Human-LLM Collaborative Feature Engineering for Tabular Data
Authors: Zhuoyan Li
Aditya Bansal
Jinzhao Li
Shishuang He
Zhuoran Lu
Mutian Zhang
Qin Liu
Yiwei Yang
Swati Jain
Ming Yin
Yunyao Li
Venue: ICLR 2026 Poster (2026)
Keywords: human-ai interaction
human-centered evaluation
Primary area: alignment, fairness, safety, privacy, and societal considerations

Abstract: Large language models (LLMs) are increasingly used to automate feature engineering in tabular learning. Given task-specific information, LLMs can propose diverse feature transformation operations to enhance downstream model performance. However, current approaches typically assign the LLM as a black-box optimizer, responsible for both proposing and selecting operations based solely on its internal heuristics, which often lack calibrated estimations of operation utility and consequently lead to repeated exploration of low-yield operations without a principled strategy for prioritizing promising directions. In this paper, we propose a human–LLM collaborative feature engineering framework for tabular learning. We begin by decoupling the transformation operation proposal and selection processes, where LLMs are used solely to generate operation candidates, while the selection is guided by explicitly modeling the utility and uncertainty of each proposed operation. Since accurate utility estimation can be difficult especially in the early rounds of feature engineering, we design a mechanism within the framework that selectively elicits and incorporates human expert preference feedback—comparing which operations are more promising—into the selection process to help identify more effective operations.
Our evaluations on both the synthetic study and the real user study demonstrate that the proposed framework improves feature engineering performance across a variety of tabular datasets and reduces users’ cognitive load during the feature engineering process.

Published as a conference paper at ICLR 2026
HUMAN-LLM COLLABORATIVE FEATURE ENGINEER- ING FOR TABULAR LEARNING
Zhuoyan Li', Aditya Bansal', Jinzhao Li", Shishuann He Zhuoran Lu', Mutian Zhang', Yiwei Yang', Qin Liu', Swati Jain', Ming Yin', Yunyao Li Purdue University AAdobe SUICC
*University of Washington
'UC Davis
ABSTRACT
Large language models (LLMs) are increasingly used to automate feature engi- neering in tabular learning. Given task-specific information, LLMs can propose diverse feature transformation operations to enhance downstream model perfor- mance. However, current approaches typically assign the LLM as a black-box optimizer, responsible for both proposing and selecting operations based solely on internal heuristics, which often lack calibrated estimations of operation util- ity and consequently lead to repeated exploration of low-yield operations with- out a principled strategy for prioritizing promising directions. In this paper, we propose a human-LLM collaborative feature engineering framework for tabular learning. We begin by decoupling the transformation operation proposal and se- lection processes, where LLMs are used solely to generate operation candidates, while the selection is guided by explicitly modeling the utility and uncertainty of each proposed operation. Since accurate utility estimation can be difficult espe- cially in the early rounds of feature engineering, we design a mechanism within the framework that selectively elicits and incorporates human expert preference feedback-compante
which operations are more promising-into the selection process to help identify more effective operations. Our evaluations on both the synthetic study and the real user study demonstrate that the proposed framework improves feature engineering performance across a variety tabular datasets and reduces users' cognitive load during the feature engineering process.
INTRODUCTION
Today, Al-powered models are increasingly integrated into diverse applications, such as fraud de- tection (Yang et al., 2025) and online platform recommendation (Alfaifi [024] Resnick & Varian 1997). where tabular data remains one of the most popular data modalities represent structured information like financial transaction logs or user profiles Nam et al. (2023). The quality of the feature engineering, which transforms feature columns into meaningful representations, often plays a critical role in determining the performance of AI models. To reduce manual effort in the feature engineering, traditional AutoML-based methods (Zhang al. 2023) Hrickson et al.) 2020) have been developed to automate feature construction by applying predefined transformation oper- ators over feature columns, which often lack the task-specific understanding and tend to generate redundant features that lead to suboptimal model performance.
Recent advances in large language models (LLMs) (Achiam al., 2023), with their strong language understanding and reasoning abilities, have motivated numerous studies to directly apply LLMs in feature engineering for tabular prediction tasks, aiming to leverage their semantic understanding to create informative features to improve the downstream model performance (Bordt et al.) 2024]] Han jet al., 2025). For example, CAAFE (Hollmann et al., (023) uses the task description together with the semantic description of each feature to prompt meaningful features. Nam et al. treats the LLM as a black-box optimizer that proposes and refines feature generation rules using feedback from the validation score of the downstream AI model and the verbalized reasoning distilled from a fitted decision tree. Although these approaches can improve performance, the current approaches typically assign the LLM both the role of proposing feature transformation operations and the role of selecting among them, so that the entire process is driven purely by the model's 's internal heuristics, which often lacks calibrated estimates of the utility and the
LLM to iteratively synthesize semantically

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uncertainty of each operation and in turn can lead to repeated exploration of low-yield operations without a principled strategy for prioritizing more promising directions. As a result, it tends to waste evaluation budget on low-yield transformations and performs poorly when the number of feature engineering iterations is limited.
In this paper, we propose human collaborative feature engineering framework for tabular learning. We begin by decoupling the transformation operation proposal and selection processes, where LLMs are used solely propose diverse transformation operation candidates based on their internal heuristics and understanding of the current task, while the selection is guided by explicit modeling of the utility and uncertainty of each proposed operation. However, the estimated util- ity of feature operations can often be poorly calibrated against their actual utility, particularly in the early rounds of feature engineering when only limited observational data is available to fit the e estimation model. In such cases, human expert collaborators (e.g., machine learning practitioners), who have accumulated domain knowledge about which types of feature transformations are likely to be beneficial, can provide qualitative insights to support more informed selection among the LLM- proposed feature operations. To incorporate such qualitative knowledge into the selection process in tractable manner, following prior work (AV et al., 2022 Xu et al. 2024a), we consider the form of preference feedback T from the human, where they compare pairs of feature operations to indicate which is better. To effectively leverage this form of human preference feedback without incurring excessive human cognitive cost, we design a mechanism within the framework that selectively elic- its preference feedback from the human collaborator only when the potential gain from the human feedback can justify this additional human effort. To evaluate the effectiveness of the proposed framework, we first conducted a synthetic study across a variety of tabular datasets. Compared to different baselines including both AutoML methods and LLM-powered feature engineering meth- ods, our method exhibits consistent improvement when evaluated with different downstream models. We then conducted a user study to further understand how actual users collaborate with the LLM in our framework. We observed that our algorithm can also improve the actual feature engineering performance and reduce the cognitive load experienced by human experts during the process.
2 RELATED WORK
Large Language Models (LLMs) for Tabular Learning. Recent advances in large language models
(LLMs) (Achiam et al.) alla||22.22 (202)), with their strong language understanding and reasoning abilities, have motivated numerous studies apply LLMs tabular prediction tasks (Wang et al. 2023 Ko al. 2025 Bouadi et al. 2025 Zhang et al. 2024 Han et al. [amm Nam et al. 2024b).. One line of work adapts LLMs by converting structured and tabular data into natural language and then leveraging
zero- or fho- inference or fine-tuning of LLMs (Dinh et al. 2022 Hegselmann et al., 2023
Yan et al. 2024). More recently, researchers have explored using LLMs to directly propose and select new features from the original feature columns and task descriptions to augment predictive performance (Hollmann et al. 2023 Bordt et al., 2024| Han et al., 2025 Abhyankar et al.) 2025). For example, Nam et al. treats the LLM black- box optimizer which proposes and refines feature generation rules using feedback from the validation score of the downstream machine learning model and the verbalized reasoning distilled from fitted decision tree. In this paper, we ask whether it is possible to decouple the generation and the selection process by explicitly modeling both the utility and the uncertainty of LLL--generted feature operations with Bayesian optimization, as to guide the selection and composition of features in a more efficient and principled manner.
Human-Al Collaboration for Interactive Machine Learning. There has been growing interest among researchers in leveraging human knowledge to enhance standard machine learning pipelines. Many studies investigate the factors that influence effective human-Al collaboration (Lai et al.] 2021 Buçinca et al.) 2021: Chiang et al., [0023] 2024). For example Wang et al.) conducted in- terviews with industry practitioners to understand their experiences with AutoMI. systems. Some recent work has focused on redesigning the interaction between humans and models during the learning process (Mozannar et al. 2023 Wei et al.) 2024 De Toni et al.) 2024 Alur et al., 2024). In this line of research, collaboration is operationalized through mechanisms such as modifying the objective functions to promote human-AI complementarity (Bansal et al. 2019a b. Mahmood et al.
As shown in prior research (Kahneman & Tversky 2013), this pairwise preference format enables humans more effectively express their internal judgments compared to directly evaluating individual instances.
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2024]. adjusting how to present information like Al confidence explanation based the human behavior modeling (Vodrahalli et al. 2022] Li & Yin 2024 Li et al. 2024 Lu et al. 2023 Li et al,
2023b 2025), and reducing human workload by shifting their role from primary decision maker to on-demand advisor, typically through selective querying paradigms that solicit human input only
when it is expected to be most valuable (AV et al.) 2022 Xu et al.) [202a by Souza et al. 2021] Hvarfner et al., (2022). In this paper, we ask how human expertise should inform and shape the LLM-powered feature engineering process in order achieve more complementary outcomes.
METHODOLOGG
3.1 MOTIVATION AND PROBLEM FORMULATION
In this study, we explore the scenario of human- LLM collaborative feature engineering in supervised tabular prediction tasks, and now formally describe it. Let D { yı) yi) denote the tabular dataset, where each ø; E Rd is d-dimensional feature vector for the task instance, and the j-th dimension corresponds to a feature column with name Cj E C' = {ci. .... ca). The label y is the target output, with y {0, 1, .... K) for K-class classification task and y: IR. for regression task. By splitting the dataset D into the training set Dinin and the validation set D tabular learner fubalar is trained on Prain and evaluated on Dval by aask-appropriate score function J (fubbllrri Pval) (e.g., AUCROC for classification task, and negative MSE for regression task). Let & denote the space of candidate feature transformation operations. Each operation & maps the original data matrix X' E Rnxd into a new feature column 2. E R". The updated training and validation datasets are represented as Dusin @ e and Vaa e, respectively, where represents the addition of the new feature column to the existing dataset. To evaluate the utility of feature operation e, we first define the black-box utility function g()):
g(e) = (fubulars v 1 e) where ftabular = arg min L(f; Duain + +e)
(1)
Here, C is the loss function used to train the tabular learner (e.g., cross-entropy for classification or MSE for regression). Since feature engineering proceeds in multiple rounds, where at each round t, evaluate the utility of a selected transformation e,, and update Duain and Dval only if g(ez) > 0.
The goal of feature engineering in each round is to identify the operation that maximizes predictive performance on the current validation set:
e = arg max g(e)
eEE
However, the utility function g(-) is observable only after an expensive refit and re-evaluate cy- cle on the updated model. Prior work LLM-powered feature engineering (Hollmann et al. 2023 Nam et al. 2024a) addresses this black-box optimization by treating the LLM as an implicit surrogate for g()), with the LLM M handling both proposal and selection of the optimal trans- formation e* In round t of feature engineering process, given the observed performance history H, {(e,, (= column descriptions C, and dataset-level metadata including the prediction objective Meta, the LLM samples set of N candidates S, {el, ...; distribution:
S Par(- He, C, Meta)
(3)
The LLM M would then select a candidate e, E S₁ based on internal heuristics about which transfor- mation might be most useful in the current round. In this paper, we ask whether it is possible de- couple transformation operation proposal and selection processes, where the LLM M serves solely as a operation proposal generator, sampling candidates from the distribution Pas He, C. Mcta), and the selection is guided by explicitly modeling the utility of LLM LLM-proposed feature operations. To address the black-box nature of the utility function g(-), we adopt Bayesian optimization ap- proach that first constructs an explicit surrogate model g(-) based on the history H, to estimate the utility and uncertainty of cach operation. Based on this surrogate model, we next move to explore how to select among the LLM-proposed candidate operations in each round, and how to selectively elicit and incorporate human preference feedback into the model selection process.
3.2 SURROGATE MODEL FOR APPROXIMATION OF THE UTILITY FUNCTION g(-)
In this part, we describe how construct a surrogate model g(-) to approximate g(-).
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Encoding of LLM-PPoposed Feature Operations. To allow the surrogate model effectively ap- proximate the utility function g()), each LLM proppose feature transformation is first mapped into vector representation. We first apply pretrained embedding encoder Pembeslding (e) to obtain dense semantic representation of the operation e, which captures compositional and Tinguistic re- lationships among candidate operations that are learnable by the surrogate model. However, the semantic embedding @embedddigg (e) alone may be insufficient when multiple feature columns have similar linguistic descriptions. To address this, we incorporate column-usage encoder @columa (e) to provide explicit structural information about which feature columns are used in the feature op- eration. Specifically, Ocoluma() (-) maps an operation e into a binary vector m E {0, 1)", where m [ is used in e | and each c; E C' denotes column from the input data matrix. Finally, the overall embedding representation for the surrogate model ğ is obtained by concatenating the two components:
((e) = [ dembbediing (e), @columm (c)]
(4)
Bayesian Neural Network as Surrogate Model. In Bayesian optimization, Gaussian processes (GPs) are widely used as surrogate models in different tasks such as hyperparameter tuning (Snoek) et al. 2012) and system design (Wang et al. 2024), which typically involve relatively simple, low- dimensional feature spaces that GP's can scale effectively (Xu et al. (202c)). In contrast, our setting requires modeling LLM- -roppooseed feature operations that are expressed in lanural- format and mapped via an encoder 6(-) into a high-dimensional representation, where GPs often strug- gle to scale and capture non-stationarity (Snock et al. 2015). Therefore, in this paper, we opted for Bayesian neural network (BNN) as the surrogate g. which can provide greater scalability and expres- siveness for modeling high-dimensional, language-derived feature embeddings (Li et al.) 2023a).
Specifically, given the performance history the current round H, {(e, {ee,)) the surro- gate 9 constructed
Bayesian neural network parameterized by 0, which defines a predictive model (((()); 0) over candidate operations to approximate their true utility g(e). To capture un- certainty, instead of learning a single point estimate of 0, we adopt a Bayesian approach and set out to learn the posterior distribution of the model parameters conditioned on history H,, i.e., P(@ H.). As directly computing this posterior P(@ H,) is intractable, we learn the variational distribution qe (o) = (0; M,, Σ,) by minimizing the KI. divergence between ge (0) and the true posterior:
/ (0) log P(O H.)
9:( do
(L( H.)
9(0)) = [a((0) log
(5)
P(0) log P(T 0) + log P(H.) do
= (L(()))))P(( Eq,
P(H. 0) P(H, log P [(H)]]
where P(O) is the prior distribution over model parameters and P(H,) is constan Given the learned variational posterior qu (0). the predicted expected utility Me(e) of a candidate operation e and its corresponding uncertainty of (c) are computed as:
(e())= ( [(((()):))
de(c) = Em,co) (o( 0) me(e)²
(6)
Lemma 3.1. At round 1, the L.M M proposes a set of candidate operations St. For any 8 1), with probability at least 1 1-5, the deviation between the actual utility g(e) and the predicted expected utility pr(e) is uniformly bounded for all E Si:
IP Vt 2 1, Ve E S.: (g(e) - mr(e)l S VBhor(e)) 2 1-8, B, = 2 log
|S 30
Proof sketch. Assuming the actual utility function g(-) can be linearly represented in the surrogate feature space ((-), the standardized error (g(e) - (4(e)) (o)))/a((() is L-sub-Gaussian, which gives the tail bound (((g(() (e())) > (c)) < 2c 2w=22/ /2, By applying a union bound over S,, we can then establish the confidence event in Equation. 7 The full proof is provided in Appendix A.
Given the predicted expected utility (e(e) and the uncertainty (e) of each candidate operation at round i of the feature engineering process, when human expertise not available, the selection is solcly based on the surrogate model's estimation. We adopt the Upper Confidence Bound (UCB)
selection function (Auer et al. 2002; AV et al. 2022 Xu ct al. 2024a) to balance exploitation of
"In this study, we instantiate dhemhedding with OpenAl's I's text-eebeddinng iobbeeddigg--3 3-mmal model. IIn this study, we set P(0)- =0, 7) as the prior over network parameters.
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high predicted utility and exploration of uncertain operations. Specifically, for each round T and LLM propposed candidate operation e E St, the UCB selection function is defined as:
(e) pc(e) + VBla((e)
(8)
[S 2,
where Br 2 log
is set according to Lemma 3.1 which guarantees that, with probability
36
least 1 g(e) E [LCB,(e). (e), (c] for E St, where LCBB(e) = (t(ee) ((e).
3.3
SELECTION OF LLM-PROPOSED FEATURE OPERATIONS WHEN HUMAN EXPERTISE IS
AVAILABLE
When the human collaborator is available, we next proceed to explore how to selectively elicit and incorporate their preference feedback into the selection process of LL.M-proposed operations.
Selection of the Feature Operation Candidate Pair for Human Preference Feedback. Specifically, the human expertise in the feature engineering process is modeled as a stochastic oracle K. At round 1, given a pair of feature operations (ef; et), the oracle elicits binary response k(e,, eq) Z, e (+1,-1) where Z. = +1 indicates ei > ee and Z, = -1 indicates e > eq. We begin by describing how to select the pair (ef, to obtain human feedback at each round t. If the human collaborator x is not available at round t, we directly follow the UCB function (Equation () to select ef arg max.es, UCB,(e) (e) to evaluate in this round. When human expertise is available, an additional operation et can be selected from the remaining pool St \ {<() such that the preference feedback Z, over the pair (cf,e) yields the highest expected utility gain relative to the surrogate model's current choice e/- To define the expected information gain, let ef denote the true optimal feature transformation at round i. The prior regret of the current selection is defined r. g(e)) g(ef), which measures the utility gap between the surrogate's 's choice ef and the unknown optimum et. If we select another candidate et and query the human oracle x to obtain preference feedback Z,, we may revise the final decision to é, E {ef, e²") according to the feedback. The new regret becomes r/ = g((e)) - g(t)). Therefore, the expected utility gain is defined as the expected reduction in regret resulting from incorporating human preference into the selection process:
U Ule ei x) Ez, (r+ r'] = (g(e)) g(et))
Lemma 3.2. By the Lemma 3.7 let ef S₁ be the UCB choice, the the following holds for any operation
e² E St \ {eft and 1 TT
Ulel,
Ex 1 r <
(10)
Proof sketch. Under the feature operation selection pair (et, et), the human preference feedback can switch the round-! choice of feature operation between ef and eq. so T; -r', S max(((())) g(et), 03. By Lemma 3.1 we have g(et) < UCB, (et) and g(ef) 2 (ef,, which together gives the lemma. The full proof is provided in Appendix. A.2
Corollary 3.1. By Lemma 3.2 we have:
U Ule k3) S max
+ <maxxumBBBa (U') - LCBI(e)), 0} s (B((or(e))+
(II)
Based on Lemma
3.2 the candidate operation et is selected to be paired with ot for human prefer-
ence feedback:
argmax,ess UCB,(e)
(12)
Selective Elicitation for Human Preference Feedback. However, since eliciting human feedback inevitably incurs cognitive costs and increases the expert's 's workload, it is impractical to query the human collaborator in every round. Instead, feedback should be requested only when it is expected to yield gains that outweigh the associated costs, and incorporating human expertise can guide the selection algorithm toward more informed and effective decisions. Given the candidate pair (ef: et), we first consider whether human expert feedhack has the potential provide additional utility beyond the current selection. If (e) s LCB:(ef). then by Lemma 3.1 it indicates
'In this study, 8 is 1.1 control the confidence interval.
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that g(e)) S g(ef). In this case, the current feature operation of strictly outperforms the candi- date ef, leaving no possibility for human expertise to improve the utility further. To prevent this, first condition requires that the two confidence intervals should overlap to ensure that there mains uncertainty about which operation is better, thereby leaving the "room" for human expertise to potentially improve the selection:
(C1) Overlap: UCB > (c)
(13)
Even when overlap exists, however, not all human feedback are equally valuable. The second consid- eration is whether the underlying improvement of utility is sufficiently large justify the cognitive
cost of involving the expert. Let "/e denote the cost of eliciting human feedback each round | By Corollary 3.1 the maximum possible utility gain is upper-bounded by (B((oxee)) + (e )- To prevent unprofitable queries, we therefore impose the following requirement to guarantees the human feedback is only triggered when the potential utility improvement might outweigh the cost:
(C2) Uncertainty: v (o)(()) + (e))) Z Y
(14)
Taken together, the human feedback query is only elicited if and only these two conditions hold:
(CI) Overlap: UCB.(e)) > (c), and (C2) Uncertainty: VBl(o.(e)) + a.(e))) 7/ (15)
Posterior Selection with Human Preference Feedback. Once the condition is satisfied and pref- erence feedback Z, k[ee, et) is elicited, the model distribution (0)) for the surrogate model ÿ. which is constructed based on the past performance history H, using Equation [ serves as the model's s current belief about which feature operations may be useful. We then proceed determine the final feature transformation for round t by integrating this model belief g:(O) with the elicited human preference Zy Instead of making a direct selection based on Z,, we treat Ze as a proba- bilistic observation that provides information about the relative utility between the two candidate operations. Specifically, the feedback likelihood is modeled using a probit function:
P(Z, 0, elf e 4 [g 0) - )( where 0 ~ ())
(16)
where (--) is the standard normal cumulative distribution function (CDF), and r7 is hyperparam- eter that controls the confidence level of feedback | Assuming that the elicited human feedback 7,, derived from human expertise, is conditionally independent of the performance history H,, the posterior distribution of (0) can be updated as:
()
(T P(O Hi, ei, ei)) = (1) log P(O H,, Ze, ef ct)
ei,
d0
= = (d(()
log (0)
P(0 H.) log P(z, 0, e P(e H ei d0
(17)
R (0) |I ge (0)) : log P(z,) 0 el, e?) E log P(Z. He, ei, et)
With the updated posterior distribution (0), we can then make the selection of the final feature operation to be evaluated for this round t:
et
selected
argmax xe (c) UCB:(e)
(18)
Finally, Algorithm| 1 summarizes how does the proposed framework perform the iterative selection of LLM--poposedd feature transformation operations in the feature engineering process.
4 EVALUATIONS
4.1 EXPERIMENTAL SETUP
Dataset. Following previous work (Hollmann et al.) 2023 Bordt et al. 2024), we select 18 datasets from Kaggle and UCI Irvine, which contain a mix of categorical and numerical features for classi- fication or regression tasks. Since LLMs are trained on large-scale public data, which may include
7/s is empirically set to in this study to balance the trade-off between final performance and efficiency for the clicitation of human feedhack. bn is set in this study.

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Table 1: Comparing the performance of the proposed method, LLM-based baselines, and non-LLM- based baselines on 13 classification datasets in terms of AUROC (%) with GPT-4o as the backbone model for all 4LM--asee methods, evaluated using MLP and XGBoost as the tabular learning mod- els, respectively. The best method in each row is highlighted in blue, and the best baseline method highlighted in light blue, The number in the brackets O indicate the error reduction rate compared to the best baseline method. All results are averaged over 5 runs.
Donaset
OpenPi
AutoGlacn
CAAPE
MLP
OCTrre
(W/o human)
Oun
(wl luman))
Oun
OpenFi AutoGitoaa
CAARE
XGiboest
OCTTree
(Woo Iuman)
Ours
(w/ human)
Ours
flight
90.3
02.0
92.0
91.8
05.5 (ar
0713 las 19)
96.7
96.4
96.2
96.4
97.6 (40399)
00.0 (ar
wine
77.2
77.2
77.6
78.2
78.5 (8.45
75.7 (-23%)
$1.3
$1.00
80.9
82.1
82.5 (445%)
83.3 1477%
Iuan
95.3
$8.3
96.7
95.9
06.0 06.0
96.3 1-40%
56.2
96.0
96.1
96.5
95.9 (11.49)
97.1 (433.5)
diabetes
S1.1
62.4
82.8
62.8
88.0 (8299
83.0 (.22%)
64.1
53.9
63.9
84.4
86.2
84.8 (-249)
munic
84.1
$4.
66.3
26.5
H6.5 (2.29)
87.0
85.0
54.8
87.0
87.4
81.3
H8.5 17305
tnwel
80.4
50.3
81.1
81.7
82.0
82.5
63.6
53.2
63.6
86.6
85.5 (4449)
85, (-549)
aisegg
07.8
67.5
68.2
68.0
68
05.3
71.8
71.5
71.3
72.4
(
75.6 (-119))
53.7
53.2
36.7
$7.9
56.7
56.1
50.8
61.7
65.2 (x39%)
92222
92.6
92.4
50.5
94.3
051
adul:
50.5
90.8
91.3
91.3
922.0
65.0
pesorality
1601
56..2
comferston
90.0
91.2
91.0
Table 2: Comparing the performance of different LLMS as backbones for LLLM-based methods in terms of average AUROC (%) on 13 classification datasets, evaluated using MLP and XGBoost as the downstream tabular learning models, respectively. All results are averaged over 5 runs.
Model
Opennt
CAALE
MLF DC
Oone (W10)
Oun hamans
Openr Amrottionn
CNAPE
Xcifiwei OCe
UNI (17 (HMAA)
Loepstakers
853
R13
33.3
30.1
85.4
33.0
35.4
M5.6
87.3
85.2
OPE-35-mmh
81.
81.5
K311
84.1
546
85.
85.6
35.
85-2
36.0
86.5
K2.1
GPT-4o
83.5
83.5
54.3
84.7
85.6
36.7
87.4
57.4
5PT55
83.5
83.5
55.5
858
88,0
58.7
information on how to construct successful features for these widely used datasets, we additionally include proprietary company dataset of predicting users' conversion intentions, which cannot be accessed by the LLM, to provide a more robust evaluation. Detailed information about each dataset is provided in Appendix C.I
Baselines and Operationalizing the Proposed Method.
We consider both AutoML and LLM--basd feature engineering methods as baselines. For AutoML methods, we include OpenEE (Zhang For LLM-based methods, we consider 2024a). To evaluate the effectiveness of 1986) and XGBoost (Chen & Guestrin feature engineering, we employ MIP (Rumelhart et al.
et al. 2023) and AutoGluon (Erickson et al. (020).
CAAFE(Hollmann et al.) 2023) and OCTree(Nam al.
2016) as downstream prediction models. For non-I.L. methods, the feature engineering process proceeds until convergence to their best performance. For all LLM-based methods, we use GPT- 4o (OpenAll 2024) as the backbone model to generate feature operations in each round with a sampling temperature of 1, and the maximum iteration budget is set at 50. For the proposed method, the LLM generates 15 candidate feature transformation operations per prompt in each iteration. We specifically evaluate our method under two settings: one where the human collaborator is absent (w/o Humun), and one where the human collaborator is available (w/ Human) ). To simulate the w/ Human setting, we employ GPT-4o proxy as the human expert to provide the preference feed- back. In particular, for each dataset, we first fit a base classifier and use SHAP (Lundberg & Lee, 2017) to identify the most important features for the task. These feature importance scores are then converted into the expert prompts, enabling the proxy model to judge which feature pairs are more promising. Each dataset is randomly partitioned into 80% training and 20% validation sets, and this process is repeated for 5 iterations to evaluate each method's performance. For detailed prompt template used in each round, please see Appendix
4.2 EVALUATION RESULTS
Table presents the comparison of final feature engineering performance across 13 classification datasets using the proposed method, L.Led baselines, and AutoMI. baselines with GPT-4o model as the backbone to generate feature operations for all L.MM-bseed methods. Overall, we observe that both Ours (wo human) and Ours (w/ human) consistently outperform the best baseline methods across different datasets. Specifically, when using MLP as the evaluation model, Ours (w/o human) achieves an average error rate reduction of 7. 7.24%, while Ours (w/ human) achieves 8.96% compared to the best baseline. Similarly, when using the XGBoost as the evaluator, Ours (w/o human) achieves 9.02%, and Ours (w/ human) achieves 1 11.23% average error rate reduction over the best baselines. Below, we summarized key observations.
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purt In hamnart OChec
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07:00
hammard
OChice
06.72
OChrce
AUROC (%)
AUROC
AUROC
00
01.
90.0
85.10
Iteration
Iteration
Iteration
(a) Conversion
Flight
Titanic
72.5
(ammen)
61.3
CAAPe
CEAPF
31.2
AUROC (18g
AUROC (96)
602
AUROc
60.0
60-0
02.9
50.7
50.8
15
35
20
Iteration
Iteration
Iteration
(d) Heart
Wine
(f) Adult
Figure 1: Comparing the performance trajectories of the proposed method with two LLM-based baselines (CAAFE and OCTree) in the feature engineering process, using an iteration budget of 50 and MLP as the tabular learner across six tasks. Error shade indicates the standard error of the mean. LLM-based feature engineering methods outperform traditional non-LLM automatic feature engineering approaches. We found that feature engincering methods leveraging large language models (c.g., CAAFE, OCTree, and our proposed method) generally outperform traditional auto- matic feature engineering methods such as OpenFE and AutoGluon. We attribute this improvement to the strong semantic understanding, reasoning, and generative capabilities of LLMs, which enable them to efficiently generate effective feature transformation operations tailored to different tasks. Explicitly modeling utility and uncertainty of feature operations improves LLM--powered fea- ture engineering. Unlike prior LLM-based methods such as CAAFE and OCTree, our proposed approach generally leads to improved performance across different tasks. As a proof of concept, on the proprietary company-owned conversion dataset, OCTree achieves an AUROC of 91.1%. 1.. In contrast, Ours (w/o human) achieves 92.4% and Ours (w/ human) achieves 92.6% further under the same iteration budget as OCTree.
Incorporating human preference feedback improves performance. Finally, we evaluated the impact of incorporating human preference feedback on the performance our proposed feature engineering method. We observed that the addition of human feedback consistently improves the method's performance across most tasks compared to when no feedback is available. Specifically, in the MLP model, the incorporation of human feedback increases the average 1.72% compared to the best baseline. Similarly, in the XGBoost model, the improvement increases further to 3.21 % of the average error reduction. For the remaining regression tasks, observe sim- ilar performance trends across both baseline methods and our proposed method (see Appendix C.2)
reduction rate by
In addition using GPT-4o as the backbone model, we further compare the performance of all LLM-based feature engineering methods under alternative backbone generators. For the Ours (w/ human), we continue to use GPT-4o to simulate human preference feedback to ensure consistency across different backbone settings. As shown in Table 2 we evaluate four LLMs -Dee--ee-----
V3 (Liu et al. 2024). GPT-3.5-Tuubo (Ouyang et al., 2022 GPT-40 (OpenAl 2024). and GPT- 5 (0225))
2025) across the same 13 datasets using both MIP and XGBoost as downstream tabular models. Across all backbones, both Ours (w/o human) and Ours (w/ human) consistently outper- form LLM-based and non-LLM basclines, demonstrating that the proposed framework is robust to the choice of backbone model. Even with a weaker generator such as GPT-3.5 T-rrbo, our method can still maintain strong performance.
4.3 ANALYSIS OF ITERATIVE GAINS FEATURE ENGINEERING
To better understand the advantage of our approach over LLM-based baselines that are based solely on LLM's 's internal heuristics to generate and select feature operations, we analyze iterative gains throughout the feature engineering process by comparing the performance trajectories of our method (w/o human and w/ human) against -LMee baselines. Figures Ia Figure present the per- formance trajectories of method versus CAAFE and OCTrre during the feature engineering
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Table 3: Runtime breakdown of the proposed feature engineering pipelines when varying the
Table 4: Runtime breakdown of the proposed
feature engineering pipeline when varying the number of training instances from 1,000 to 100,000 with 100 initial feature columns.
number of initial features from 10 10,000 with full dataset of 10,000 instances.
Features
LLM
Surrogute (s)
UCB (s)
Eval (s)
Sumples
LLM (s)
Surrogate (s)
UCB (s)
Eval (s)
10
1.2
0.17
0.006
1.79
1,000
1.82
0.17
0.005
0.28
50
1.82
0.16
0.005
1.24
5,000
1.82
0.18
0.005
0.89
100
1.82
0.19
0.005
1.78
10,000
1.82
0.23
0.006
1.47
1,000
1.82
0.20
0.009
8.4
50,000
1.82
0.18
0.006
5.22
10,000
1.82
0.57
0.018
23.4
100,000
1.82
0.18
0.005
10.65
process, under iteration budget of 50, using MLP as the tabular learner. Visually, we observe that
unlike CAAFE and OCTree, which often become trapped in local optima or experience performance stagnation, method is able to identify high-impact feature operations that lead to notable perfor- mance jumps at various points and make steady progress during the iteration process. Furthermore, when investigating the impact of incorporating the selectively elicited human feedback, we observe that it often helps the algorithm redirect the feature search toward more promising operations, which enables the algorithm make sharper gains and achieve even higher performance compared the setting without human input. Similar trends are observed in other datasets (see Appendix C.3
4.4 ANALYSIS OF COMPUTATIONAL SCALABILITY OF THE PROPOSED METHOD
To understand the computational scalability of the proposed method, we measured the runtime of each component of the LLM-powered feature engineering pipeline within single iteration, which consists of four steps:
1. calling the L.M to generate candidate feature transformations.
2. fitting the BNN surrogate model using the accumulated observations.
3. computing UCB scores for the proposed candidates.
4. evaluating the selected transformation using the downstream tabular model.
Here, steps 2 and 3 are the specific computational components of our framework, while steps 1 and shared across all LLMM-based pipelines. We then conducted controlled experiments synthetic binary-clase mmary-classification datasets by systematically varying the number of initial feature columns and the number of data instances to directly measure how each component of the pipeline scales under datasets of different sizes. All feature columns were sampled from standard normal distributions, labels from a Bernoulli distribution, and an MLP was used as the downstream evaluator. All LLM calls were made using GPT-4o, and for the downstream evaluation step, we fixed the MLP training to a single epoch to ensure consistent and comparable timing across all conditions. We first fixed the dataset size to 10,000 instances and varied the number of initial features from 10 to 10,000. As shown in Table the time required for fitting surrogate model and UCB score computation increases only mildly with feature dimensionality, whereas the downstream evaluator dominates the runtime as the number of initial features grows. For instance, with 10,000 initial features, the surrogate model and UCB steps together account for only about 2.2% of the total runtime. Next, fixed the number of initial features to 100 and varied the dataset size from 1,000 to 100,000 rows. As shown in Table the surrogate fitting and UCB computation times remain nearly constant across all sample sizes and contribute only small percentage of the total runtime, because both operate at the feature-operation level and are independent of the number of data instances. In contrast, the downstream evaluation time increases with dataset size, as it requires training the MLP on the full dataset. Overall, these results indicate that our method scales favorably with both feature dimensionality and dataset size.
4.5
USER STUDY: HOW DO HUMANS PERFORM AND PERCEIVE WITH OUR METHOD?
Finally, to understand how actual users would collaborate with our algorithm complete the feature engineering task, we conducted a user study, where we selected the flight dataset of predicting passengers' satisfaction levels as the feature engincering task. We designed three treatments by varying the ways in which participants could collaborate with the LLM to complete the task:
The human fully leads the feature engineering process. In each round, the human needs to provide language instructions about what to do, and the LLM will parse the instructions to create the feature operations.
CONTROL:
SELF REASONING (SR): In this treatment, the overall algorithm remains the same as our pro- posed method, except that the querying mechanism is modified so the LLM autonomously de-
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AUROC (%)
0.98
Time (/min)
211
ALC
17
Scale
0.96
13
0.94
Performance
Completion time
Esgnitive load Trust L.M
Satisfcction
(a) Performance
(b) Completion time
(c) User experience
Figure 2: Comparing participants' average final feature engineering performance, completion time, and the user experience perceptions for the flight satisfaction prediction task the CONTROL, SR, and our ALG treatment, respectively. Error bars represent the 95% confidence intervals of the mean values. and denote statistical significance levels of 0.1, 0.05, and 0.01 respectively.
cides in each round whether to query human preference feedback, rather than being triggered by our algorithm.
OURS (ALg): In this treatment, the human preference feedback is triggered by our proposed method.
We recruited 31 machine learning engineers/researchers graduate students with a background in AI/MI. as participant Each participant was randomly assigned to one of the three treatments. At the beginning of the study, participants were provided with SHAP-generated explanations, which highlighted the importance of each original feature in the dataset, to equip them with task-specific expertise. Following this, participants proceeded with the feature engineering task in different ways of collaborating with the LLM depending on the assigned treatment until the iteration budget was exhausted. We used GPT-4o as the backbone model to generate feature transformation operations and MLP as the tabular learner to evaluate the utility of the feature operations, with the iteration budget of feature engineering process set to 40 for all treatments. Finally, participants were required to complete an exit survey to report their perceptions on the overall feature engineering process, In this survey, we used the NASA Task Load Index (Hart Staveland 1988) to measure the cognitive load experienced by participants during the feature engineering process, including their perceived mental demand, time pressure, effort level, and frustration. In addition, participants were also asked to rate the overall satisfaction, and the trust in using the system for future on a scale from I to 10.
Figure 2a compares the participants' average final feature engineering performance across three treatments. One-way ANOVA and subsequent Tukey's ' HSD test show that participants who worked under our ALG framework demonstrated significantly higher final feature engineering task perfor- mance compared to those in the CONTROL treatment (p 0.011). Figure comppars participants' average task completion time across the three treatments. Visually, we observe that participants in ALG framework exhibited higher time efficiency compared to the other two conditions. One- way ANOVA and further Tukey's HSD test show that participants under our ALG framework had significantly lower completion times compared to those in CONTROL (p < 0.001) and those in SR (p = 0.02). We finally move on to investigate how participants perceive their experience under dif- ferent treatments. As shown in Figure 2c] participants in ALG framework reported significantly lower cognitive load compared to those in CONTROL (p < 0.001) and those in SR (p 0.043). as well as a marginally significantly higher satisfaction compared to those in SR (p = 0.072).
CONCLUSION
In this paper, we propose a human-LLM collaborative feature engineering framework for tabular learning. We begin by decoupling the transformation operation proposal and selection processes, where LLMs are used solely to generate operation candidates, while the selection is guided by explicitly modeling the utility and uncertainty of each proposed operation. We then design mech- anism within the framework that selectively elicits and incorporates human expert preference feed- back into the selection process help identify more effective operations explore. Our evaluations on the both synthetic study and real user study demonstrate that the proposed framework improves feature engincering performance across a variety of tabular datasets and reduces users' cognitive load during the feature engineering process.
Thhis study was approved by the author's Institutional Review Board.
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A PROOF FOR TECHNICAL LEMMAS IN SECTION 3
A.1 COMPLETE PROOF FOR LEMMA
3.1
Given the surrogate model posterior distribution (0) (0 M,, Er), and the surrogate model g(o(e); 0), we first perform first-order Taylor expansion of ((((e); 0) around Mc:
0( = g(((e); M:) + Veg (o(e); M,) ( M,) + +R((E;0)
(((e)
dT(e)T
= =c((e) (e) w O)(e;))
where w N(w; E.). Since our surrogate model implemented as multilayer percep- tron (MLP) with I-Lipschitz activations (e.g., ReLU), based on the prior research (Boyd & Van- denberghe, 2004), this indicates that the second-order remainder R2(e; θ) is locally bounded as [R2 0] < C ||wl| where C' is model cetiitectuur-dependee constant. We therefore omit R₂ and use the linearized surrogate model the subsequent proof:
(in()(e); w) ce(e) + de(e)
T w, where w ~ N(w; 0, Ex)
Assumption A.1 (Utility Linearity). Given some model weights w" ~ NO, Et), the true utility of a feature transformation operation g(e) can be linearly represented in the feature space o(-), i.e. g(e) = ce(e) + de(e) w".
2(0) ((le) is Saub-Gaussinn,
Lemma A.1. By Assumption A.1 the standardized deviation v
acle)
i.e. E [exp(AA)] S exp
A.ER.
Proof. Given the predicted expected utility p4(e) of candidate operation and its corresponding uncertainty (e):
po(e) E (0)) [(((()))))) 0)],
(e) (o)(()) -M({))
As the surrogate model g(-) can be approximated as the linearized surrogate model (.a(.), the ex- pected utility (e) and the uncertainty (e) can be represented as:
(e) = E(c((e) + (e()) T w| = ce(e). (e) = Var(c((e) + di(e) T w) = xo(e) E(d((e)
Consequently, the standard deviation v is:
V g(e) me(e) ( w - ce(e)
+
(e(e) un Vor(e) Eje((e)
oi(e)
Vor(e) E(dr(e) [(
Since w* (O, E.), we have (e(c) Tw" W(0,0i(e) Eede(e)). Thus, v (i(c) w Voole)
( E(d((e)
(0, 1), and E(cxp(AV)) & cxp
2- )
Lemma 3.1 At round t, the LLM M proposes a set of candidate operations S.. For any (0, 1), with probability at least 1-5, the deviation between the actual utility g(e) and the predicted expected utility (e(e) is uniformly bounded for all E St:
vt 2 1, Se
( -H(e)) s VBloo(e)) 2 1 - 8, B. = 222
S.. ?2,
36
Proof. By Lemma A.I for any u > 0, we have:
(((g(() 14(e)) > (e)( = PP 1| > u) = =244=22-00 (- S 2c -2212
where is the standard normal CDF. At round t, given the LLM A.M--prooosed feature set S,, setting 3B and applying the union bound yields:
173e E S. ig(e) --((e)) (Boo(ee)
Σ
2e-2 e e-
eES,
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Let
Be =2log
38
Then
P(3e S;: lg(e)- - ue(e)) > vBcor(e)
68
22
Define the failure event round
Fi Ee S 1ø(c) mi(e)) (ioolc))
Apply the union bound over all rounds 2 1, we have:
66
P
U
P(F)
722 8
1=1
t=1
[=1
Thus, with probability at least the confidence bound holds uniformly for e ES, V 1.
A.2 COMPLETE PROOF FOR LEMMA
Lemma 3.2 By the Lemma 3.1
let ef E St be the UCB choice, the following holds for any operation
c E S. {ett and S ST:
c(
Ez,(r/ -r] re] S -CC((UC
(C),
Proof. After receiving the human preference feedback Z,, the posterior feature transformation oper- ation is selected from the pair (ce: ) The regret reduction is defined as:
= [g(e]) s({ - [g(c)) g(e() g(e)) - g(
where et is the optimal operation and eş {e/, } is the final selected one. Since ef E {ee, et the regret reduction bounded by:
r max {g(e)) g(e)), 0}.
Taking expectation over the preference feedback Z₁, obtain:
UlE (()) Ez,rr r!] max (g(e)) g(e)), 0}.
Under the confidence event in Lemma 3.1 for any St, the true utility is bounded as:
g(e) [pe(e) V&ea,(e), mi(e) + vBeo,(e)).
i.e.
g(e) < UCB:(e)= pe(c) + (e), 2 LCB((c) pi(e) (Blo.
Therefore,
g(e)) - g(e)) < UCB.(e)) LCB,(e)),
and thus,
U(CE,e(;n) s max (C)) LCB, 0}.
B ALGORITHM
Algorithm summarizes how does the proposed framework perform the iterative selection of LLM- proposed feature transformation operations in the feature engineering process.
C EVALUATIONS (ADDITIONAL DETAILS)
C.I DESCRIPTIONS OF THE DATASETS USED IN THE MAIN STUDY
We evaluate methods across 13 widely-used Kaggle classification datasets, covering diverse domains such as healthcare, finance, customer behavior, and public records. brief description of each dataset is provided below:
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Algorithm Iterative Selection of LLM-Proposed Feature Transformation Operations
Input: Training Dataset Draain validation set Deval, LLM M, tabular learner f. Iteration Budget T 1: Initialize H1 <- Ø, the feature operation pool Sp + Ø
2: for 1 to I do
3: S; - {feature operations proposed by .M in the round f} U U \ f selecteed
4: Fit the surrogate model qr (0) via equation
5: Select ef via equation g
6:
if human expertise is not available then
7:
efplecedd f- 1z
8:
else
9:
Select et via equation 12
if the trigger conditions (equation 15 all hold then
11:
Query human preference feedback: Z, +- k(e, e
Update the surrogate model of (0) via equation 17
13:
selected <-
arg max ce(,e( UCB via equation 18
14:
else
15:
selected et
f- ez
16:
Fit the tabular learner f on Draii @ exelecied and evaluate g ((ttleci))) on val @ eselected H1+1 e- {(ettlected (((ecelcc)))))
18:
if g(epclcctod > O then
Dirai t Drain @ eg edclocted, Deval Deval @ epelected return {eg0lcaod T
20:
Derain and Deval
hamarr
12.00
50.0
96.
AUROC (90)
AUROC (%5
AUROC (16)
10:00
24:3
Iteration
Iteration
Iteration
Iteration
(a) water
(b) travel
(c) personality
(d) loan
05.3
hammen
hamen)
85.0
Octree
AUROC (%)
04.
20.2
02.75
82.10
32.55
68.5
20
Iteration
Iteration
Iteration
(e) diabete
(f) customer
(g) usage
Figure C.1: Comparing the performance trajectories of the proposed method with two based baselines (CAAFE and OCTree) the feature engineering process, using an iteration budget of 50 and MI. as the tabular learner across seven tasks. Error shade indicates the standard error of the mean.
C.2 EVALUATION RESULTS (ADDITIONAL DETAILS)
Table C.2 compares the performance the proposed method, LLM-based baselines, and non-LLM- based baselines on 5 regression datasets in terms of errr reduction rate (%) compared with the base learner with GPT-4o as the backbone model for all LLM-based methods, evaluated using MLP and XGBoost the tabular learning models, respectively. We again observed that proposed methods can outperform the baselines including AutoML methods and LLM-poowred feature engineering approaches.
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Dataset
Description
#Features
#Instances 25,976
Task Type Classification
flight
Predict whether a flight is delayed based on schedule and airline attributes.
22
wine
Classify wine quality using physicochemi- cal test results.
11
945
Classification
loan
Predict loan approval based on applicant de- mographic and financial attributes. Diagnose diabetes from medical measure- ments of female patients.
13
45,000
Classification
Diabetes
21
40,000
Classification
titanic
Predict passenger survival on Titanic from demographic and ticket info. Predict whether a customer purchased travel insurance filed claim. Predict whether survey respondent reports using AT tools.
891
Classification
travel
8
63,326
Classification
ai_usage
10,000
Classification
water
Classify whether water potable given physicochemical properties.
3,276
Classification
heart
Diagnose presence of heart disease based on clinical measurements,
11
918
Classification
adult
Predict income exceeds S50K based on census demographic data.
32,561
Classification
customer
Predict whether telecom customer will churn from usage statisties.
20
7,043
Classification
personality
Predict Big Five personality types from sur- vey responses. Predict whether an online shopper will con- vert (make purchase).
2,900
Classification
conversion
178
15,000
Classification
housing
Predict house price based on the informa- tion of house. Predict burned area in forest fires based on geographic information.
20640
Regression
forest
12
517
Regression
bike
Predict daily bike rental counts from weather and calendar info.
17,414
Regression
crab
Predict age of crabs based on biometric mcasurements.
3,893
Regression
insurance
Predict the insurance cost.
6
1,339
Regression
Table C.1: Summary of the datasets used in our experiments.
Table C.2: Comparing the performance of the proposed method, LLLM-based baselines, and non- LL.M-based baselines on 5 regression datasets in terms of normalized root mean square error with GPT-40 as the backbone model for all L.-M-based methods, evaluated using MIPP and XGBoost as the tabular learning models, respectively. The best method in each row is highlighted in blue, and the best baseline method is highlighted in light blue. The number in the brackets O indicate the error reduction rate compared to the best baseline method. All results averaged over 5 runs.
Opealit
AutoGGoon
(win buman)
human)
Opeatit
AutoGlaon
(wia buman)
human)
housing
0.316
0:10
0.292
0.285
0.270
0.266
0:228
0.231
0.221
0.221
0.218
0:214
forew
1.851
1.851
1.750
1.724
1.655
1.6221
1.448
1.469
1.421
1.418
1.402
1.30K
hike
0.206
0.302
0.363
0.274
0.262
0.961
0.216
0.210
0.211
0.208
0.903
0-201
crub
0.256
0.288
0.258
0.252
0.242
0.200
0.226
0.230
0.221
0.222
0.219
0:217
insurance
0.511
0.512
0.473
0.482
0.467
0.462
0.554
0.385
0.382
0.38
0.370
0.378
C.3 ANALYSIS ITERATIVE GAINS IN FEATURE ENGINEERING (ADDITIONAL DETAILS)
Figure C. Figure C.Ig compare the performance trajectories of the proposed method with two
LLM-- baselines (CAAFE and OCTree) in the feature engineering process, using an iteration budget of 50 and MLP as the tabular learner across seven tasks.
D PROMPT TEMPLATE
The prompt for GPT-4o to propose feature transformation operations consists of two parts:
Introduction Prompt:
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You expert data scientist with deep expertise in feature engineering. have the ability to:
11 Analyze patterns in previous feature performance to guide new
feature creaticn
2) Reason about why certain features succeeded failed Design complementary features that address gaps the current feature set
4) Consider domain knowledge and statistical relationships in your feature design
Instruction Prompt:
Dataset Context:
Task type:
Mstric: [ROC_AUC_OR_OTHER]
Colunns (rame:type): [COLS_MITHLTYPES]
Target: (TARGET_NAME.
Notes (missingness, skew, constraints): <DATA_NOTES>
Recent performance feedback: |FERFORMANCE HISTORY
Remaining iteration budget: [BUDGET]
**Strategic Reasoning**
Based the performance feedback above, consider: what patterns you see the performance history?
2. What types of relationships might be missing from current features?
3. How can you build upon successful features while avoiding failed appreaches?
What domain-specific insights can guide your next feature ideas
**Takk*
Suggest up to complementary features** as list. Rach item should include:
"name"; baaeee__sss_ddetiffier""
"explanation": "<detailed reasoning: why this feature helps, how it builds on feedback>",
"reasoning": "<strategic thinking: what patterns from history inform this chcice>",
"code": "feature <python expression using df[...] helper ops>",
"expected_benefit" "<specific hypothesis about how this will improve the model>"
Important Guidelines:**
Do not suggest features that need label information.
Learn from rejected features avoic similar patterns that
failed
Build upor successful features create complementary variatiens
You can try to combino multiple (N > 2) features to create a new feature to capture à more complex relationship.
Ensure features are diverse and capture different aspects of the
data
Provide specific, actionable reasoning for each feature cheice
- For the reasoning process and expected benefit. analysis, be your best to be concise and clear.
Return ONLY the JSON list.
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The prompt for GPT-4o to simulate as the human expert to provide the preference feedback consists of two parts:
Introduction Prompt:
You senior ML scientist specializing in tabular feature engineering and feature evaluation. Given dataset context and SHAP-based feature importances from a baseline model, your goal is to judge which of two candidate feature operations is more likely to improve the downstream metric when added to the current pipeline. Avoid label leakage; prefer complementary, non-redundant transformations to improve the mode!. performance.
Instruction Prompt:
Task: Choose the more promising feature operation between and B
Dataset:
Task type: [CLASSIFICAATION_O) SO__O_RRRRGGSSSOOO
Metric: [ROC, AUC CO_.RR__OO0 OTHER]
Columns (rame:type): : [COLS_WITH
Target: BAARGET_NAMES
Notes (missingness, skew, constraints): <DATALNOTES>
Baseline model SHAP:
Base model: [MODEL_NAME,
Top SEAP features (name:score): <[ (fl, sl), (f2, s2), ...1>
Recent performance feedback: [FERFORMANCE HISTORY]
Remaining iteration budget: [BUDGET]
Candidates:
name: <A NAME>
code: BLLCCODEEEIPPPPE USING_dEN ... ...'J '>
rationale: <WHY THIS_ BHV__THISMIGHTT HELP>
name: <B_ NAME>
code:
'
rationale: WHY__THISMIGHT__AE HELP>
Decision instructions:
Prefer features that:
1) Leverage high-SHAP columns sensibly (monotone transforms, interactions, ratios/differrecee bins);
2) Complement accepted features (diversity > redundancy)
3) Are robust to outliirs/missingness and unlikely to leak labels.
Penalize features that: a) Duplicate existing ones; Are overly noisy/fragile; Output format (JSON only):
"choice"; "A" "B"
Return the JSON object.
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E EXAMPLES OF LLM-PROPOSED FEATURE OPERATIONS
Below we provide several examples of LLM-proposeed feature transformation operations selected by our algorithm. Each block shows the feature name together with its corresponding Python expres- sion.
DAYS_ DANSSSIINE..TTSSEEEEVVT....
feature 10.5 df dayss since. first eeerteeeett...e event_xxxxx XXXXxxxxxxxxx_xxxxx__x___x_____X event event_datel
+ 0.5 d' dayddsisscceeeeessaeeodddaoaaaae first_ event -
W.F.GLENNLNESS.BOOI
feature df Inflight wifi service' df Ltl'Cleanliness' Online booking'
Ease of
DIGITALEXXPRRIBNEE.C
gmean = (dfl Inflight wifi service' | das' of Online booking') ] dfl'Onling boarding' (1/3)
comfort tant ( I' comfert'] d'['Leg room service' 1) 2.0)
feature (gmean * [df["Cleanliness'] ** 0.5)) comfort
B0S BUS
feature (dfl' Type of Travel'] 'Business travel')
df[l' cleanniesss 'Seat comfort', 'Leg room service' mean axis-1)
feature (df('Age'] (df(' Highbe'] df HighHighColl'] + df[' HoarDleacoxAtak (1 d' Smoker'] df(' })
feature [df('Fruits'] + df[ Veggies'' + df PfL'Phyaactivity") / (
df[' Smoker' df HyyAlcchholCcnsumpp) + df ('Doocbcoost')
ACTIVITY.DIRT.RA.NM
feature (df('Fruits'] + Veggies) + df['Physactivity") / df[' Smoker') df Heyllcycccoll))nnsuuugg +
DIFF SON..AAALLEAALEE.TTRRRRA
feature df['Diffwalk') df['BMI')
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M01ILLNTTS.REDE..CHHPIIT
feature =
(df['Age'] HighBri df['BighChol df('
HoartissssorAttak
/ (1 + Smoker') (1 df('BKI']) ]))
(éf['Fruits'] of I' Veggiess' df["Physkcttiitty] + 1)
(1 + df[' coyAlscoholConsumpp d' NNNooccccCoatt
22


Reviews:
[{"review_id": "GSlVFhwqwU", "reviewer": "Reviewer_qUc9", "summary": "This paper explores the use of Bayesian neural networks (BNN) to explicitly model the utility of feature transformations models in LLM-based feature engineering. The LLM serves as a sampler of candidate feature transformations that are then selected using a UCB bandit based on utility predictions. The authors further introduce an approach to incorporating human preference feedback in utility modeling. They demonstrate through experiments that the proposed framework improves feature engineering performance and reduces users’ cognitive load.", "questions": "There may be typos in line 194 and Equation (6) on $q_t(\\theta))$.\n\nI wonder why the right-hand side of inequality (11) does not involve $\\mu_t(e^a_t)$ and $\\mu_t(e^b_t)$.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "I think the paper suggests a valid approach to LLM-based feature engineering. The use of utility models helps selectively evaluate promising features proposed by the LLM and thus can reduce the computation cost of feature evaluations. The paper also presents an approach to incorporating human preference feedback in utility modeling. The mathematical rationale of the framework is well explained.", "weaknesses": "It would be appreciated if the authors could provide further details on the setting of BNNs and the optimization algorithms for solving Equations (5) and (17).\n\nSome experimental details are missing. The standard errors across repeated runs are not provided. It is not stated how the parameters of downstream models have been selected, which could have an impact on the performance. \n\nThe experiments could evaluate other LLM backbones in addition to GPT-4o. It would be great to also include a study on the LLM generation cost and feature evaluation cost of the framework.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xaRNIMkZOz", "reviewer": "Reviewer_M3JD", "summary": "This paper addresses the limitations of existing feature engineering methods for tabular learning and proposes a Human-LLM collaborative feature engineering framework.The framework decouples the \"operation proposal\" and \"operation selection\" processes: LLMs only generate diverse feature transformation candidates based on task understanding, while selection is guided by explicit modeling of the utility and uncertainty of each candidate. For scenarios where utility estimation is inaccurate (especially in early iterations), the framework designs a selective human feedback mechanism—it elicits human experts’ pairwise preference feedback (comparing which operations are more promising) only when the potential gain of feedback outweighs the cognitive cost, thereby improving selection accuracy .In terms of technical implementation, a Bayesian Neural Network (BNN) is used as a surrogate model to approximate the black-box utility function of features, and the Upper Confidence Bound (UCB) strategy is adopted to balance exploitation and exploration when human feedback is unavailable.", "questions": "（1）This paper uses GPT-4o to replace expert scoring. Has consideration been given to how the preferences simulated by GPT-4o differ from those of real domain experts in terms of consistency, noise distribution, and preference biases?\nCan the performance gains observed under simulated feedback be replicated with real human feedback? How do feedback latency and fatigue affect the trigger strategy (C1/C2)? Is the cognitive cost threshold γ=4 based on real measurements? How does this threshold vary across different expert groups?\n（2）Why choose BNN over (sparse) GP, lightweight ensemble, or incremental update strategies? Is uncertainty estimation under high-dimensional ϕ(e) reliable for BNN?\n（3）How sensitive is the framework to different generators (GPT-4o vs GPT-3.5 vs open-source LLMs)? Will low-quality LLM-generated candidates drag down the entire selector?The paper could consider candidate quality metrics such as duplication rate, similarity to existing features, and potential label leakage, and analyze the correlation between these metrics and final performance.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "（1）Innovative Method Design with Clear Targets:The core idea of decoupling LLM’s \"operation proposal\" and \"operation selection\" effectively addresses the key limitation of existing LLM-powered methods (i.e., LLMs acting as black-box optimizers). By introducing explicit utility and uncertainty modeling, the framework avoids blind exploration of low-yield operations, and the selective human feedback mechanism balances the value of human expertise and cognitive cost.\n（2）Comprehensive Experimental Validation and Multi-Dimensional Evaluation:Diverse Dataset Coverage: The experiments include public datasets (Kaggle, UCI) and a proprietary enterprise dataset, avoiding overfitting to public data biases and enhancing the generalization of results.In addition to downstream model performance (AUROC for classification, normalized RMSE for regression), the study incorporates a user study using the NASA-TLX scale to measure cognitive load, which fully reflects the framework’s practical value in human-AI collaboration scenarios.\n（3）Technical Implementation and Transparency:The paper provides clear mathematical formulations for core components (e.g., utility function, surrogate model training objective, UCB calculation), and details key parameter settings. Appendices also include dataset descriptions, prompt templates, and proof of technical lemmas, which is conducive to reproducibility.", "weaknesses": "（1）Limitation in Human Feedback Simulation: The \"w/ Human\" setting in the experiment uses GPT-4o to simulate human experts, rather than recruiting real domain experts for feedback. This may deviate from the actual scenario where human experts rely on domain experience to make judgments, and the authenticity of feedback needs to be further verified.\n（2）Insufficient Discussion on Scalability: The paper does not discuss the framework’s performance in ultra-large-scale tabular data scenarios. The BNN surrogate model may face efficiency challenges in high-dimensional feature spaces, and the cost of LLM generating operation candidates may increase significantly.\n（3）Lack of Analysis on LLM Candidate Quality: The framework assumes that LLMs can generate high-quality operation candidates, but does not analyze the impact of LLM performance differences (e.g., GPT-4o vs. GPT-3.5, open-source LLMs like LLaMA) on the framework’s final effect. It also does not discuss how to handle low-quality candidates generated by LLMs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "g9l9Bmj0zf", "reviewer": "Reviewer_oY9U", "summary": "The paper proposes a human-LLM collaborative framework for tabular learning in which the generation of candidate features and selection are decoupled for better automatic feature engineering. Specifically, LLMs are used solely for the generation of operation candidates, while a separate surrogate model guided by Bayesian optimization is used to model the utility and uncertainty of those candidates for selection. As accurate estimation of the utility can often be difficult, the framework selectively elicits input from human experts. Empirical results using GPT-4o as a simulated human demonstrate promising performance compared to prior work.", "questions": "Q. Have the authors experimented with alternative embeddings for the surrogate model before deciding on $[\\phi_\\text{embedding}, \\phi_\\text{column}]$?\n\nQ. How does the method scale to datasets with varying numbers of initial samples and features?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. Clearly motivated framework. The authors rightly point out the conceptual limitation of existing LLM-based feature engineering approaches, which is that an LLM is used as a black-box optimizer for both proposing and selecting new features. Instead, the paper proposes using LLMs solely for generation with the selection guided by a Bayesian neural network as a surrogate model for utility and uncertainty estimation.\n\n2. Principled human-in-the-loop mechanism. For more accurate estimate utility, the paper adds a mechanism to elicit human expert preference feedback. In order to make more effective use of human expertise, the propose mechanism selectively request human feedback based on the estimated potential for improved selection (\"overlap\" and \"uncertainty\").", "weaknesses": "1. Missing ablations on the backbone model. The authors primarily evaluate GPT-4o as the backbone model for LLM-based feature engineering methods. It would be useful to analyze how much the joint feature proposal and selection is an issue depending on the backbone model, e.g., comparing open vs. closed models, different model sizes, and reasoning-enabled vs. non-reasoning models.\n\n2. Missing experiments with humans of varying levels of expertise. In the main experiments, GPT-4o is used as a simulated human -- which is likely comparable to a human expert -- in which case the method sees additional improvements compared to the version without it. It would be useful to better understand what level of human expertise (e.g., using noisy preference) is required to achieve these additional gains (or, potentially loss in performance with poor feedback).\n\n3. Evaluation of scalability. It is unclear how much computational cost is involved in fitting BNN surrogate, calculating UCB values for every candidate, etc. Additional results on scalability with datasets of varying sample sizes and numbers of initial features would provide greater insight.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "84Jorkeo7l", "reviewer": "Reviewer_abCY", "summary": "This paper proposes LLM-based feature engineering methods for tabular learning. Specifically, while letting LLM to explore operation candidates, the proposed method evaluates such generated operation rule by human experts, if available. While providing strong empirical evaluations, the authors also provide some theoretical aspects of the proposed method.", "questions": "See Weaknesses.", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The authors provided strong evaluation results, and also provided some theoretical explanations of their approach.\n\n2. Writing is easy to follow.\n\n3. The evaluation results are based on multiple datasets.", "weaknesses": "1. What is the novel point of the proposed method, compared to the existing methods? I acknowledge that involving human experts might have some pros on doing feature engineering, and also agree that it will definitely improve the performance. However, still, involving human experts may cause some scalability issues. In other words, the authors provided the experimental results where the LLM-generated operations are evaluated by LLM itself. In this case, what advantages does the author's method have, compared to only evaluating target AI models (like MLP or XGBoost)?\n\n2. How were the human experts selected? Did the authors act as a human expert? If not, how much did it cost to hire such human experts?\n\n3. It would be better to show the cost analysis compared to baseline methods.\n\n4. The datasets used in the experiments actually have just a few columns. If the table has millions of columns, is the proposed method scalable? Even in this case, is it possible to involve human experts?\n\n5. How can such an approach be expanded when the column names do not exist? For example, for some financial datasets, the column names and values can be anonymized because of some privacy issues.\n\n6. There are some missing citations. For example, P2T [1] is one of the notable work which leverages LLMs for tabular learning.\n\n[1] Nam et al., Tabular Transfer Learning via Prompting LLMs, COLM 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)