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iPOE derives annotation guidelines from explanations and refines them with operations like removing, adding, shuffling, and merging to optimize prompts.

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2026-06-30 18:48 UTC pith:UUGP5UEU

load-bearing objection iPOE turns explanations into editable guidelines for prompts and reports gains up to 39%, but the abstract gives almost no experimental controls or baseline details to judge if the results hold. the 1 major comments →

arxiv 2605.18113 v2 pith:UUGP5UEU submitted 2026-05-18 cs.CL

iPOE: Interpretable Prompt Optimization via Explanations

Jiahui Li , Yarik Menchaca Resendiz , Sean Papay , Roman Klinger This is my paper
classification cs.CL
keywords prompt optimizationinterpretable promptsLLM explanationsannotation guidelinesprompt engineeringnatural language processingexplainable AI
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces iPOE to join prompt optimization with human-style annotation guidelines by automatically generating guidelines from explanations of decisions, either from LLMs or humans. These guidelines are then refined through a series of operations to create more transparent and effective prompts for LLMs on annotation tasks. The approach makes the optimization process interpretable, allowing the decision logic to be inspected. Experiments across four datasets demonstrate performance gains and show that LLM-generated explanations can substitute for human ones while maintaining substantial agreement on guideline contributions.

Core claim

iPOE improves over evaluated baselines by up to 39 percent by guiding prompt optimization with automatically created guidelines derived from explanations of annotation decisions; these guidelines are further refined by operations including removing, adding, shuffling, and merging, resulting in prompts that instruct the annotation process transparently, and LLM explanations can replace human explanations while humans and LLMs agree on contributing guidelines with Cohen's kappa up to 0.65.

What carries the argument

The central mechanism is the automatic derivation of guidelines from explanations of annotation decisions, followed by iterative refinement through removing, adding, shuffling, and merging operations to produce the final interpretable prompt.

Load-bearing premise

Guidelines derived automatically from explanations will produce annotation consistency comparable to carefully human-designed guidelines, and the refinement operations will reliably improve them without introducing new inconsistencies.

What would settle it

A controlled test on the same datasets where prompts using the refined guidelines show no performance gain or lower inter-annotator agreement than standard prompts or expert-designed guidelines.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • iPOE achieves up to 39 percent improvement over the evaluated baselines on four datasets.
  • LLM explanations can substitute for human explanations without loss of effectiveness in the method.
  • Humans and LLMs reach substantial agreement on which guidelines contribute to annotations, with Cohen's kappa up to 0.65.
  • The resulting prompts make the LLM decision process transparent and support use by non-experts in specialized domains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be tested on non-annotation tasks such as text generation or reasoning chains to check if guideline refinement generalizes.
  • The interpretability feature might enable systematic debugging of prompt failures by inspecting which guidelines are applied.
  • Future work could explore whether the agreement between human and LLM guideline selection holds across more diverse domains or annotation schemes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces iPOE, a method for interpretable prompt optimization that generates annotation guidelines from explanations of annotation decisions (human or LLM-generated), optimizes these guidelines through operations such as removing, adding, shuffling, and merging, and incorporates them into prompts for LLMs. On four datasets, it reports improvements of up to 39% over baselines, that LLM explanations can replace human ones, and a Cohen's kappa of up to 0.65 between humans and LLMs on guideline contributions.

Significance. If the empirical results hold under rigorous evaluation, the work could advance prompt optimization by increasing transparency and accessibility for non-experts, drawing an explicit parallel to human-designed annotation guidelines. The inclusion of an interpretability validation study with quantitative agreement metrics is a positive feature.

major comments (1)
  1. [Abstract] Abstract: the central claims of up to 39% improvement over baselines and Cohen's kappa of 0.65 are stated without any description of experimental controls, baseline implementations, number of runs, statistical significance tests, or the protocol used to measure guideline quality. This directly prevents assessment of the performance and replacement claims.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater transparency in the abstract regarding experimental details. We address this point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims of up to 39% improvement over baselines and Cohen's kappa of 0.65 are stated without any description of experimental controls, baseline implementations, number of runs, statistical significance tests, or the protocol used to measure guideline quality. This directly prevents assessment of the performance and replacement claims.

    Authors: We agree that the abstract, due to its length constraints, omits key experimental details that support the reported claims. The full manuscript specifies the baselines and their implementations in Section 4.1, the number of runs and statistical significance testing protocol in Section 4.2, and the guideline quality measurement (including the Cohen's kappa protocol) in Section 5. To improve assessability, we will revise the abstract to concisely reference these controls and evaluation aspects without exceeding length limits. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes iPOE as a procedural pipeline that generates guidelines from explanations (human or LLM), applies operations such as removing/adding/shuffling/merging, and evaluates the resulting prompts empirically on four datasets. No equations, fitted parameters, or derivation steps are present that could reduce by construction to the method's own inputs. Claims rest on reported performance gains and agreement metrics rather than any self-referential mathematical structure, rendering the approach self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach rests on the domain assumption that LLM-generated explanations can serve as reliable seeds for annotation guidelines and that the listed editing operations improve guideline quality without side effects. No free parameters or invented entities are mentioned in the abstract.

axioms (2)
  • domain assumption Explanations of annotation decisions contain information that can be converted into reusable guidelines that improve LLM consistency.
    Stated in the motivation comparing to human annotation guidelines.
  • domain assumption The sequence of operations (removing, adding, shuffling, merging) produces better guidelines than the initial set.
    Implicit in the description of how the prompt is optimized.

pith-pipeline@v0.9.1-grok · 5767 in / 1331 out tokens · 21835 ms · 2026-06-30T18:48:54.035591+00:00 · methodology

0 comments
read the original abstract

Prompt optimization has often been framed as a discrete search problem to find high-performing and robust instructions for an LLM. However, the search result might not make it transparent why and where specific prompt changes lead to performance gains. This is in contrast to how humans are instructed for annotation tasks. Here, researchers carefully design annotation guidelines, leading to enhanced annotation consistency. Our paper aims at joining these two approaches and introduces iPOE, a novel interpretable prompt optimization strategy via explanations. We guide the prompt optimization process by automatically created guidelines from explanations of annotation decisions (either automatically generated or from humans). This set of guidelines is furthermore optimized by as series of operations, including removing, adding, shuffling, and merging. The resulting prompt includes guidelines that instruct the annotation, making the decision process of the LLM and the optimization transparent. It therefore supports also laypeople in the area of prompt optimization, particularly in challenging domains requiring expertise. In our experiments on four datasets, we find that iPOE can improves over the evaluated baselines by up to 39% and LLM explanations can replace human explanations in the proposed method. Moreover, our interpretability validation study demonstrates that humans and LLMs can substantially agree on which guidelines contribute to their annotations, achieving a Cohen's kappa score of up to 0.65.

Figures

Figures reproduced from arXiv: 2605.18113 by Jiahui Li, Roman Klinger, Sean Papay, Yarik Menchaca Resendiz.

Figure 1
Figure 1. Figure 1: The depiction of our proposed iPOE method to [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The conceptual workflow of our iPOE ap￾proach. G refers to the current guideline set which is an empty set in the very beginning, and G′ is the updated guideline set from the operations that achieves the best performance. F(·) refers to a performance metric. Our approach also aim to exploit such additional in￾formation either provided by humans or LLMs, but instead to generate rules and guidelines by learn… view at source ↗
Figure 3
Figure 3. Figure 3: Learning curves of F1 scores on the training and validation sets for our iPOE approach. Each plot [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: Learning curves of F1 scores on the training and validation sets for our iPOE approach. Each plot [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: A screenshot of the content page for the medical fact-checking survey. [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: A screenshot of the instruction page for the medical fact-checking survey. [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: A screenshot example of the annotation task for the medical fact-checking survey. [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗

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Reference graph

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