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REVIEW 5 major objections 6 minor 28 references

MODP: Multi Objective Directional Prompting

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Prompt engineering as a weighted multi-objective search yields a reported 26% gain.

desk verdict Useful industrial framing, but the central scoring mechanism is never computed and the 26% gain doesn't match the table. read the letter →

arxiv 2504.18722 v1 pith:ZZZQ755V submitted 2025-04-25 cs.CC cs.AI

Aashutosh Nema , Samaksh Gulati , Evangelos Giakoumakis , Bipana Thapaliya This is my paper · ORCID
classification cs.CCcs.AI
keywords promptengineeringmulti-objectiveoptimizationLLM-specificobjectivesdirectionalpromptingweightedscoringrepresentativesamplingtoxicityhandlingproductionLLMdeployment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that prompt engineering fails when it optimizes only task accuracy, because the chosen LLM contributes its own behavior—hallucination, toxicity, lost instructions, format drift—that a good prompt must manage. MODP turns this into a concrete optimization: each prompt gets a weighted score combining task-specific objectives (category accuracy) with LLM-specific objectives (e.g., refusing toxic queries), and the prompt with the highest score is selected. The authors show that iterative refinement under this score lifts accuracy 26 percentage points from the initial prompt on a summarization-style reading-comprehension task with synthetic toxic cases, and that accuracy trends on a 20% sample persist on the remaining data. They also report applying the same process to an enterprise support tool used by more than 10,000 agents, where the optimized prompt improved faithfulness, relevance, and precision by about 8% on average.

What carries the argument

The load-bearing object is Algorithm 1's weighted objective score, F(P_i) = w1·O1 + w2·O2 + w3·L1 + w4·L2, with weights in [-1, 1] set by the practitioner. This single scalar turns 'is this prompt better?' into a well-defined argmax over prompts and models; the framework's four-step loop—find a representative sample, define objectives, assign weights, iterate through prompts—is the process that feeds the score.

What would settle it

Perturb the four weights in Algorithm 1 by ±0.2 on the same 20% sample and re-select the prompt: if the argmax prompt changes, the claimed 'optimal' prompt is an artifact of the chosen weights rather than a robust outcome.

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Extended reading notes

Core claim

The central claim is that prompt engineering should be formulated as a multi-objective optimization problem in which task-specific objectives—such as overall and category-level accuracy—are scored alongside objectives derived from the LLM's intrinsic behavior, such as hallucination avoidance, toxicity refusal, and output-format adherence. Algorithm 1 defines F(P) = w1·O1 + w2·O2 + w3·L1 + w4·L2 and selects the prompt that maximizes this score across prompts and models. The iterative process starts from a 20% representative sample, refines prompts directionally, then validates on the remaining 80%. The paper reports a 26% performance gain over initial prompts on the synthetic summarization task, and an average 8% gain across task-specific metrics in the enterprise deployment, with LLM-specific objectives (response adherence, answer consistency) improved in human evaluation.

Load-bearing premise

The optimal prompt is optimal only relative to human-assigned weights and a roughly 20% sample assumed representative; if the weights are arbitrary or the sample is skewed, the selected prompt may be an artifact of those choices, and the paper gives no sensitivity analysis for either.

Editorial extensions

If this is right

  • Prompt quality becomes a well-defined scalar, so prompts can be compared across different LLMs on the same objective set, including during model migrations.
  • The 26% gain on the synthetic summarization task indicates that a simple initial prompt leaves much of the model's capability unused, and iterative multi-objective refinement recovers it.
  • A 20% representative sample suffices for prompt selection because category-wise accuracy trends persisted on the held-out 80%, lowering the cost of prompt iteration.
  • In production, MODP-optimized prompts improved faithfulness, answer relevance, and context precision by about 8% on average, with response adherence and answer consistency rated higher in human evaluation.
  • The framework supports adding ethical objectives like toxicity refusal without sacrificing overall accuracy: the best prompts balanced category accuracy and toxicity handling.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: A natural extension the paper does not test is sensitivity analysis—re-running Algorithm 1 with perturbed weights would show whether the chosen 'optimal' prompt is stable or an artifact of the human-assigned weights.
  • Inference: The representative-sample result suggests a broader principle: if a 20% sample reproduces ranking trends, prompt engineers can afford exhaustive prompt search on small data before full evaluation; this could be tested across tasks and datasets.
  • Inference: The multi-objective score could double as a live monitoring signal in production, giving an early warning when a model update or data drift degrades one objective and triggering prompt re-optimization.
  • Inference: Because the production comparison used human-rated LLM-specific objectives, an automated proxy for response adherence and answer consistency would make the framework fully closed-loop rather than limited to development-time evaluation.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes MODP (Multi-Objective Directional Prompting), a framework for prompt engineering that balances task-specific objectives (e.g., accuracy, category-specific performance) and LLM-specific objectives (e.g., toxicity handling, format adherence) through a weighted scoring function, representative data sampling, and iterative prompt refinement. Algorithm 1 formalizes the optimization as selecting the prompt that maximizes F(P) = w1*O1 + w2*O2 + w3*L1 + w4*L2 over a set of prompts and models. The authors apply the framework to a ReCoRD-based summarization-like reading comprehension task with synthetically added toxic content, report per-category and overall accuracies for twelve manually developed prompts, claim a 26% improvement over the initial prompt, and describe a deployment in Dell's Next Best Action support tool serving over 10,000 agents.

Significance. If fully instantiated, a metrics-driven, multi-objective prompt optimization framework would be a useful contribution to both applied prompt engineering and production LLM deployment. The paper contains several commendable elements: the full prompts are listed in an appendix, the evaluation uses a public dataset (ReCoRD) plus synthetic toxicity data, and the enterprise case study provides a rare glimpse into real-world prompt development. However, the central methodological mechanism—the weighted multi-objective score F(P)—is never computed, reported, or used in the experiments. The empirical sections test manual prompt iteration and accuracy selection, not the proposed optimization objective. Consequently, the paper's headline claims are not reproducible from the shown data, and the contribution as written is an untested recipe rather than a validated framework.

major comments (5)
  1. [Algorithm 1, Section 3.4, Section 6.1] Algorithm 1 (lines 10–16) defines the weighted score F(P_i) = w1*O1 + w2*O2 + w3*L1 + w4*L2 and selects the optimal prompt as argmax F(P) over prompts and models. No experiment in the paper reports O1, O2, L1, L2, the weights w_i, or a single computed F value. Table 1 reports only overall accuracy and per-category percentages, and Section 6.1 selects Prompt9 and Prompt11 based on overall accuracy (73%) and qualitative commentary, not by any objective score. Thus the central optimization loop is never instantiated, and the empirical results support only ordinary manual prompt iteration, not MODP as defined.
  2. [Section 6.2.2] The paper lists format adherence as an objective in Section 3.1 and in Table 2, but Section 6.2.2 explicitly states 'we did not specifically measure this format adherence in our current experiments.' An objective that is never measured cannot appear in the weighted score F(P) for the ReCoRD experiments. This is a direct admission that the multi-objective evaluation described in Sections 3.3 and 3.4 was not actually carried out, further undermining the claim that the results validate MODP.
  3. [Section 6.1, Table 1] The abstract and conclusion claim a 26% performance gain over initial prompts. From Table 1, the lowest prompt (Prompt2) scores 44% and the highest (Prompt9/10/11) score 73%, a gain of 29 percentage points relative to Prompt2, or 25 percentage points relative to Prompt1 (48%). Whether computed against Prompt1 or Prompt2, the improvement is not 26% under either the absolute-percentage-point or relative convention. Moreover, no error bars, sample sizes, or significance tests are reported for any accuracy value, so the headline improvement is not statistically grounded.
  4. [Section 3.2, Section 6.3] Section 3.2 states that a representative sample is found 'by clustering or otherwise strategically sampling approximately 20% of the data,' but no concrete clustering algorithm, sampling procedure, or formal representativeness test is given. Section 6.3 claims that accuracy trends observed on the 20% sample persisted on the remaining 80%, but Figure 5, which is offered as evidence, is only a caption in the manuscript; no quantitative agreement metric (e.g., correlation, error bounds, or per-category comparison) is reported. The representativeness claim, which is load-bearing for the framework's efficiency, is therefore unsupported.
  5. [Section 4.2, Section 2.1.5] The paper uses Mixtral to create the news categories (Section 4.2) and then evaluates prompts on Mixtral, while Section 2.1.5 argues that LLM outputs are not reliable for self-assessment and that prompt validation must not depend solely on the LLM being optimized. No human validation of the Mixtral-generated category labels is reported, and the same-model-family dependency is not addressed. Because category-wise accuracies serve as the task-specific objective proxies, biased or unstable labels could directly bias the O1/O2 scores, so this circularity concern affects the multi-objective claim.
minor comments (6)
  1. [Abstract] The abstract contains a grammatical issue: 'a framework on two key concepts' should be 'a framework based on two key concepts.'
  2. [Section 5] The text says 'The dataset coin large-scale and machine-generated dataset'; this should read 'The dataset is a large-scale...'.
  3. [Table 2] The 'Length of Answer(Tokens)' row reports 'Medium/Long/Medium,' which are not token counts; either rename the metric or provide actual numbers.
  4. [Section 6.3] Figure 5 is cited as supporting the persistence of trends from the 20% sample to the 80% sample, but the figure content is not present in the manuscript; please include the actual figure and a quantitative comparison.
  5. [Appendix A] Prompt6 in the appendix has an unmatched '[INST]' tag at the end; verify that the prompt text is transcribed correctly.
  6. [References] Reference [24] is listed as an arXiv preprint; if a peer-reviewed version exists, it should be cited instead for completeness.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found: Algorithm 1's F(P) is an unimplemented selection rule, and the empirical claims rest on manual accuracy comparisons rather than fitted parameters or self-citations.

full rationale

The paper does not contain a derivation chain that reduces to its own inputs. Algorithm 1 defines F(P_i) = w1*O1 + w2*O2 + w3*L1 + w4*L2 and declares the optimal prompt to be the argmax of F, but this is a proposed selection rule rather than a fitted model: the experiments never report the weights w_i, the objective values O1, O2, L1, L2, or any computed F score, and Table 1 reports only overall and category-wise accuracy. Section 6.2.2 explicitly concedes that format adherence, described as a possible objective, was 'not specifically measured,' and Section 3.4 states that no formal stopping mechanism was adopted, so the 'optimal' prompt is selected by observed accuracy plateaus rather than by the paper's own objective function. There are no fitted parameters renamed as predictions: the weights are human-assigned and never reported, and no sensitivity analysis is given, but this is an evidence gap rather than a circular step. The related-model dependence noted in Section 4.2 — Mixtral was used to create the news categories while Mixtral was also the evaluated model — is a methodological bias or independence concern, not a self-citation chain and not a definitional reduction of the claimed result. The headline 26% gain is not reproducible from Table 1 (48% to 73% is 25 percentage points, or about 52% relative), and the central MODP mechanism is unfalsifiable from the shown data, but reproducibility and reporting defects are distinct from circularity. Accordingly, the honest finding is no significant circularity, with a score of 0.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claim rests on hand-assigned weights, an unspecified sampling procedure, and assumptions that a synthetic toxicity-augmented reading comprehension set proxies real production support traffic, that Mixtral's tag syntax is a framework-level benefit, and that Mixtral-generated categories are valid. None of these is independently verified, and the algorithm's scoring function is never computed.

free parameters (2)
  • Objective weights w_1 to w_4 = not specified
    Algorithm 1 line 10 says weights are assigned based on relative importance, but no values, normalization, or sensitivity analysis are given, and F(P) is never computed in the experiments.
  • Representative sample ratio = approximately 20%
    Section 3.2 chooses approximately 20% of the data by an unspecified clustering or strategic sampling procedure; the claim of representativeness is validated only by one experiment on one dataset.
assumptions (5)
  • ad hoc to paper A weighted linear sum of objective scores is an adequate objective function for prompt optimization.
    Algorithm 1 defines F(P) as a linear combination without justifying scalarization, normalization, or the semantics of negative weights; no Pareto analysis is provided.
  • domain assumption ReCoRD fill-in-the-blank accuracy, augmented with ToxiGen toxicity instances, is a representative proxy for production support tasks.
    Section 4 builds the test set from ReCoRD and ToxiGen, and Section 7 claims transfer to Dell's NBA tool without sharing the enterprise data or showing task similarity.
  • domain assumption Mixtral's [INST] and <s> tags improve instruction following enough to be a framework-level conclusion.
    Section 6.1.2 credits model-specific tags for accuracy gains, but this is an empirical claim about one model version, not a demonstrated property of the MODP framework.
  • domain assumption Synthetic toxicity from ToxiGen and the 'cannot answer, toxic content' response define correct safety behavior.
    Section 4.3 and Section 5 define success on toxic queries as detecting ToxiGen statements and returning a fixed refusal; real-world toxicity and refusal behavior may differ.
  • ad hoc to paper Category labels generated by Mixtral are valid and stable enough for objective-specific evaluation.
    Section 4.2 uses Mixtral to create the news categories, with no validation against human labels, and the same model family is used for the evaluation.

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Cite this review

Pith. "Pith review of MODP: Multi Objective Directional Prompting." pith.science (2026). https://pith.science/paper/ZZZQ755V

@misc{pith2026250418722,
  author       = {Pith},
  title        = {Pith review of: MODP: Multi Objective Directional Prompting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZZZQ755V}},
  note         = {Machine review of arXiv:2504.18722}
}
read the original abstract

Recent advances in large language models (LLMs) have led to their popularity across multiple use-cases. However, prompt engineering, the process for optimally utilizing such models, remains approximation-driven and subjective. Most of the current research on prompt engineering focuses on task-specific optimization, while neglecting the behavior of the LLM under consideration during prompt development. This paper introduces MODP -- Multi Objective Directional Prompting, a framework based on two key concepts: 1) multi-objectivity: the importance of considering an LLM's intrinsic behavior as an additional objective in prompt development, and 2) directional prompting: a metrics-driven method for prompt engineering to ensure development of robust and high-precision prompts. We demonstrate the effectiveness of our proposed ideas on a summarization task, using a synthetically created dataset, achieving a 26% performance gain over initial prompts. Finally, we apply MODP to develop prompts for Dell's Next Best Action support tool, which is now in production and is used by more than 10,000 internal support agents and serving millions of customers worldwide.

Figures

Figures reproduced from arXiv: 2504.18722 by the authors.

Figure 1
Figure 1. Prompt Objectives 2 Related Work Prompt engineering emerged as a pivotal technique for extending the capabilities of LLMs without modifying core model parameters. Since the introduction of prompt-based learning in language models (Brown et al. 2020), several efforts have been suggested for engineer￾ing a prompt (Sahoo et al. 2024). Advancements in research have been majorly around either prompting approaches or auto… view at source ↗
Figure 2
Figure 2. Methodology 2.1.5 Addressing the guesswork and importance of the human in loop: To address the limitations of manual prompting, several stud￾ies [23], [4], [17], [10], [12] have explored automated techniques for optimizing prompts, aiming to offload the human guesswork through search-based approaches and by using multi-LLM systems. A significant challenge with the currently suggested methods is the inherent nature o… view at source ↗
Figure 3
Figure 3. How was the Dataset Created? 5.2 Category-wise Accuracy Accuracy was also calculated separately for each test category (e.g., toxicity_added, Crime, Health, etc.) to provide deeper insight into how well the LLM performed in different areas. The formula for Category-wise Accuracy is: Category-wise Accuracy = Correct Responses in Category Total Test Cases in Category Where: • Correct Responses in Category: The total n… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: 6.2.1 Category-Specific Accuracy. While prompts like Prompt9 (73%) and Prompt11 (73%) achieved high overall accuracy, their performance still varied significantly across specific categories. For instance, Prompt9 excelled in the ’tech’ (78.3%) and ’sports’ (77.5%) cate…
Figure 5
Figure 5. Figure 5: Prompts vs Accuracy 7 Enterprise Application of MODP MODP provides a framework for developing reliable and robust prompts. While the above sections validate its theoretical aspects, at Dell Services we leverage this framework within the Next Best Action (NBA) support t…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 5 canonical work pages

  1. [1]

    Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin...

    arXiv 2020

  2. [2]

    Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2022. KnowPrompt: Knowledge-aware Prompt- tuning with Synergistic Optimization for Relation Extraction. InProceedings of the ACM Web Conference 2022 (Virtual Event, Lyon, France) (WWW ’22). Association for Computing Machinery, New York, NY, USA, 27...

    doi:10.1145/3485447 2022

  3. [3]

    Gelei Deng, Yi Liu, Yuekang Li, Kailong Wang, Ying Zhang, Zefeng Li, Haoyu Wang, Tianwei Zhang, and Yang Liu. 2024. MASTERKEY: Automated Jailbreaking of Large Language Model Chatbots. In Proceedings 2024 Network and Distributed System Security Symposium (NDSS 2024) . Internet Society. doi:10.14722/ndss.2024. 24188

    doi:10.14722/ndss.2024 2024

  4. [4]

    Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. 2023. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution. arXiv:2309.16797 [cs.CL] https://arxiv.org/abs/2309.16797

    arXiv 2023

  5. [5]

    Iason Gabriel. 2020. Artificial Intelligence, Values, and Alignment. Minds and Machines 30, 3 (Sept. 2020), 411–437. doi:10.1007/s11023-020-09539-2

    doi:10.1007/s11023-020-09539-2 2020

  6. [6]

    Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models. arXiv:2009.11462 [cs.CL] https://arxiv.org/abs/2009.11462

    arXiv 2020

  7. [7]

    Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, and Iryna Gurevych. 2024. A Survey of Confidence Estimation and Calibration in Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 1: Long Papers) , Kevin Duh, Hele...

    work page 2024

  8. [8]

    Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar. 2022. ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection. arXiv:2203.09509 [cs.CL] https://arxiv.org/abs/2203.09509

    arXiv 2022

Show all 28 references
  1. [9]

    Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2024. Large Language Models Cannot Self- Correct Reasoning Yet. arXiv:2310.01798 [cs.CL] https://arxiv.org/abs/2310.01798

    2024 arXiv

  2. [10]

    Zekun Li, Baolin Peng, Pengcheng He, Michel Galley, Jianfeng Gao, and Xifeng Yan. 2023. Guiding Large Language Models via Directional Stimulus Prompting. arXiv:2302.11520 [cs.CL] https://arxiv.org/abs/2302.11520

    2023 arXiv

  3. [11]

    Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024. Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics 12 (2024), 157–173

    2024

  4. [12]

    Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng

  5. [13]

    Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson. 2023. Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! arXiv:2310.03693 [cs.CL] https: //arxiv.org/abs/2310.03693

    2023 arXiv

  6. [14]

    Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. arXiv:2402.07927 [cs.AI] https: //arxiv.org/abs/2402.07927

    2024 arXiv

  7. [15]

    ScienceDirect. 2025. Pareto Front. https://www.sciencedirect.com/topics/ engineering/pareto-front Accessed: 2025-02-10

    2025

  8. [16]

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng

  9. [17]

    Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh

  10. [18]

    Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. 2023. Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of- Thought Prompting. In Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. H...

    2023

  11. [19]

    Shubham Vatsal and Harsh Dubey. 2024. A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks. arXiv preprint arXiv:2407.12994 (2024)

    2024 arXiv

  12. [20]

    Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po- Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, Zac Kenton, Sasha Brown, Will Hawkins, Tom Stepleton, Courtney Biles, Abeba Birhane, Julia Haas, Laura Rimell, Lisa Anne Hendrick...

    2021 arXiv

  13. [21]

    Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli. 2024. Hallucination is Inevitable: An Innate Limitation of Large Language Models. arXiv:2401.11817 [cs.CL] https: //arxiv.org/abs/2401.11817

    2024 arXiv

  14. [22]

    Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Ben- jamin Van Durme. 2018. ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension. arXiv:1810.12885 [cs.CL] https://arxiv.org/abs/1810.12885

    2018 arXiv

  15. [23]

    Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023. Large Language Models Are Human-Level Prompt Engineers. arXiv:2211.01910 [cs.LG] https://arxiv.org/abs/2211.01910

    2023 arXiv

  16. [24]

    cannot answer, toxic content

    Yulin Zhou, Yiren Zhao, Ilia Shumailov, Robert Mullins, and Yarin Gal. 2023. Revisiting Automated Prompting: Are We Actually Doing Better? arXiv preprint arXiv:2304.03609 (2023). KDD ’25, August 03–07, 2025, Toronto, Canada Aashutosh Nema *, Samaksh Gulati*, Evangelos Giakouma...

    2023 arXiv

  17. [2020]

    arXiv preprint arXiv:2010.15980 (2020)

    Autoprompt: Eliciting knowledge from language models with automatically generated prompts. arXiv preprint arXiv:2010.15980 (2020)

    2020 arXiv

  18. [2021]

    arXiv:2105.04054 [cs.CL] https://arxiv.org/abs/2105.04054

    Societal Biases in Language Generation: Progress and Challenges. arXiv:2105.04054 [cs.CL] https://arxiv.org/abs/2105.04054

    arXiv

  19. [2023]

    Gradient Descent

    Automatic Prompt Optimization with "Gradient Descent" and Beam Search. arXiv:2305.03495 [cs.CL] https://arxiv.org/abs/2305.03495

    arXiv

  20. [6595]

    doi:10.18653/v1/2024.naacl-long.366

    2024 doi

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Reviewed August 16, 2026 · model on record in the stance chip above.