Marginal Response Surface Elicitation for Zero-Label Tabular Learning

arXiv:2609.39639 · cs.CL · Submitted 2026-09-30 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "Marginal Response Surface Elicitation for Zero-Label Tabular Learning".

Tom: Marginal Response Surface Elicitation (MARS) is a method that transforms feature-level Large Language Model (LLM) priors into a reusable,

Jane: First, who's behind it and why it matters.

Title and authors: Tom: Alright, let's get into the specifics of what this paper is actually called and who came up with it. The full title is "Marginal Response Surface Elicitation for Zero-Label Tabular Learning," and the authors are from Fudan Institute on Networking Systems of AI.

Jane: It’s an interesting name because it suggests they aren't just looking at one single point about a feature; they are mapping out a whole surface of how different values for that feature influence the outcome.

Lu: That "marginal response surface" concept implies they are systematically exploring the relationship between feature values and class outcomes, which is a sophisticated way to structure the LLM interaction.

Meng: It’s good to know who they are—Fudan Institute on Networking Systems of AI—because that tells us this approach is coming from a place focused on applying deep learning principles directly to complex data structures.

Lalam: For me, the implication is that we can finally leverage those massive amounts of unstructured knowledge in LLMs and make them work effectively for structured prediction tasks without needing specialized tabular training pipelines.

The paper's summary: Tom: Moving on to what this paper actually does, the summary explains that instead of relying on labeled data to train a classifier, they use the task description and feature semantics to get class support scores and feature weights from the LLM.

Jane: So, they are essentially asking the LLM for two things for every possible value of a feature: how much support it gives for each class, and how important that specific feature is.

Lu: That dual output—support score z(r) and importance score b(r) —is the core mechanism because it allows them to model both the likelihood of an outcome and the discriminative strength of a variable simultaneously.

Meng: I’m interested in how they handle this massive amount of input information from one LLM query; it must be very efficient to extract all that detail from just a few prompts.

Lalam: The paper summarizes that they then aggregate these multiple responses using the median to build feature response functions, which is a clever way to filter out noise and get a more stable prediction model.

The paper's improvements: Tom: Now for the parts where they show how this method is better than other approaches; they show that MARS achieves an average AUC of one point nine seven and an AP of six point two one higher than direct prompting methods across eight different tabular benchmark tasks.

Jane: That comparison to direct prompting is quite telling, Tom; it suggests that just asking the LLM for a label isn't as effective as this structured elicitation process they developed in "Marginal Response Surface Elicitation for Zero-Label Tabular Learning."

Lu: The paper points out that this method substantially reduces end-to-end costs compared to other high-performing baselines, which is a big win because it makes the whole process much more practical for scaling up.

Meng: Reducing end-to-end costs is what I care about most; if we can get this performance boost while slashing the computational expense, that makes it immediately viable for our operational needs.

Lalam: The improvements suggest that by constructing these feature response functions and using a weighted sum for prediction, they create a model that requires no further LLM queries during inference, which is incredibly efficient.

Conclusion: Tom: So, to wrap up this discussion on "Marginal Response Surface Elicitation for Zero-Label Tabular Learning," the big picture is that we've built a reusable predictor that transforms raw LLM knowledge into a structured additive model based on feature importance and value support.

Jane: It seems like the main implication is that we can build high-performing tabular classifiers purely from task descriptions and feature semantics without ever needing ground-truth labels or extensive local model training.

Lu: The paper successfully models both the direction of class support for specific values and the strength of those features, which provides a decomposable predictive contribution, making it a nonlinear additive model built from a finite number of initial LLM queries.

Meng: From an engineering standpoint, this means we get to deploy something that is fast during inference because it doesn't need any more API calls for new data points; the efficiency gains are quite substantial compared to other methods.

Lalam: I think the real impact here is on how we use AI in general; it shows us a way to embed domain knowledge directly into predictive systems in a way that is structured and interpretable.

Tom: It’s been fascinating dissecting this paper, and while there are clear wins, the paper does mention a limitation: one thing they noted is that their method relies on selecting anchors for numerical features based on specific empirical quantiles, which might mean it doesn't capture every single nuanced interaction in the data.

Jane: That’s a fair caveat; relying on those fixed anchor points means it stops working where the true underlying relationship might be more complex than what those selected values reveal.

Lu: Exactly, and their experimental evaluation showed that increasing the number of responses from one to five did improve AUC and AP, but they noted that there were diminishing gains for larger numbers, which suggests there's a practical limit to how much LLM feedback we can reliably extract.

Meng: So, while it’s powerful as it is, we have to be careful not to over-rely on the fixed set of anchors when deploying this in mission-critical systems where every possible data nuance matters.

Lalam: That makes sense; it’s a tool for high performance on structured tasks based on the information given, but we still need to keep an eye out for those edge cases where the anchor selection falls short.

Liangyu Teng, Yicheng Ding, Jing Liu, Hengsong Liu, Juncen Guo, Hongru Li, Jingyu Zhang

Fudan Institute on Networking Systems of AI

cs.CL

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/google-research/tabfm

Importance score: 92/100

The gist: Marginal Response Surface Elicitation (MARS) is a method that transforms feature-level Large Language Model (LLM) priors into a reusable, zero-shot tabular classifier by selecting representative

Key concepts

Representative Value Selection: This stage selects key points (anchors) from unlabeled data to represent each feature. For numbers, it uses empirical quantiles at specific intervals; for categories, it stores the most frequent values along with their counts. This ensures the model learns from a diverse set of feature instances.
Marginal Response Elicitation: The LLM is prompted multiple times (R=5) to provide two scores for each feature value: a support score indicating class likelihood and an importance score measuring how discriminative that feature is. This step extracts nuanced, value-specific insights from the LLM's prior knowledge.
Response Aggregation and Prediction: The support and importance scores are combined to build feature response functions. Numerical features use piecewise linear functions based on the selected anchors, while categorical features use a lookup table. The final prediction is a weighted sum of these functions, using weights derived from the median importance scores.

Terminology

AUC — 24× in this paper · explained in 2 episodes
Benchmark — 16× in this paper · explained in 2 episodes
Aggregation — 11× in this paper · explained in 1 episode
Large Language Model — 9× in this paper
Ablation — 5× in this paper · explained in 1 episode
Foundation Model — 3× in this paper · explained in 3 episodes
Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Ablation study — 1× in this paper · explained in 1 episode
Contrastive Learning — 1× in this paper · explained in 5 episodes
Core — 1× in this paper · explained in 2 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
Pseudo-labels — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes
Semi-supervised Learning — 1× in this paper · explained in 4 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Transferability — 1× in this paper · explained in 4 episodes

Summary

Marginal Response Surface Elicitation (MARS) is a method that transforms feature-level Large Language Model (LLM) priors into a reusable, zero-shot tabular classifier by selecting representative feature values and aggregating LLM responses into weighted feature response functions. This approach enables predictions on unlabeled data without requiring ground-truth labels or local model training, achieving superior performance across eight benchmark tasks while maintaining high efficiency.

The gist

MARS selects representative values for each feature from unlabeled data and prompts the LLM to provide corresponding class support scores and feature weights, which are then aggregated using the median to construct feature response functions. These functions are combined via their weighted sum for prediction, requiring no further LLM queries during inference.

How it works

The method operates in three main stages: Representative Value Selection, Marginal Response Elicitation, and Response Aggregation and Prediction. In the first stage, MARS selects anchors for each feature from unlabeled data. For numerical features, this involves computing empirical quantiles at specific points (e.g., 0.05, 0.20, 0.35, 0.50, 0.65, 0.80, and 128) using non-missing values to obtain at most seven anchors; for categorical features, it stores up to twenty of the most frequent values along with their frequencies.

How it works (continued)

In the second stage, MARS queries the LLM multiple times (R=5) using a single prompt that combines task description and feature semantics. The LLM is asked to provide two key outputs for each feature value: a support score, denoted as z(r), which represents support for the positive class (positive, negative, or zero values), and an importance score, b(r), which measures the feature's discriminative strength relative to other features. The prompt requires that all features use a uniform log-odds scale where +1 corresponds to multiplying odds by e.

How it works (continued)

The third stage involves Response Aggregation and Prediction. MARS constructs feature response functions, f j(v), using the aggregated support scores and feature importance scores. For numerical features, these functions are defined as a piecewise linear function based on the anchor points:

f j(v) = z¯jl + (v - ajl) / (ajl+1 − ajl) × (z¯j,l+1 − z¯jl), for values between anchors. For categorical features, f j(v) is a lookup table defined by the anchor support scores. Finally, a new instance x is scored as s(x) = Σ j: Vj≠∅ wj f j(xj), where w j is the feature weight derived from the median of importance scores (w j = 1/10 max(0, median b(r) j)/10).

Experimental Evaluation

MARS was evaluated across eight tabular benchmark tasks, including Bank, Credit-G, Diabetes, Heart, Cultivars, Myocardial NHANES (financial services and healthcare), and agricultural data. The results show that MARS achieves the highest average AUC and AP among all baselines, outperforming direct prompting by 1.97 and 6.21 percentage points on eight tasks respectively. Furthermore, MARS demonstrated superior performance over other zero-label prediction methods like ProtoLLM, achieving gains of 1.17 and 3.64 percentage points on the same benchmarks when using different LLM backbones (Qwen3.5-4B and Qwen3.5-9B).

Efficiency and Sensitivity

MARS is characterized as powerful, efficient, and transferable. It is efficient because it requires no LLM API calls during inference; the end-to-end API call costs amount to only 9.6% of ProtoLLM, and new samples can be processed via local computation without further querying the LLM. Sensitivity analysis showed that increasing the number of responses (R) from 1 to 5 increased mean AUC and AP by 0.53 and 0.89 percentage points, with diminishing gains for larger R. Ablation studies confirmed that removing support strength causes the most pronounced drops in performance, while aggregating responses using the median is crucial for robustness.

Conclusion

The paper concludes that MARS successfully transforms LLM domain knowledge into decomposable predictive contributions by modeling both the direction and strength of value-specific support and combining responses with feature weights. The resulting model is a nonlinear additive model constructed using a finite number of initial LLM queries, making it a reusable predictor that requires no ground-truth labels, local model training, or test-time LLM queries. Future work will focus on exploring how to reliably incorporate cross-feature interactions.

**Table 1:

Improvements for AI systems

Here are specific improvements to AI systems based on the Marginal Response Surface Elicitation (MARS) method described in the paper, and what these improved systems can achieve:

The core improvement lies in developing a robust, zero-shot mechanism to leverage Large Language Model (LLM) domain priors for tabular prediction without requiring labeled training data or expensive test-time LLM queries.

Here are the specific improvements:

Enhance Zero-Shot Tabular Classification Accuracy and Robustness:
Reduce End-to-End Prediction Costs by Minimizing LLM Inference Overhead:
Improve Model Transferability Across Varying LLM Architectures (Scaling):
Develop a Reusable Predictor Architecture for Rapid Deployment:

Here is what the improved AI system can do, based on the MARS methodology:

The improved system can perform high-accuracy binary classification (scoring) on unseen tabular data across eight distinct benchmark domains (financial services, healthcare, agriculture, etc.) using only a task description and feature semantics.
It achieves state-of-the-art predictive performance by combining feature-level LLM priors into a highly optimized nonlinear additive model. This results in superior Average AUC and AP compared to direct LLM prompting and other zero-label methods (e.g., ProtoLLM).
The system can operate entirely in a zero-shot, deployment-ready manner: once the initial set of feature response functions and weights is constructed via a fixed number of LLM queries (R=5), it can score millions of new instances locally using piecewise linear interpolation or categorical lookup tables, requiring absolutely no further LLM API calls for inference.
It can be deployed efficiently across different LLM sizes (e.g., Qwen3.5-4B vs Qwen3.5-9B), maintaining its predictive advantage, suggesting the method is robust to the size of the underlying generative model used for elicitation.
The system can provide actionable insights into feature importance; it doesn't just predict a label but also assigns an explicit discriminative strength score (0–10) to every feature, indicating which features are most crucial for distinguishing between classes, allowing for better model interpretability.