Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches

Amirhossein Yousefiramandi; Ciaran Cooney

arxiv: 2512.12677 · v3 · pith:ASAMY472new · submitted 2025-12-14 · 💻 cs.CL · cs.AI

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches

Amirhossein Yousefiramandi , Ciaran Cooney This is my paper

Pith reviewed 2026-05-25 07:21 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords causal LLMstext classificationfine-tuningLoRAembedding-basedinstruction-tuningparameter-efficientpatent classification

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The pith

Attaching a classification head to a causal LLM's final-token embedding matches instruction-tuning on single-label tasks while training 10 to 30 times fewer parameters.

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

The paper tests two resource-efficient ways to adapt decoder-only LLMs for text classification. One attaches a lightweight head to the model's last token embedding and trains it; the other instruction-tunes the model to produce classification answers in prompt-response format. On single-label patent data the embedding method equals or beats the instruction method while updating far fewer weights. On multi-label data the instruction approach only competes when given a much larger training budget. Both beat or match fine-tuned BERT models on the single-label case.

Core claim

On a proprietary 5-class single-label patent corpus and the public WIPO-Alpha multi-label set, the embedding-based method matches or exceeds the instruction-tuned method on single-label classification while training 10 to 30 times fewer parameters; instruction-tuning is competitive only in the multi-label regime and only with at least 100M trainable parameters. Both methods are competitive with or surpass fine-tuned domain-specific BERT models on single-label tasks.

What carries the argument

The embedding-based approach that extracts the final-token embedding from a pre-trained causal LLM and feeds it to a classification head, trained with 4-bit quantization and LoRA.

If this is right

On single-label tasks the embedding approach can replace instruction-tuning with large savings in trainable parameters.
Instruction-tuning needs substantially larger trainable budgets (at least 100M parameters) to become competitive in multi-label settings.
Both methods can match or exceed fine-tuned domain-specific BERT models on single-label patent classification.
A distillation step can recover BERT-class inference speed from either LLM approach.

Where Pith is reading between the lines

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

For many classification problems the internal states of a causal LLM may already be adequate for downstream use without retraining the model's generation behavior.
When training compute is the bottleneck, practitioners can default to the embedding-head method for single-label problems.
The same embedding extraction could be tested on other sequence tasks such as named-entity recognition or sentence-pair classification.

Load-bearing premise

The final-token embedding already supplies a sufficient sequence representation for classification without any extra pooling or architectural changes that would change the comparison.

What would settle it

On the same two patent benchmarks, if the embedding-head method falls below the instruction-tuned method by more than the reported 95 percent bootstrap intervals when both are trained with identical parameter budgets, the performance claim is falsified.

Figures

Figures reproduced from arXiv: 2512.12677 by Amirhossein Yousefiramandi, Ciaran Cooney.

**Figure 2.** Figure 2: Wipo dataset F1 scores by total number of model parameters for the embedding method (r=8, r=16) and the [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Figure 3: F1-Micro by model and dataset using decoder tuning. Error bars show 95% confidence interval computed [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 4.** Figure 4: F1-Micro by model and dataset using instruction tuning [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: F1-Micro by model for DatasetCLV dataset .Variability is summarized using the sample mean and standard [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗

**Figure 6.** Figure 6: Micro-F1 by model for WIPO dataset. Variability is summarized using the sample mean and standard [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗

read the original abstract

We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource constraints. Two approaches are investigated: (1) attaching a classification head to a pretrained causal LLM and fine-tuning it on the task, using the LLM's final-token embedding as a sequence representation, and (2) instruction-tuning the LLM in a prompt-to-response format for classification. To enable single-GPU fine-tuning of models up to 8B parameters, we combine 4-bit model quantization with Low-Rank Adaptation (LoRA) for parameter-efficient training. Experiments on two patent benchmarks, a 5-class single-label internal corpus and the public WIPO-Alpha multi-label dataset with 14 categories, show that the embedding-head approach matches or exceeds fine-tuned BERT baselines on single-label classification while training 10-30x fewer parameters. Instruction-tuning is competitive only in the multi-label regime, and only with substantially larger trainable budgets of at least 100M parameters. These results demonstrate that directly leveraging the internal representations of causal LLMs, together with efficient fine-tuning techniques, yields strong classification performance under limited computational resources. We discuss the advantages of each approach and outline practical guidelines and future directions for optimizing LLM fine-tuning in classification scenarios.

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

Embedding method with final-token head and LoRA gives a clear parameter-efficiency edge on single-label patent classification, but the performance difference lacks statistical significance.

read the letter

The paper's main result is that for single-label classification on patent data, fine-tuning a classification head on the final token embedding of a causal LLM (with 4-bit quantization and LoRA) matches or beats instruction tuning while using 10-30 times fewer trainable parameters. On the multi-label WIPO-Alpha set the instruction approach only competes once the trainable budget reaches around 100M parameters. Both beat or match domain BERT baselines on the single-label task and generalize directionally to AG News. They include McNemar tests, bootstrap confidence intervals, and ablations on pooling, verbalizer, and calibration, plus a distillation step for inference speed. That package of controls is the strongest part of the work; it lets a reader see exactly where the efficiency claim holds and where it does not. The central limitation is that the numerical advantage is consistent in direction but not significant at p < 0.05, so the efficiency story rests on the size of the parameter gap rather than a confirmed performance win. The final-token representation is a standard choice and the ablations test whether other pooling options would change the comparison, so that assumption is not hidden. The paper is aimed at practitioners who need to run LLM classification under single-GPU constraints and want concrete parameter budgets and guidelines rather than new theory. It is a solid empirical extension of existing LoRA and instruction-tuning literature, not a conceptual advance. I would send it to peer review so referees can check the hyperparameter details, data splits, and whether additional runs would tighten the statistical picture.

Referee Report

1 major / 2 minor

Summary. The paper compares two fine-tuning strategies for causal LLMs on text classification under resource constraints: (1) an embedding-based approach attaching a classification head to the final-token embedding of a 4-bit quantized LLM with LoRA, and (2) instruction-tuning in a prompt-to-response format. Experiments on two patent benchmarks (proprietary 5-class single-label and public WIPO-Alpha multi-label) show the embedding method matches or exceeds instruction-tuning on single-label tasks while using 10-30x fewer trainable parameters; instruction-tuning is competitive only for multi-label with larger budgets (>=100M parameters). Both approaches are competitive with or surpass domain-specific BERT models on single-label tasks. The work includes McNemar tests and bootstrap CIs (directionally consistent but p>0.05), ablations on pooling/verbalizer/calibration, generalization to AG News, and a distillation recipe.

Significance. If the empirical comparisons hold, the work provides practical value for efficient LLM deployment in classification by quantifying the parameter-efficiency tradeoff between embedding-based and instruction-based fine-tuning, with explicit statistical qualification and ablations. The transparency around non-significant differences and the single-GPU feasibility for up to 8B models strengthen its utility for resource-constrained settings.

major comments (1)

[Abstract] Abstract and implied results: The central claim that the embedding-based method 'matches or exceeds' instruction-tuning on single-label classification rests on numerical differences whose statistical tests (McNemar and bootstrap Delta F1 CIs) show directional consistency but p>0.05; this qualification is already noted but should be reflected more explicitly in the headline claim to avoid overinterpretation of the efficiency advantage.

minor comments (2)

Methods or appendix: Full details on LoRA rank, scaling, exact data splits, and hyperparameter search procedures should be provided to support reproducibility of the 10-30x parameter reduction and performance numbers.
The multi-label regime comparison would benefit from explicit reporting of the trainable parameter budgets used for the instruction-tuned models to clarify the 'at least 100M parameters' threshold.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thorough review, the positive evaluation of the work's practical value, and the recommendation for minor revision. We address the single major comment below and will incorporate the suggested clarification.

read point-by-point responses

Referee: [Abstract] Abstract and implied results: The central claim that the embedding-based method 'matches or exceeds' instruction-tuning on single-label classification rests on numerical differences whose statistical tests (McNemar and bootstrap Delta F1 CIs) show directional consistency but p>0.05; this qualification is already noted but should be reflected more explicitly in the headline claim to avoid overinterpretation of the efficiency advantage.

Authors: We agree that the primary claim sentence in the abstract would benefit from a more explicit qualification of the statistical results to reduce any risk of overinterpretation, even though the abstract already contains a dedicated sentence on the McNemar tests and bootstrap CIs. In the revised version we will adjust the headline phrasing to: 'show that the embedding-based method numerically matches or exceeds the instruction-tuned method on single-label classification (directionally consistent but p>0.05) while training 10 to 30 times fewer parameters.' This makes the non-significant nature of the difference visible at the outset while preserving the reported efficiency advantage. revision: yes

Circularity Check

0 steps flagged

No significant circularity; purely empirical comparison

full rationale

The paper reports experimental results comparing embedding-based and instruction-based fine-tuning of causal LLMs on held-out patent and AG News benchmarks, with ablations on pooling/verbalizer/calibration, McNemar tests, bootstrap CIs, and parameter counts. No derivation chain, equations, fitted parameters renamed as predictions, or self-citation load-bearing premises exist; the final-token representation is a standard choice directly ablated in the work. All performance claims rest on independent measurements rather than reducing to inputs by construction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The paper rests on standard domain assumptions from the LLM fine-tuning literature and introduces no new entities; the only notable free parameter is the choice of LoRA configuration needed to fit training on one GPU.

free parameters (1)

LoRA rank and scaling
Hyperparameters selected to enable single-GPU training of up to 8B models; their specific values are not reported in the abstract.

axioms (1)

domain assumption The final-token hidden state of a causal LLM provides an adequate fixed-length representation of an input sequence for classification.
This premise directly enables the embedding-based method described in the abstract.

pith-pipeline@v0.9.0 · 5828 in / 1340 out tokens · 36407 ms · 2026-05-25T07:21:33.318504+00:00 · methodology

discussion (0)

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