Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs

Konrad Vesey; Siun-Chuon Mau; Taejin Kim

REVIEW 4 major objections 6 minor 21 references

Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper proposes a metric space for quantifying the perspective of text, trained by contrastive learning on a handful of author-labeled examples, and a prompt-optimization procedure that steers LLM output toward a user-chosen point in…

desk verdict A plausible idea for measuring and steering LLM perspective, but the evaluation is too thin and partly circular to support the claims; worth sending to review with the expectation of major revision. read the letter →

arxiv 2506.23377 v2 pith:RZZQ7SN4 submitted 2025-06-29 cs.CL cs.AI

Taejin Kim , Siun-Chuon Mau , Konrad Vesey This is my paper

classification cs.CLcs.AI

keywords perspectivemeasurementLLMoutputcontrolcontrastivelearningsiamesenetworkpromptengineeringgreedy-coordinatedescentbiasdetectiontextembedding

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

Perspective-Dial claims that the viewpoint of arbitrary text can be measured as a point in a metric space learned from LLM embeddings, and that this same space can be used to control the viewpoint of LLM-generated text. The paper introduces two linked pieces: a Perspective Space built by contrastive fine-tuning of a BERT-style siamese network on manually labeled clusters (soccer fan allegiances, US-election leanings), and a systematic prompt-engineering loop that uses greedy-coordinate descent to swap prompt phrases until the LLM's output embedding lands near a target point in the 2-D PCA projection of that space. If the approach works, users could quantify bias or stance in texts and adjust LLM responses on demand without retraining the generation model. The authors present initial evaluations on two small datasets, acknowledging the need for broader validation.

What carries the argument

The central mechanism is a contrastively fine-tuned embedding space, the Perspective Space, in which texts are points and distances encode differences in viewpoint. A siamese BERT network is trained with contrastive or cosine-similarity loss on pairs of texts labeled 1 for same cluster, 0 for opposite clusters, and 0.35 for neutral-to-pole pairs, so that similar perspectives are pulled together and opposing ones are pushed apart. Control is then carried by greedy-coordinate descent over prompt tokens: the user specifies a target point in the 2-D PCA projection, and the algorithm queries the LLM with candidate prompt phrases, keeping those that lower the L2 distance between the output embedding and the target.

What would settle it

Have several independent annotators label the same Quora soccer posts and election op-eds into perspective clusters without seeing the authors' labels; if inter-annotator agreement is low, or if a Perspective Space trained on one annotator's labels fails to rank held-out texts in the same order as another annotator's judgments, then the method is measuring an idiosyncratic labeling rather than a stable property of text.

Watch

Extended reading notes

Core claim

The authors claim to be the first to empirically quantify 'perspective' in text and to use that quantity to guide LLM outputs. Their central discovery is that contrastive learning on pairs of texts labeled by perspective cluster (similar vs. dissimilar) reshapes an LLM's embedding space so that texts of the same viewpoint cluster together, while texts of opposite viewpoints separate; and that the resulting space can serve as a loss function for a greedy-coordinate-descent prompt optimizer, which iteratively replaces prompt tokens with alternatives and keeps those that pull the LLM's output toward a user-specified point. They demonstrate the pipeline on two manually collected datasets, Real Madrid vs. FC Barcelona fandom and 2024 US-election left/center/right, and report that trained perspective scores separate held-out texts better than untrained embeddings, though left-leaning election texts are an acknowledged failure case.

Load-bearing premise

The load-bearing premise is that the authors' hand-assigned cluster labels are a valid ground truth for 'perspective'; if another annotator would label the same texts differently, both the measured distances and the prompt-control targets lose their meaning.

Editorial extensions

If this is right

Users could detect the stance of any text by reading its coordinates in a trained Perspective Space.
LLM outputs can be steered toward a desired viewpoint without retraining or fine-tuning the generation model.
Bias mitigation could be reframed as nudging outputs toward a predefined neutral or balanced region of the space.
The same metric could track shifts in perspective across a corpus, such as public discourse over time.
New topics require only a small labeled dataset, since the space inherits language knowledge from the base LLM embeddings.

Reading between the lines

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

Inference: Because the perspective space is built from a fixed LLM's embeddings, the method likely inherits that model's own biases about which texts are similar; an independent test with a different embedding backbone would show whether 'perspective' is model-dependent.
Inference: The 2-D PCA projection used for user targeting discards information; the method might work better in higher dimensions, but then users would need another way to specify intent.
Inference: The failure on left-leaning election texts hints that the approach may need cluster-balanced or more exhaustive training data before it can be trusted in real discourse tracking.
Inference: The greedy-coordinate-descent search is a proxy for continuous optimization; a gradient-based prompt optimizer might find stronger steering with fewer queries.

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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

You should know two things about this paper before deciding what to do with it. First, the core idea is genuinely worth a look: train a contrastive siamese BERT embedding to put texts with similar 'perspective' near each other, then use that geometry to pick prompt phrases that nudge an LLM's output toward a desired point. That is a reasonable way to make the vague notion of viewpoint operational, and the soccer dataset results in Table 1 show the embedding does separate the three clusters reasonably well after training. Second, the evidence for the central claims is much weaker than the conclusion suggests. The election dataset, which is the other test case, has a clear failure: the left-leaning test texts align more with the right-leaning training cluster than with their own. The prompt-control section reports no numbers at all, only that 'the correct phrase is returned,' and the success criterion is L2 distance in the same PCA space that defines the target. That is circular, or at least self-referential: you are checking whether the optimizer can move outputs inside an embedding that was built with the same labels, not whether a human would agree the output changed perspective. The paper also calls it greedy-coordinate-descent but actually does a brute-force search over phrase combinations, and there is a duplicated paragraph in Section 4 plus no released data or code. These are not nitpicks; they are the reasons the current manuscript does not establish the stated contribution. Even so, I would not dismiss the direction. The authors are transparent that the labels rest on their own interpretation, and they explicitly note the election failure, which suggests they are not hiding the problem. The related work is thin but not wrong; the main missing comparisons are to existing bias and stance detection methods and to other prompt-optimization approaches. As a paper, this needs major revision: a real definition or at least a validation of the perspective construct, a baseline comparison for the metric, quantitative control results with human agreement, and an honest fix for the circular evaluation. The right venue might be a workshop or a short paper after substantial rework. If you have time, it is worth reading the full text once to see the tables directly; the abstract slightly oversells what is actually reported. My recommendation for peer review: send it out, but the referees should be told that the bar is whether the authors can demonstrate external validity of the perspective space and non-circular control. I would not cite it in my own work yet.

Referee Report

4 major / 6 minor

Summary. The paper introduces Perspective-Dial, a two-component pipeline for quantifying and controlling the perspective of text. The first component is Perspective Space, a metric obtained by fine-tuning BERT embeddings with contrastive learning on two manually labeled datasets (soccer fan texts from Quora; US election opinion pieces), with labels assigned by the authors. The second component is prompt engineering: after PCA projection to two dimensions, a user specifies a target point and the system searches over base/additional phrases to find a prompt whose LLM output minimizes L2 distance to the target in the Perspective Space. The paper reports metric evaluation in Tables 1 and 2 and illustrates prompt-control outcomes for GPT-4, concluding that the method can measure and adjust viewpoint in LLM outputs.

Significance. If the claims were established, the approach could provide a practical tool for detecting and steering bias and narrative in LLM outputs, with applications in public discourse analysis and debate bots. The paper deserves credit for formulating perspective as a measurable construct, for using contrastive learning on embeddings, for making the dependence on author labels explicit, and for acknowledging the failure of the metric on the left-leaning election test set. However, the current evidence does not establish the central claim: the metric is validated only on two small author-labeled datasets, the control evaluation is circular and non-quantitative, and no external or human validation is provided. The contribution is therefore currently an interesting proposal rather than a demonstrated method.

major comments (4)

[Section 4 and Figure 5] The prompt-control evaluation is circular with respect to the metric. The loss minimized during prompt search is the L2 distance in the 2D PCA projection of the same Perspective Space that was trained on the authors' labels; success is reported only qualitatively as 'the correct phrase is returned' with no loss values, success rates, or comparisons to baselines such as random phrase search. This only demonstrates that the optimizer can move outputs inside the authors' constructed embedding. An independent check, such as human raters judging whether outputs express the intended perspective or evaluation against an external perspective measure, is required before the claim of controlling LLM output perspective is supported.
[Table 2] For the election test set, the left-leaning texts align more closely with the right-leaning training cluster after training (0.62 vs. 0.53 for the left cluster), while the paper only notes this as a limitation and does not quantify how this affects the control experiments. Since the control objective inherits the metric, a failure of the metric on one of the three perspectives directly undermines the claim that Perspective-Dial can adjust viewpoint across perspectives. The paper should either correct the metric (for example, with more training data or a different training setup) or restrict the claim to perspectives for which the metric is shown to work.
[Section 3] The only supervision for the Perspective Space is the authors' manual cluster labels, as stated: 'the labels assigned to each text was based of the authors' interpretation of the text.' No inter-annotator agreement, validation against independent annotations, or evidence that the clusters are reproducible is provided. Since perspective is defined entirely by these labels, the metric's external validity is untested; another annotator could plausibly produce a different space. At minimum, the paper needs multiple annotators and agreement statistics, and ideally a held-out human evaluation of the metric's outputs.
[Section 4 and Figure 4] The procedure described as 'greedy-coordinate-descent' is, by the paper's own description, a brute-force search over all combinations of base phrases and additional phrases ('a simulation of greedy-coordinate-descent'). No iterative coordinate-wise updates are performed; this is not coordinate descent. This mischaracterization matters because the paper's claim of an optimization-based control method rests on the algorithm's name and purported efficiency, yet no algorithmic details (search space size, number of queries, convergence) are reported.

minor comments (6)

[Abstract and Section 1] The text contains several typos, including 'discource', 'thourougly', and 'consesus'; please proofread the manuscript carefully.
[Section 4] A paragraph describing PCA and greedy-coordinate-descent appears twice verbatim; the duplicate should be removed.
[Table 2] The table and surrounding text use 'left learning' instead of 'left-leaning' in several places.
[Figure 4 caption] The caption reads 'Greedy-coordinate-decent' and should be 'Greedy-coordinate-descent'.
[Section 3] The phrase 'based of' should be 'based on'.
[Introduction] The sentence 'method to quantity, measure and control perspective' should use 'quantify' rather than 'quantity'.

Circularity Check

2 steps flagged · score 6.0 of 10

Prompt-control success is demonstrated only as reduced L2 distance in the same author-labeled PCA space that defines the target; the metric itself is not independently validated.

self definitional [Section 4, 'Optimization-Based Prompt Engineering' (pp. 4-5)]
"Within the PCA space, a user can now specify a point in 2 dimensions within the perspective space. ... The loss function between the LLM output and the desired perspective in the perspective space is depicted as the L2 norm between the two points in the 2-dimensional PCA space. ... Through a brute-force search of all combinations of base phrases and additional phrases, it is shown for both data sets, the correct phrase is returned."

The 'desired perspective' is a point in the same PCA-compressed, contrastively trained Perspective Space, and success is measured by the L2 distance to that point. The targets are the paper's own cluster labels, which are also what trained the metric. Thus any phrase that minimizes the distance is automatically deemed 'correct' according to that same embedding; no independent, human-verifiable check of the LLM output's viewpoint is reported. The control claim reduces by construction to the trained metric rather than providing an external validation of perspective adjustment.
renaming known result [Section 4, 'Optimization-Based Prompt Engineering' (p. 5)]
"Instead of swapping out individual tokens, each query consists of a [base phrase] + [combination of additional phrases], where the additional phrases are swapped out to perform a simulation of greedy-coordinate-descent (GCD)."

The method named 'greedy-coordinate-descent' is, by the paper's own description, an exhaustive enumeration over predefined base and additional phrase combinations. Calling this greedy-coordinate descent renames a brute-force search as an optimization procedure, and the reported success is only over the finite, hand-selected phrase set. This is a renaming rather than a derivation of a coordinate-descent control result, so the claimed prompt-optimization outcome does not follow from the named algorithm.

full rationale

The metric component has some non-circular content: contrastive training on manually labeled clusters and evaluation on held-out test texts (Tables 1 and 2) does test generalization under the authors' labeling scheme. However, the central control claim is circular: Section 4 defines the desired perspective as a point in the PCA-reduced Perspective Space and the loss as L2 distance to that point; success is then reported as 'the correct phrase is returned.' Since the target points correspond to the same cluster labels used to train the metric, any phrase minimizing that distance is classed as correct by construction, with no human evaluation or independent measure of viewpoint. The paper itself concedes that the election left-leaning test set aligns more with the right-leaning train cluster (Table 2: 0.62 vs 0.53), undermining the metric's independent validity, yet the control section does not address this. Additionally, the claimed greedy-coordinate-descent is explicitly a brute-force search over base and additional phrase combinations, so it is a renaming rather than an optimized coordinate-descent procedure. The score of 6 reflects partial circularity: the prompt-control evaluation reduces to the fitted embedding, while the underlying metric still retains some held-out generalization evidence under the authors' labels.

Assumptions & free parameters 4 free parameters · 5 assumptions · 2 invented entities

The central claim rests on several unverified domain assumptions: author-assigned labels define ground-truth perspective; BERT embeddings can be fine-tuned to separate perspectives; 2D PCA preserves the geometry needed for control; and greedy phrase swaps reliably steer outputs. Free parameters include the hand-set center similarity value, the unstated contrastive margin, the PCA dimension, and the unreleased phrase vocabulary. The paper introduces no physical entities, but the Perspective Space itself is a self-referential construct.

free parameters (4)

center/neutral similarity label = 0.35
In Section 3, the center cluster is labeled 0.35 similar to each pole; this hand-set value shapes the metric without justification.
contrastive loss margin = not reported
Contrastive loss requires a minimum margin between dissimilar pairs; the value is not stated, so training is not reproducible.
PCA components = 2
Embedding space reduced to 2 dimensions for user targeting; this choice affects the L2 loss geometry used in prompt optimization.
additional phrase vocabulary = not reported
The brute-force prompt search uses a hand-picked set of base and additional phrases; the list is not provided.

assumptions (5)

domain assumption BERT embeddings, after contrastive fine-tuning, capture perspective in a way that cosine distance reflects perspective difference.
Section 3 assumes the embedding space can be trained to separate perspectives; no theoretical or external evidence.
ad hoc to paper Author-assigned cluster labels are valid ground truth for perspective.
Section 3 explicitly says labels are based on the authors' interpretation; no inter-annotator agreement or external criterion.
ad hoc to paper Two manually collected datasets (soccer, US election) are sufficient to demonstrate general perspective measurement.
The paper generalizes from two small datasets and admits left-leaning test texts misalign in Table 2.
domain assumption PCA projection to 2D preserves enough perspective structure for the L2 loss to guide prompt optimization.
Section 4 uses 2D PCA; no analysis of variance retained or distortion.
domain assumption Greedy coordinate descent over phrase swaps reliably finds prompts shifting LLM output perspective.
Only a brute-force search over a small phrase set is reported, and no loss values or distances are given.

invented entities (2)

Perspective Space
purpose: A fine-tuned embedding metric claimed to measure text perspective quantitatively.
No external validation, released artifacts, or falsifiable predictions; the space is defined entirely by the paper's own training labels and evaluated with its own similarity measure.
perspective as a point in a 2D PCA projection
purpose: Gives users a target coordinate for steering LLM output.
The target is a coordinate in the trained space, so control success is measured against the method's own construct; no independent ground truth.

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Cite this review

Pith. "Pith review of Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs." pith.science (2026). https://pith.science/paper/RZZQ7SN4

@misc{pith2026250623377,
  author       = {Pith},
  title        = {Pith review of: Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RZZQ7SN4}},
  note         = {Machine review of arXiv:2506.23377}
}

read the original abstract

Large language models (LLMs) are used in a variety of mission-critical roles. Due to the rapidly developing nature of LLMs, there is a lack of quantifiable understanding of the bias and perspective associated with LLM output. Inspired by this need, this paper considers the broader issue of perspective or viewpoint of general text and perspective control of large-language model (LLM) output. Perspective-Dial consists of two main components: a (1) metric space, dubbed Perspective Space, that enables quantitative measurements of different perspectives regarding a topic, and the use of (2) Systematic Prompt Engineering that utilizes greedy-coordinate descent to control LLM output perspective based on measurement feedback from the Perspective Space. The empirical nature of the approach allows progress to side step a principled understanding of perspective or bias -- effectively quantifying and adjusting outputs for a variety of topics. Potential applications include detection, tracking and mitigation of LLM bias, narrative detection, sense making and tracking in public discourse, and debate bot advocating given perspective.

Figures

Figures reproduced from arXiv: 2506.23377 by the authors.

**Figure 2.** Contrastive learning via siamese network used via [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Utilization of PCA to compress embedding space [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Greedy-coordinate-decent used to perform [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

**Figure 5.** Figure 5: Greedy-coordinate-descent approach using the perspective metric. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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doi: 10 .18653/v1/2021.acl-long.416

Association for Computational Linguis- tics. doi: 10 .18653/v1/2021.acl-long.416. URL https: //aclanthology.org/2021.acl-long.416/. 6 Title Suppressed Due to Excessive Size OpenAI. Gpt-4 technical report,

work page 2021