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arxiv: 2511.04070 · v3 · pith:AZWZRNZMnew · submitted 2025-11-06 · 💻 cs.CL

T-FIX: Text-Based Explanations with Features Interpretable to eXperts

Shreya Havaldar , Weiqiu You , Chaehyeon Kim , Anton Xue , Helen Jin , Marco Gatti , Bhuvnesh Jain , Helen Qu
show 7 more authors
Amin Madani Daniel A. Hashimoto Gary E. Weissman Rajat Deo Sameed Khatana Lyle Ungar Eric Wong
This is my paper

Pith reviewed 2026-05-21 19:40 UTC · model grok-4.3

classification 💻 cs.CL
keywords expert alignmentLLM explanationsevaluation frameworkdomain reasoningautomatic scoringscientific tasksgeneralization
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The pith

T-FIX turns expert-defined criteria into automatic scores for whether LLM explanations match domain reasoning, and those scores generalize to new explanations without further expert input.

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

The paper presents T-FIX as a framework that lets researchers measure how closely LLM-generated explanations follow the reasoning patterns of domain experts in scientific fields. Instead of asking experts to label every new explanation, T-FIX first captures their standards once as concrete criteria across seven tasks in three domains. These criteria then drive automatic evaluation that applies to explanations the system has never seen before. The approach aims to replace costly, example-by-example expert annotation with a reusable, personalizable method that still reflects professional judgment. If the criteria capture the right aspects of reasoning, developers could test alignment once per domain and reuse the same system as models and tasks evolve.

Core claim

T-FIX operationalizes expert alignment as a measurable property of LLM explanations by encoding domain-grounded criteria supplied by experts, then applies those criteria to produce automatic scores that remain valid for explanations outside the original set of examples.

What carries the argument

T-FIX, a unified evaluation framework that converts expert-defined criteria for domain-grounded reasoning into automatic, generalizable scores for LLM explanations.

If this is right

  • Evaluation of new explanations no longer requires fresh expert annotations for each case.
  • The same criteria can be reused across multiple LLM outputs and tasks within a domain.
  • Different expert groups can supply their own criteria to create personalized alignment measures.
  • The framework covers seven tasks spanning three scientific domains, supporting cross-task comparisons.

Where Pith is reading between the lines

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

  • The method could support ongoing monitoring of deployed LLMs in high-stakes settings by flagging when explanations drift from expert standards.
  • If criteria prove stable, they might serve as shared benchmarks for comparing explanation quality across different model families.
  • Extending the approach to additional domains would require only new expert criteria rather than redesigning the entire evaluation process.

Load-bearing premise

Expert-defined criteria can be made concrete enough to support reliable automatic scoring while still capturing the core reasoning that experts use across different explanations.

What would settle it

Collect a fresh set of LLM explanations that domain experts rate as well-aligned, then check whether T-FIX assigns them consistently high scores; consistent mismatch between expert ratings and T-FIX scores would falsify the generalization claim.

Figures

Figures reproduced from arXiv: 2511.04070 by Amin Madani, Anton Xue, Bhuvnesh Jain, Chaehyeon Kim, Daniel A. Hashimoto, Eric Wong, Gary E. Weissman, Helen Jin, Helen Qu, Lyle Ungar, Marco Gatti, Rajat Deo, Sameed Khatana, Shreya Havaldar, Weiqiu You.

Figure 1
Figure 1. Figure 1: Current evaluations of LLM explanations typi [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of the T-FIX construction process. For each dataset, we first establish expert alignment criteria – features deemed important by domain experts for a specific task – through collaboration with these experts and LLM-based deep research tools. These criteria form the basis of the T-FIX evaluation pipeline, which processes an LLM-generated explanation to output an expert alignment score. A high sc… view at source ↗
Figure 3
Figure 3. Figure 3: Our T-FIX pipeline. To evaluate an LLM-generated explanation, we first decompose it into atomic claims. Next, we filter out irrelevant claims, such as unsupported or speculative statements. Each remaining claim is then scored against the domain-specific expert alignment criteria: a score of “complete” indicates perfect overlap with at least one criterion, while “none” indicates no overlap. Filtered-out cla… view at source ↗
Figure 4
Figure 4. Figure 4: Overview of datasets and domains in T-FIX. We evaluate LLM expert alignment across seven diverse domains, spanning cosmology, psychology, and medicine. For each dataset, we highlight the motivating task, input–output format, representative example, and the expert responsible for validating alignment criteria. The final row summarizes the expert alignment criteria used for scoring explanations in each domai… view at source ↗
Figure 5
Figure 5. Figure 5: Shannon Entropy of expert alignment criteria for GPT-4o. For each prompting baseline, we show coverage [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Expert Alignment vs. Accuracy Correlation [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror professional reasoning. Yet evaluating whether an LLM "thinks like an expert" remains difficult: existing approaches rely on per-example expert annotation, making them costly, hard to scale, and tied to a single notion of correct reasoning within each domain. To address this gap, we introduce T-FIX, a unified evaluation framework that operationalizes expert alignment as a desired attribute of LLM-generated explanations. T-FIX spans seven scientific tasks across three domains, with each task evaluated against expert-defined criteria that capture domain-grounded reasoning rather than generic explanation quality. Our framework enables automatic, personalizable evaluation of expert alignment that generalizes to unseen explanations without ongoing expert involvement. Code is available at https://github.com/BrachioLab/FIX-2/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes T-FIX, a framework that operationalizes expert alignment for evaluating LLM explanations using expert-defined criteria. It covers seven scientific tasks in three domains and claims automatic, personalizable evaluation that generalizes to unseen explanations without further expert involvement.

Significance. Should the framework's ability to generalize hold up under scrutiny, it would represent a meaningful advance in scalable evaluation of LLM explanations in expert domains. This could reduce the cost and scalability issues associated with expert annotations. The open-source code is noted as a strength for allowing community verification and extension.

major comments (2)
  1. [§3] The description of how expert criteria are turned into automatic scoring mechanisms lacks detail on preventing overfitting to the initial annotated explanations, which is critical for the generalization claim to unseen cases.
  2. [§5.2] Results on generalization are presented for held-out explanations within the same tasks, but no experiments test transfer to explanations generated under different conditions or from other models, leaving the robustness to distribution shift unaddressed.
minor comments (1)
  1. [Introduction] Some citations to related work on explanation evaluation could be expanded to include more recent papers on LLM alignment.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which helps strengthen the presentation of T-FIX's generalization properties. We address each major comment below and commit to revisions that clarify the framework's design and experimental scope.

read point-by-point responses

  1. Referee: [§3] The description of how expert criteria are turned into automatic scoring mechanisms lacks detail on preventing overfitting to the initial annotated explanations, which is critical for the generalization claim to unseen cases.

    Authors: We agree that Section 3 would benefit from greater specificity. In the revised manuscript we will expand the description of the criterion-to-scorer pipeline to explicitly detail the safeguards against overfitting: (i) criteria are elicited at a high level of abstraction before any explanations are seen, (ii) a separate validation split of annotated explanations is used to tune the automatic scorer, and (iii) the final scorer is frozen before evaluating the held-out test set. These steps will be illustrated with a concrete example from one domain. revision: yes

  2. Referee: [§5.2] Results on generalization are presented for held-out explanations within the same tasks, but no experiments test transfer to explanations generated under different conditions or from other models, leaving the robustness to distribution shift unaddressed.

    Authors: The current experiments indeed evaluate generalization only to held-out explanations generated under the same prompting and model conditions. We will add a dedicated paragraph in §5.2 (and a short appendix table) that acknowledges this scope and reports preliminary transfer results on two tasks using explanations from a second LLM. If space constraints prevent full cross-model tables, we will at minimum include a clear limitations statement and outline the additional expert-validation steps required for broader distribution-shift testing. revision: partial

Circularity Check

0 steps flagged

No significant circularity; framework relies on independent expert criteria

full rationale

The paper introduces T-FIX as an operationalization of expert alignment using externally defined criteria across seven tasks in three domains. The central claim of automatic, generalizable evaluation to unseen explanations is grounded in these independent expert inputs rather than any self-referential definition, fitted parameter renamed as prediction, or self-citation chain. No equations or derivations reduce the output to the input by construction; the approach treats expert criteria as an external benchmark that is then automated, with generalization tested on held-out explanations. This is self-contained against external validation and receives the default non-finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the domain assumption that expert criteria can be turned into automatic, generalizable evaluations; no free parameters or invented entities are mentioned.

axioms (1)
  • domain assumption Expert-defined criteria capture domain-grounded reasoning rather than generic explanation quality.
    Invoked when describing how each task is evaluated against expert-defined criteria.

pith-pipeline@v0.9.0 · 5744 in / 1167 out tokens · 93951 ms · 2026-05-21T19:40:57.356235+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Interpretability Can Be Actionable

    cs.LG 2026-05 conditional novelty 6.0

    Interpretability research should be judged by actionability—the degree to which its insights support concrete decisions and interventions—rather than explanatory power alone.

Reference graph

Works this paper leans on

91 extracted references · 91 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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