not) yields cost-effective routing: the light-tier pass rate on healthy code must exceed the inter-tier cost ratio, and code health must discriminate the required model tier with at least a small effect size (

\hat{p} \geq 0.56

). Conclusion: Triage transforms a diagnostic code quality metric into an actionable model-selection signal. We present a rigorous evaluation protocol to test the cost--quality trade-off and identify which code health sub-factors drive routing decisions.

Abstract

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