DecisionFlow: Advancing Large Language Model as Principled Decision Maker

Cheng Qian; Heng Ji; Hongru Wang; Peixuan Han; Shanyong Wang; Xiusi Chen

REVIEW 3 major objections 5 minor 13 references

DecisionFlow: Advancing Large Language Model as Principled Decision Maker

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read DecisionFlow lifts LLM decision accuracy by up to 30% by forcing structured, utility-based reasoning before any choice is made.

desk verdict A usable structured-prompting pipeline with real gains on DeLLMa, but the headline MTA numbers rest on an author-expanded benchmark and eval-set-tuned thresholds. read the letter →

arxiv 2505.21397 v2 pith:FFFOPXZG submitted 2025-05-27 cs.CL

Xiusi Chen , Shanyong Wang , Cheng Qian , Hongru Wang , Peixuan Han , Heng Ji This is my paper

classification cs.CL

keywords decisionmodelinglargelanguagemodelsutilitymaximizationstructuredpromptingmedicaltriageexplainablereasoningsymbolicchain-of-thought

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

DecisionFlow is a prompting framework that tries to make large language models deliberate the way a decision analyst would: instead of answering directly, the model first turns the scenario into a structured set of actions, attributes, and constraints, then scores each action with an explicit utility function and picks the highest-scoring option that satisfies the constraints. The paper's central claim is that this structured, utility-driven pipeline produces more accurate and more explainable decisions than standard prompting, with reported gains of up to 30% over Chain-of-Thought and Self-Consistency baselines on high-stakes benchmarks. That claim matters because high-stakes domains like medical triage, agriculture, and stock selection require decisions that can be audited, not just guessed. The paper also reports that the pipeline narrows the gap between high- and low-alignment performance, which it interprets as mitigating inherent model bias. The evidence is restricted to two benchmarks, one of them a medical triage set the authors expanded from 62 to 200 instances; the headline numbers should be read in that context.

What carries the argument

The load-bearing object is an $n \times m$ action-by-attribute relevance matrix $R$, where rows are candidate actions and columns are attributes extracted from the scenario. DecisionFlow uses an LLM to fill $R$ with verbalized relevance scores, then assigns importance weights $w$ and masks trivial entries by thresholding with $\epsilon$, producing a filtered matrix $R' = w' \circ R$. That matrix is summed into a linear utility $O(A)=\sum_i a_i \sum_j r'_{i,j}$, subject to constraints such as $\sum_i a_i=1$, and the argmax gives the final decision. This machinery converts unstructured text into a symbolic, auditable scoring table that is then used to generate the rationale alongside the chosen action.

What would settle it

Run DecisionFlow and the strongest baseline on the original 62 medical triage instances with original labels only, without any author-expanded synthetic cases and without target-bias definitions in the prompt; if the average-accuracy advantage shrinks to near zero, the 30% claim does not generalize beyond the expanded benchmark.

Watch

Extended reading notes

Core claim

The paper claims that LLMs decide better when they are made to build an explicit decision table first: extract candidate actions and their attributes, assign relevance weights, filter out trivial factors, define a linear utility objective over the remaining weighted attributes, and then pick the action that maximizes that objective under constraints. Concretely, the objective is $O(A)=\sum_{i=1}^n a_i \sum_{j=1}^m r'_{i,j}$ with $\sum_i a_i=1$, and the decision is $a^*=\arg\max_{a_i} O(a_i)$. On the Medical Triage Alignment and DeLLMa benchmarks, this pipeline is reported to outperform Chain-of-Thought, Self-Consistency, and Program-of-Thoughts baselines, reaching up to 90.50% high-alignment accuracy on the expanded medical triage set, 76.67% on agriculture, and 72.50% on stocks, with a 30% gain in the low-alignment setting.

Load-bearing premise

The reported gains on medical triage rest on the assumption that the 200-instance expanded benchmark, synthesized with GPT-4o and selected by the authors, has labels that correctly encode the intended biases; if those labels are biased or the target-bias definitions leak into the prompts, the MTA accuracy numbers could overstate real-world alignment.

Editorial extensions

If this is right

If the choice rule $a^*=\arg\max O(a_i)$ is as effective as reported, structured utility scoring could replace free-form reasoning in decision-support systems where auditability matters.
The reported stability across action-pool sizes from 2 to 7 candidates suggests the method degrades more gracefully than direct prompting when options multiply.
Reducing the gap between high- and low-alignment settings implies the pipeline can steer model behavior toward a stated policy rather than leaving the model's default bias in control.
The modular four-step design means each stage can be individually inspected, corrected, or re-executed with a stronger model, which the paper's component-swap experiments support.

Reading between the lines

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

A testable extension the paper does not run: isolate the LLM's scoring step from the final argmax by feeding the extracted matrix $R'$ into a deterministic optimizer; if accuracy holds, the value lies in structured extraction rather than in final LLM judgment.
The bias-reduction result suggests DecisionFlow could be repurposed as an audit tool that reveals a model's default decision tendencies before any alignment prompt is added.
The benchmark expansion itself implies a natural stress test: the method should be re-evaluated on independently labeled triage cases generated without the authors' target-bias definitions.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

DecisionFlow is a modular prompt pipeline that gives an LLM an explicit decision space: it extracts actions, attributes and a relevance matrix, filters with learned weights, builds a linear utility objective, and returns an argmax plus rationale. The components are familiar from DeLLMa, PoT and constraint prompts, but the four-step formulation with the relevance-matrix representation is new enough, and the authors ship code and data. On DeLLMa, where the benchmark labels are external, the method beats strong baselines in most cells—GPT-4o agriculture 76.67 vs CoT-with-tools 69.17, stocks 64.72 vs 50.83. That is a real result.

The soft spot is MTA. The authors expanded the original 62 medical triage instances to 200 by prompting GPT-4o for new cases and manually selecting the best; they also wrote the target-bias definitions used to label the set, and then put those same definitions into the DecisionFlow prompts (Appendix A.1, Figure 11). So the MTA high/low numbers partly measure instruction-following against the authors' own operationalization. The stress-test note lands. This does not make the MTA gains worthless—low-accuracy numbers improve a lot (GPT-4o 68.00 vs best baseline 54.10)—but it means 'state of the art' should be qualified and the expanded labels need independent verification.

Two smaller issues. First, the 'consistently outperforms' claim in the abstract is contradicted by Table 2: Llama-3.1-8B DeLLMa average is 72.36 vs 72.92 for CoT-with-tools, and Qwen2.5-7B agriculture is 55.00 vs 58.33 for zero-shot. Fine to report wins, but not 'consistent.' Second, Appendix D shows the epsilons were selected by sweeping on the evaluation sets (MTA best at 0.3, DeLLMa best at top-3), with no held-out selection and no error bars on most baselines. That inflates the headline numbers.

Overall, the central mechanism—structured extraction plus utility scoring helps LLM decisions—is credible on the external DeLLMa benchmark, and the disentangling analysis is a useful addition. The paper deserves a serious referee. I would send it to review and ask for independent MTA labels, held-out hyperparameter selection, and a softer consistency claim.

Referee Report

3 major / 5 minor

Summary. The paper introduces DecisionFlow, a four-stage prompting framework for LLM decision-making. In Step 1 the LLM extracts candidate actions, attributes, and an action-attribute relevance matrix; Step 2 scores and sparsifies these entries under constraints; Step 3 defines a linear utility objective; and Step 4 selects the argmax action and generates a rationale. The authors evaluate on the Medical Triage Alignment (MTA) benchmark, which they expand from 62 to 200 instances, and on the DeLLMa agriculture and stock benchmarks, using five LLMs. They report accuracy gains over zero-shot, CoT, PoT, self-consistency, and two DeLLMa variants, and argue that DecisionFlow balances high/low alignment and reduces model bias.

Significance. The proposed modularity is appealing: explicit extraction, filtering, and utility maximization are easy to audit, and the authors provide ablations, human evaluation of pipeline steps, a runtime comparison, and a public code/data link. The central empirical claim, however, is weakened by two issues: the MTA benchmark is expanded and labeled by the authors using definitions that also appear in the method's prompts, and Table 2 contains several cells where DecisionFlow is not better than a strong baseline. If the MTA results are accepted, the approach is a useful step toward transparent LLM decision support, but the current evidence does not fully support 'consistently outperforms' across settings.

major comments (3)

[Section 5.1, Appendix A.2, Figure 11] The expanded MTA benchmark is the backbone of the headline 'up to 30%' gains, yet its labels are not independent of the method. Appendix A.2 states that GPT-4o generated candidate instances and the authors manually selected 200, using the target-bias definitions formalized in Appendix A.1. Those same definitions are pasted into DecisionFlow's filter and scoring prompts, e.g., the target-bias block in Figure 11. Evaluating on labels constructed from the authors' normative definitions while giving those definitions to the model is circular; the high/low accuracy numbers may measure how faithfully the model follows the authors' textual operationalization rather than how well it triages. The authors should report results on the original external 62-instance MTA, provide inter-annotator agreement and a detailed label-adjudication protocol for the expanded set, and ideally use independently sourced definitions or blind labeling.
[Table 2] The claim of consistent superiority is not supported by the DeLLMa rows. For Llama-3.1-8B, DecisionFlow's DeLLMa average (72.36) is below CoT with tools (72.92); for Qwen2.5-7B, agriculture accuracy (55.00) is below zero-shot (58.33) and below CoT with tools (60.83); and for Llama-3.1-8B on stocks, DecisionFlow (72.50) is below CoT with tools (74.17). These are the same domains central to the paper's statement that DecisionFlow 'consistently outperforms strong baselines.' The authors should report pairwise significance tests and discuss these failure cases rather than relying solely on aggregate averages.
[Appendix D, Section 5.4] The method's performance is highly sensitive to the two free parameters epsilon_MTA and epsilon_DeLLMa, which appear to be tuned on the test benchmarks. Table 8 shows MTA average accuracy varying from 53.50 (epsilon=0.0) to 79.25 (epsilon=0.3) and then falling, and Table 9 shows DeLLMa average varying from 52.08 (top 1) to 70.70 (top 3). The chosen values are not given a principled selection rule, and no equivalent hyperparameter search is reported for baselines. This asymmetry can explain a substantial part of the reported gains, so the authors should specify a validation-based selection protocol and report results for all epsilon values.

minor comments (5)

[Algorithm 1] The algorithm title contains a typo ('psuedo code'), and line 4 reads 'w = 0if |w| <= epsilon' without a space; please fix the formatting.
[Table 2] The arrows (up-arrow and down-arrow) in the DecisionFlow rows are not defined in the table caption or legend; please specify whether they indicate the difference relative to the best baseline and whether the plus-minus values are standard deviations across runs.
[Table 5] The DeLLMa Prompt-token entries for DecisionFlow appear misaligned: Agriculture is shown as 942.75 and Stocks as 7842.60, which seems inconsistent with the surrounding numbers; please check the table formatting and transposition.
[Section 6.1] The text reports '0.08 points (68.50 vs 68.42)', but 68.50 does not appear in Table 3; please reconcile the numbers or correct the citation.
[Appendix A.2] The expansion procedure says GPT-4o creates 10 samples per original sample and the authors select 200 of 'the highest quality'; please clarify whether the final 200 include any original 62 instances and how the manual selection was distributed across the 12 target-bias categories.

Circularity Check

1 steps flagged · score 4.0 of 10

MTA benchmark loop is the only circularity-adjacent step; DeLLMa results are independent, so circularity is partial.

self definitional [Section 5.1 (MTA dataset description); Appendix A.2; Figure 11 Scoring and Decision Prompt]
"we revise the original examples and expand them into a 200-instance human-verified examples, supplemented with formalized definitions of target biases (Appendix A.1) ... for each and every original data sample, we prompt GPT-4o to create 10 samples that are different from the original data sample and make sure at least 1 choice is for a high value and 1 choice is for a low value. We then inspect these synthesized data samples manually and select 200 data samples of the highest quality. ... Target Bias: Prioritize individuals with high moral deservingness (e.g., those helping others)."

The expanded MTA gold labels are generated from Appendix A.1's formalized target-bias definitions (via GPT-4o and the authors' manual selection), and the same definitions are inserted into DecisionFlow's prompts (e.g., Figure 11's 'Target Bias' block and Figure 7's '<Detailed explanation for each target bias>'). So DecisionFlow is asked to reproduce the very normative standard used to construct the ground truth: gold = f(D), prediction = g(D), accuracy = match(f(D), g(D)). The MTA gains therefore partly measure instruction-following against the authors' own operationalization, not independently validated triage decisions. This is a benchmark-construction loop; it is partial because DeLLMa uses external, objective labels.

full rationale

The DecisionFlow pipeline itself is not circular: the utility objective O(A) = sum_i a_i sum_j r'_ij is the method's own operationalization, and no external law is claimed to be derived from it. The DeLLMa benchmarks are external and profit-based, so the agriculture and stock results provide independent evidence. The only load-bearing circularity-adjacent step is the MTA expansion, where the authors authored the target-bias definitions, used them to generate and select the 200 instances, and then supplied the same definitions in the prompts. This makes the MTA accuracy numbers partly an agreement-with-author-rubric score. However, the method still improves over baselines on the same rubric, and DeLLMa results stand apart; no equation-level equivalence or self-citation chain forces the central claim. Separately, epsilon is tuned on the test set (Appendix D), which is a correctness and overfitting risk, not a circularity. Overall, the circularity is partial and confined mainly to the MTA evaluation.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The central method rests on two tuned thresholds and several domain assumptions about utility-maximizing decisions and the faithfulness of LLM extraction. The latent utility function is a modeling construct without independent evidence.

free parameters (2)

epsilon_MTA = 0.3
Confidence cutoff for discarding low-confidence medical predictions; selected via hyperparameter scan in Appendix D (Table 8) to maximize test accuracy.
epsilon_DeLLMa = top 3
Number of top impact factors or probable trends retained per action; selected via hyperparameter scan in Appendix D (Table 9) to maximize test accuracy.

assumptions (4)

domain assumption There exists a utility function O: A -> R that rationalizes decisions through argmax over actions.
Posited in Section 3, Eq. (2); no proof that real decision problems are utility-maximizing.
domain assumption The most probable action under the context equals the expected-utility maximizer.
Assumed in Eq. (3) to connect LLM output probabilities to utility; used to justify the argmax decision rule.
ad hoc to paper LLM-extracted attributes P and relevance matrix R faithfully capture decision-relevant information.
The framework depends on the decision LLM producing accurate structured representations, with only a small human evaluation over 80 samples as external validation.
ad hoc to paper The 200-instance MTA expansion is a valid, unbiased benchmark with correct labels.
The dataset is generated by GPT-4o and manually selected by the authors (Appendix A.2), with no comparison to original MTA labels and no independent annotation study.

invented entities (1)

Latent utility function O(A)
purpose: Scores candidate actions under the scenario and constraints, driving the final selection in Eq. (8).
Introduced as an unobserved construct; operationalized solely by LLM-generated scores and weights, with no external calibration or falsifiable handle outside the paper.

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Cite this review

Pith. "Pith review of DecisionFlow: Advancing Large Language Model as Principled Decision Maker." pith.science (2026). https://pith.science/paper/FFFOPXZG

@misc{pith2026250521397,
  author       = {Pith},
  title        = {Pith review of: DecisionFlow: Advancing Large Language Model as Principled Decision Maker},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FFFOPXZG}},
  note         = {Machine review of arXiv:2505.21397}
}

read the original abstract

In high-stakes domains such as healthcare and finance, effective decision-making demands not just accurate outcomes but transparent and explainable reasoning. However, current language models often lack the structured deliberation needed for such tasks, instead generating decisions and justifications in a disconnected, post-hoc manner. To address this, we propose DecisionFlow, a novel decision modeling framework that guides models to reason over structured representations of actions, attributes, and constraints. Rather than predicting answers directly from prompts, DecisionFlow builds a semantically grounded decision space and infers a latent utility function to evaluate trade-offs in a transparent, utility-driven manner. This process produces decisions tightly coupled with interpretable rationales reflecting the model's reasoning. Empirical results on two high-stakes benchmarks show that DecisionFlow not only achieves up to 30% accuracy gains over strong prompting baselines but also enhances alignment in outcomes. Our work is a critical step toward integrating symbolic reasoning with LLMs, enabling more accountable, explainable, and reliable LLM decision support systems. Code and data are at https://github.com/xiusic/DecisionFlow.

Figures

Figures reproduced from arXiv: 2505.21397 by the authors.

**Figure 1.** Overall Framework of DecisionFlow. guides the model to: (1) Enumerate possible actions A. (2) Identify context-sensitive attributes P, and (3) Fill in concrete or probabilistic values for each ri,j . To ensure consistency across candidates, attribute names are canonicalized using taskspecific schemas or lightweight ontology matching. Output. A semantically grounded, interpretable table (P, R) that serves as input… view at source ↗

**Figure 2.** Ablation study on Filtering & Scoring with [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Summarization of common errors of imperfect cases presented throughout DecisionFlow. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (13 more)

**Figure 4.** Figure 4: Preference of Joint Modeling compared to [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Human evaluation on different steps. 20 low-DMA cases), Agriculture (20 samples), and Finance (20 samples). We then ask three welleducated annotators to indicate whether each step accurately captures the information or makes the correct decision [PITH_FULL_IMAGE:figu…

**Figure 19.** Figure 19: The zero-shot prompt consists of three main parts (see [PITH_FULL_IMAGE:figures/full_fig_p015_19.png]

**Figure 6.** Figure 6: Example Zero-Shot Prompt for Medical Triage Alignment EXAMPLE CHAIN-OF-THOUGHT PROMPT FOR MEDICAL TRIAGE ALIGNMENT You are an algorithm programmed to assist with medical triage in a military medical setting. <Detailed explanation for each target bias>. Articulate your …

**Figure 7.** Figure 7: Example Chain-of-Thought Prompt for Medical Triage Alignment Example Information Extraction Prompt for Medical Triage Alignment You are an expert in information extraction and summarization. Your task is to analyze the given text and extract all key pieces of informati…

**Figure 8.** Figure 8: Example Information Extraction Prompt for Medical Triage Alignment [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]

**Figure 9.** Figure 9: Example Attribute Summary Prompt for Medical Triage Alignment [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

**Figure 10.** Figure 10: Example Filter Prompt for Medical Triage Alignment [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]

**Figure 11.** Figure 11: Example Scoring and Decision Prompt for Medical Triage Alignment [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]

**Figure 12.** Figure 12: Example Zero-Shot Prompt for Agriculture [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]

**Figure 13.** Figure 13: Example Chain-of-Thought Prompt for Agriculture [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]

**Figure 14.** Figure 14: Example Zero-Shot Prompt for Stocks EXAMPLE CHAIN-OF-THOUGHT PROMPT FOR STOCKS Below are the stocks I am considering: AMD, GME. I would like to know which stock I should buy based on the information of their historical prices in the last 24 months. I can only buy one …

**Figure 15.** Figure 15: Example Chain-of-Thought Prompt for Stocks [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]

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Reference graph

Works this paper leans on

13 extracted references · 12 canonical work pages

[1]

Proceed with surgery for Patient A

work page
[2]

Stop and switch to surgery for Patient B Target Bias In a military medical triage setting, when presented with multiple-choice questions about medical scenarios, the decision- making process is guided by the ethical principle of prioritizing the continuity of care for current patients. This involves allocating resources—such as time, medical supplies, and...

work page
[3]

Cause of injury

Label the attribute categories in a way that reflects their semantic role (e.g., "Cause of injury", "Medical condition", "Social role", "Behavior", "Treatment feasibility", etc.). Your Turn: Input: variable: Patient 1: The first is a rescue worker Patient 2: The second is a looter extracted information: - The rescue worker is injured while pulling people ...

work page
[4]

Treat the young woman

work page
[5]

Reasoning

Treat the bomber Target Bias High utilitarianism(1.Low & 2.High) Chain of Thought (CoT) Response In this scenario, the paramedic must apply high utilitarian principles to make the most beneficial decision for the greatest number of people. The young woman, despite her severe injuries, has a high chance of bleeding out, which could lead to her death even w...

work page
[6]

Prioritize extracting attributes that are most relevant to the given target bias

work page
[8]

Consider whether the attribute directly influences or is influenced by the principles outlined in the target bias

Evaluation Criteria: Relevance: Assess how closely the attribute align with the target bias. Consider whether the attribute directly influences or is influenced by the principles outlined in the target bias. Weight Assignment: Assign a weight between 0 and 1, where: - 0 indicates no relationship. - 0.5 indicates a moderate relationship. - 1 indicates a st...

work page
[9]

Action" Target Bias:

Output You should return a JSON file containing the following elements: - Relevance Explanation: Provide a brief explanation of why the attribute are relevant (or not) to the target bias. - Weight: Assign a weight based on the strength of the relationship. Your Turn: Input: Variable and attribute: "Action" Target Bias: "When selecting an answer from multi...

work page

Show all 13 references

[10]

Objective Function: Weighted combination of attribute scores
[11]

Constraints: Logical and temporal constraints
[12]

Target Bias: Ethical principles guiding prioritization
[13]

term": "0.7*C1*x1 + 0.9*A1*x1 + 0.7*C2*x2 + 0.95*CoI2*x2

Choices: Indexed decision options. Step 1: Pairwise Attribute Scoring Compare attribute values and assign relative scores (0–1) based on alignment with the target bias. Ignore any numbers preceding attributes—they are not scores. Step 2: Apply Objective Function Plug scores in...

2021
[2023]

Preprint, arXiv:2303.11366

Reflexion: Language agents with verbal rein- forcement learning. Preprint, arXiv:2303.11366. Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, and Kam-Fai Wong. 2023. Large language models as source planner for personal...

2023 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Proceed with surgery for Patient A

work page

[2] [2]

Stop and switch to surgery for Patient B Target Bias In a military medical triage setting, when presented with multiple-choice questions about medical scenarios, the decision- making process is guided by the ethical principle of prioritizing the continuity of care for current patients. This involves allocating resources—such as time, medical supplies, and...

work page

[3] [3]

Cause of injury

Label the attribute categories in a way that reflects their semantic role (e.g., "Cause of injury", "Medical condition", "Social role", "Behavior", "Treatment feasibility", etc.). Your Turn: Input: variable: Patient 1: The first is a rescue worker Patient 2: The second is a looter extracted information: - The rescue worker is injured while pulling people ...

work page

[4] [4]

Treat the young woman

work page

[5] [5]

Reasoning

Treat the bomber Target Bias High utilitarianism(1.Low & 2.High) Chain of Thought (CoT) Response In this scenario, the paramedic must apply high utilitarian principles to make the most beneficial decision for the greatest number of people. The young woman, despite her severe injuries, has a high chance of bleeding out, which could lead to her death even w...

work page

[6] [6]

Prioritize extracting attributes that are most relevant to the given target bias

work page

[7] [8]

Consider whether the attribute directly influences or is influenced by the principles outlined in the target bias

Evaluation Criteria: Relevance: Assess how closely the attribute align with the target bias. Consider whether the attribute directly influences or is influenced by the principles outlined in the target bias. Weight Assignment: Assign a weight between 0 and 1, where: - 0 indicates no relationship. - 0.5 indicates a moderate relationship. - 1 indicates a st...

work page

[8] [9]

Action" Target Bias:

Output You should return a JSON file containing the following elements: - Relevance Explanation: Provide a brief explanation of why the attribute are relevant (or not) to the target bias. - Weight: Assign a weight based on the strength of the relationship. Your Turn: Input: Variable and attribute: "Action" Target Bias: "When selecting an answer from multi...

work page