arxiv: 2604.11801 · v1 · submitted 2026-04-13 · 💻 cs.CL

Recognition: unknown

CLSGen: A Dual-Head Fine-Tuning Framework for Joint Probabilistic Classification and Verbalized Explanation

Autumn Sehy, Dmitriy Dligach, Ian Bulovic, Kangyu Zhu, Majid Afshar, Timothy A. Miller, WonJin Yoon, Yanjun Gao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:53 UTC · model grok-4.3

classification 💻 cs.CL

keywords CLSGendual-head fine-tuningprobabilistic classificationverbalized explanationsLLM fine-tuningbinary classificationcatastrophic forgettingexplanation generation

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The pith

CLSGen enables LLMs to estimate classification probabilities reliably while preserving their explanation generation ability.

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

The paper proposes CLSGen as a way to fine-tune large language models for binary classification. It uses a dual-head setup so the model can both assign probabilities to labels and generate verbal explanations. Standard fine-tuning for probabilities often makes the model lose its language skills entirely. By designing special training methods and data, CLSGen avoids this trade-off. A reader would care because many real applications need models that are both accurate with numbers and transparent with reasons.

Core claim

CLSGen is a dual-head fine-tuning framework that jointly optimizes for probabilistic classification and verbalized explanations in LLMs, outperforming baselines on AUROC and F1 while maintaining high alignment and readability in generated explanations.

What carries the argument

Dual-head architecture with one head for probability outputs and another for explanation generation, combined with a custom training methodology and data construction strategy.

If this is right

CLSGen-tuned models achieve superior AUROC and F1 scores on multiple benchmark datasets compared to existing baselines.
Predicted labels align strongly with the generated verbal justifications.
Explanations produced by the model maintain high readability.
The framework prevents catastrophic forgetting and linguistic collapse during fine-tuning for classification.

Where Pith is reading between the lines

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

Similar dual-head approaches might apply to other tasks requiring both quantitative and qualitative outputs, such as regression with descriptions.
Deployment in decision support systems could improve user trust by providing both confidence scores and rationales.
Future work could test if this scales to larger models or multi-label settings without additional interference.

Load-bearing premise

That the dual-head architecture with the proposed training and data strategies can optimize both tasks jointly without causing interference or loss of linguistic capability.

What would settle it

Training an LLM with CLSGen and then observing either significantly lower AUROC/F1 than baselines or explanations that are unreadable or misaligned with the predicted labels would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.11801 by Autumn Sehy, Dmitriy Dligach, Ian Bulovic, Kangyu Zhu, Majid Afshar, Timothy A. Miller, WonJin Yoon, Yanjun Gao.

**Figure 1.** Figure 1: Classification and generation quality during classification-only training. view at source ↗

**Figure 2.** Figure 2: Diagram of data construction pipeline (left) and CLSGen fine-tuning (right). view at source ↗

read the original abstract

With the recent progress of Large Language Models (LLMs), there is a growing interest in applying these models to solve complex and challenging problems. Modern LLMs, capable of processing long contexts and generating verbalized explanations, offer significant potential in addressing real-world applications. However, a critical hurdle in deploying LLMs for practical decision-making is their inability to provide reliable, quantitative probabilities. While task-specific fine-tuning of LLMs using traditional discriminative objectives (similar to encoder-only models) can yield probability estimates, this often leads to catastrophic forgetting and linguistic collapse. Consequently, the model loses its ability to generate explanations, severely undermining its interpretability and usability. To address this challenge, we propose CLSGen, a novel LLM fine-tuning framework designed for binary classification tasks. The CLSGen framework encompasses a new model architecture, training methodology, and data construction strategy to enable robust probability estimation without sacrificing the model's inherent explanation-generation capabilities. Experimental results across multiple benchmark datasets demonstrate that models fine-tuned with CLSGen outperform existing baselines in classification metrics (AUROC and F1-score). Regarding explanation, the results showed strong alignment between predicted labels and generated justifications, as well as high readability.

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.

Desk Editor's Note private letter to a colleague

CLSGen's dual-head setup plus data construction gives better AUROC and F1 on classification while keeping explanations aligned, but the no-collapse claim rests on post-training results without a direct pre/post comparison to the base model.

read the letter

CLSGen's dual-head fine-tuning framework lets you train an LLM for binary classification with probability outputs while trying to hold onto its ability to generate verbal explanations. That's the main thing to know here. The new element is the specific mix of architecture, training objective, and data construction strategy meant to avoid the usual linguistic collapse when you push an LLM toward discriminative fine-tuning. The experiments show gains over baselines on AUROC and F1 across multiple datasets, and the generated explanations line up with the predicted labels while scoring high on readability. That combination is useful for anyone who needs both a number and a justification from the same model. The soft spot is exactly the one flagged in the stress test: there is no direct before-and-after measurement of explanation quality on identical inputs and metrics. The paper reports post-training alignment and readability, but without showing how the untuned base model performed on the same prompts, it is hard to confirm that the dual-head design fully prevented degradation rather than just producing acceptable outputs after some loss. Details on baseline selection, hyperparameter search, and statistical tests would also strengthen the results section. This work is aimed at people building LLM systems for high-stakes classification where both calibration and interpretability matter, such as clinical or legal decision support. A reader looking for practical fine-tuning recipes would find usable ideas even if the evidence for full preservation is not yet airtight. I would send it to peer review. The core problem is real, the empirical results are directionally positive, and the framework is concrete enough that referees can give targeted feedback on the missing comparisons.

Referee Report

2 major / 2 minor

Summary. The paper proposes CLSGen, a dual-head fine-tuning framework for LLMs on binary classification tasks. It introduces a new model architecture with separate heads for probabilistic classification and verbalized explanation generation, along with a custom training methodology and data construction strategy. The goal is to achieve reliable probability estimates (via AUROC and F1) while avoiding catastrophic forgetting and preserving the model's inherent ability to generate aligned, readable explanations. Experiments on multiple benchmark datasets are reported to show outperformance over baselines in classification metrics and strong post-training explanation quality.

Significance. If the central claims hold after addressing the gaps below, the work would be significant for practical LLM deployment in decision-making settings. It targets the well-known tension between discriminative fine-tuning for calibrated probabilities and retention of generative capabilities, offering an empirical path to joint optimization that could improve interpretability without full capability degradation.

major comments (2)

[Experimental results / evaluation] Experimental results (as summarized in the abstract and implied in the evaluation sections): The central claim that CLSGen enables robust probability estimation 'without sacrificing the model's inherent explanation-generation capabilities' requires evidence that explanation quality is preserved relative to the untuned base model. No direct before/after quantitative comparison (e.g., same prompts, same alignment/readability metrics) is provided for the base LLM versus the CLSGen-tuned model. This is load-bearing, as any observed post-training quality could reflect partial linguistic collapse that the dual-head design failed to fully prevent.
[Results and baselines] Results and baselines description: Outperformance on AUROC and F1 is claimed, but the manuscript supplies no details on the exact baseline models, hyperparameter choices, statistical significance tests, or potential post-hoc data filtering. These omissions prevent assessment of whether the reported gains are robust or support the superiority of the dual-head plus data-construction approach.

minor comments (2)

[Abstract] The abstract refers to 'strong alignment' and 'high readability' for explanations but does not specify the quantitative metrics or evaluation protocol used to measure these properties.
[Methodology] The data construction strategy is described at a high level; adding concrete examples or pseudocode would improve reproducibility of the joint optimization procedure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and describe the revisions we will incorporate to strengthen the manuscript.

read point-by-point responses

Referee: Experimental results (as summarized in the abstract and implied in the evaluation sections): The central claim that CLSGen enables robust probability estimation 'without sacrificing the model's inherent explanation-generation capabilities' requires evidence that explanation quality is preserved relative to the untuned base model. No direct before/after quantitative comparison (e.g., same prompts, same alignment/readability metrics) is provided for the base LLM versus the CLSGen-tuned model. This is load-bearing, as any observed post-training quality could reflect partial linguistic collapse that the dual-head design failed to fully prevent.

Authors: We agree that a direct quantitative before-and-after comparison of explanation quality is necessary to fully support the claim of preserved generative capabilities. The current manuscript reports strong post-training alignment and readability but does not include side-by-side metrics against the untuned base model using identical prompts. In the revised version we will add these evaluations, reporting the same alignment and readability metrics for both the base LLM and CLSGen-tuned models. revision: yes
Referee: Results and baselines description: Outperformance on AUROC and F1 is claimed, but the manuscript supplies no details on the exact baseline models, hyperparameter choices, statistical significance tests, or potential post-hoc data filtering. These omissions prevent assessment of whether the reported gains are robust or support the superiority of the dual-head plus data-construction approach.

Authors: We acknowledge that greater specificity is required for reproducibility and to allow readers to assess robustness. While the manuscript outlines the baseline approaches, we will expand the experimental setup section to explicitly list the exact baseline models and configurations, all hyperparameter values and search ranges, the statistical significance tests performed (including p-values), and any data filtering or preprocessing steps applied. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical framework without derivations or self-referential reductions

full rationale

The paper introduces CLSGen as an empirical fine-tuning framework consisting of a dual-head architecture, training methodology, and data construction strategy for joint classification and explanation generation in LLMs. All claims rest on experimental results across benchmark datasets (AUROC, F1, alignment, readability) rather than any closed-form derivations, equations, or first-principles predictions. No self-citations are invoked as load-bearing uniqueness theorems, no parameters are fitted then renamed as predictions, and no ansatz or renaming of known results occurs. The work is self-contained against external benchmarks and does not reduce any result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard LLM fine-tuning assumptions and the effectiveness of the dual-head plus data strategy; no explicit free parameters, new physical entities, or ad-hoc axioms are stated in the abstract.

axioms (1)

domain assumption Dual-head fine-tuning can be performed without catastrophic interference between discriminative and generative objectives
Central to the claim that probability estimation can be added without losing explanation capability

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12 Preprint. Under review. A Appendix A.1 Statistics of the results In this section, we provide additional statistics for the results reported in the main text, which were omitted due to space constraints. Dataset/task Metric Verb prob Label pred CNN LCD benchmark 30 days AUROC0.0124 N/A 0.0015 F10.0047 0.0123 0.0457 Prec. 0.0027 0.0097 0.0315 Recall 0.01...

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