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Spot the BlindSpots: Systematic Identification and Quantification of Fine-Grained LLM Biases in Contact Center Summaries

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read All 20 LLMs tested distort call summaries the same way — amplifying negative sentiment and early talk, dropping rapport and directives — and this paper quantifies the distortion with a 15-dimension taxonomy and two metrics.

desk verdict First operational-bias benchmark for contact-center summarization with a genuinely useful taxonomy, but the headline cross-model pattern rests partly on a labeler that is also one of the models under test. read the letter →

arxiv 2508.13124 v1 pith:XL7N3S2E submitted 2025-08-18 cs.CL cs.AI

Kawin Mayilvaghanan , Siddhant Gupta , Ayush Kumar This is my paper · ORCID
classification cs.CLcs.AI
keywords operationalbiasLLMsummarizationcontactcentertranscriptsJensen-ShannondivergencecoveragemetrictaxonomyLLM-as-labelerabstractive
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

Generative summaries of contact-center calls can be fluent and factually accurate and still misrepresent the conversation: they can amplify a customer's frustration, bury resolution steps, omit the entities needed for follow-up, or scramble the order of events. This paper argues that such "operational bias" is invisible to standard quality metrics and LLM-as-a-Judge scores, and it builds a framework, BlindSpot, to measure it. The framework labels every transcript turn and every summary proposition along 15 bias dimensions — sentiment, topic, position, politeness, urgency, and others — then compares the two label distributions with Jensen–Shannon divergence (the Fidelity Gap) and a Coverage percentage. Applied to 2,500 real transcripts summarized by 20 LLMs across five model families, nearly every model showed the same signature: over-representing Negative sentiment and Early segments while under-representing Building-Rapport and Directives. The authors also show that a system prompt engineered from those findings reduces bias across the tested models, suggesting the measurement is actionable rather than merely diagnostic.

What carries the argument

The carrying mechanism is a pair of label distributions over a 15-dimension taxonomy of operational bias. An LLM labeler (GPT-4o) assigns categorical labels to each transcript turn for the semantic dimensions; structural dimensions (Speaker, Position, Turn Length) are computed directly from metadata and indices, and relational ones (Emotion Shift, Temporal Sequence) are derived from the primary labels. The generated summary is decomposed into atomic propositions, labeled with the same scheme, and mapped back to the source turns it condenses. Bias is then quantified dimension by dimension by the Fidelity Gap, the Jensen–Shannon divergence between the transcript distribution and the summary distribution, and by Coverage, the percentage of source labels that survive into the summary. This converts "bias" from a qualitative concern into two per-dimension numbers that expose which specific labels are over- or under-represented.

What would settle it

Re-annotate a random sample of transcript turns and summary propositions with human raters, or with a different-family LLM as the labeler such as Claude, and check whether the reported per-dimension Fidelity Gaps and the "negative-sentiment over-representation" signature reproduce. If the labeler's own labels carry the same skew — for instance, tagging 'Negative' more readily on transcripts than on summaries, or vice versa — then the measured bias is an artifact of the annotation instrument, and the cross-model consistency would hold regardless of the summarizer. A second check: force all models to produce summaries at matched compression levels and see whether the Temporal Sequence JS divergence of roughly 0.37 persists, which would separate reordering bias from length-driven information loss.

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Extended reading notes

Core claim

BlindSpot's central claim is that biases in LLM call summarization are systemic: all 20 evaluated models, spanning several families and scales from 1-billion-parameter models to frontier systems, exhibit the same pattern of distortion, with only the two Gemini models standing out as substantially worse. The most consistent signature is a simplified, problem-focused narrative, with models over-representing negative sentiment and early parts of the call while under-representing rapport-building, directives, and other resolution-focused content. The hardest dimension is Temporal Sequence, with an average JS divergence of 0.370, meaning event order and cause-and-effect are frequently altered; the largest information loss is in Entity Type, where roughly half of named entities are omitted. Bias grows with compression (correlation 0.76 with JS divergence and −0.88 with coverage), while holistic LLM-Judge scores correlate only weakly with bias, so a summary can score 4.85 out of 5 and still badly reorder events. The framework's usefulness is demonstrated by a mitigation experiment: a targeted prompt built from the measured label-level skews lowered the average Fidelity Gap and raised Coverage for all tested models, with gains of up to +4.87 percentage points in coverage.

Load-bearing premise

The whole measurement rests on the GPT-4o labeler's annotations being an accurate ground-truth reading of both the transcript and the summary; if the labeler itself systematically mislabels — for example, over-tagging negative sentiment or politeness cues — the Fidelity Gap and Coverage numbers partly measure labeler bias rather than summarizer bias, and this is especially acute because GPT-4o is also one of the 20 summarizers under evaluation.

Editorial extensions

If this is right

  • Because bias is systemic rather than tied to model scale or family, choosing a larger or different-family model will not by itself fix the distortion; the same problem-focused narrative signature appears across all 20 models.
  • Standard holistic quality scores are insufficient safety checks: LLM-Judge scores correlate only weakly with bias (r ≈ −0.34 for JS divergence, 0.33 for coverage), so deployment decisions need bias-specific measurement.
  • Compression is a driver of bias: higher compression correlates strongly with a larger Fidelity Gap (r = 0.76) and lower Coverage (r = −0.88), so summary-length targets trade off directly against fidelity.
  • Operational metrics derived from summaries — such as First Call Resolution, agent QA scorecards, or churn-risk flags — inherit these biases, because omitted entities, reordered events, and sanitized politeness or disfluency all corrupt downstream judgments.
  • The measured label-level skews are actionable: a prompt that instructs the model to balance sentiment, cover all conversation segments, include rapport and directives, and preserve chronology reduces the Fidelity Gap and raises Coverage across the tested models.
  • pith_inferences_full_list_placeholder_removed

Reading between the lines

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

  • Because the same labeler (GPT-4o) that defines ground truth is itself one of the 20 summarizers under evaluation, the reported Fidelity Gaps may partly reflect the labeler's own labeling tendencies; a direct test would be to re-run the audit with a different-family labeler, such as Claude or Llama, and check whether the model rankings and label skews persist.
  • The distributional-comparison recipe — label source units, label summary units, compare with divergence plus coverage — transfers to other high-stakes summarization domains such as medical notes, legal documents, or incident reports, where a fluent summary that silently shifts sentiment or drops entities creates downstream risk.
  • The framework does not yet say which biases matter most for outcomes. A natural extension is to link Fidelity Gap and Coverage to measured downstream effects, for example whether an auditor or coach makes different decisions when true entity coverage is 52 percent rather than 80 percent.
  • The shared cross-model distortion fingerprint hints that the biases may be baked into training objectives that reward concise, problem-focused narratives; if so, the remedy would extend beyond prompting to evaluation-aware training or decoding that penalizes distribution shift.
  • pith_inferences_full_list_placeholder_removed
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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

3 major / 6 minor

Summary. The paper introduces BlindSpot, a framework for quantifying what it calls operational bias in contact-center call summarization. BlindSpot defines a 15-dimension taxonomy (Content & Information Fidelity, Conversational Structure & Flow, Speaker & Role Representation, Linguistic & Stylistic, Affective & Pragmatic Interpretation), uses GPT-4o as a zero-shot labeler to obtain categorical label distributions over transcript turns and over summary propositions, and measures bias via the Jensen–Shannon Fidelity Gap and Coverage %. The authors evaluate 20 LLMs on 2,500 proprietary contact-center transcripts and report that biases are systemic across all models, with consistent over-representation of Negative sentiment and Early segments and under-representation of Building-Rapport and Directives. They also present a targeted-prompt mitigation experiment on nine models that improves Coverage by up to +4.87% and reduces JSD. The appendices provide detailed taxonomy label sets, labeler prompts, alternative divergence metrics, transcript-length analyses, and a human validation study of the LLM labeler (93.68% agreement).

Significance. If the central claim holds, the work is a useful and original contribution: it shifts summarization evaluation from holistic quality metrics to fine-grained, operationally meaningful bias dimensions, and it demonstrates that such bias measures are actionable through prompt intervention. The paper's strengths include a clearly specified taxonomy with concrete label sets, a reproducible prompt suite, an unusually broad model sweep (20 models across families and scales), robustness checks with alternative divergence metrics, and an explicit mitigation experiment with reported effect sizes. The empirical finding that high LLM-Judge scores can coexist with large Temporal Sequence bias is a valuable caution for practitioners. However, the systemic-bias claim is load-bearing and currently rests on a single-annotator validation that does not address the two channels that most threaten the measurement: differential labeler behavior on turns versus propositions, and the unit-granularity mismatch between the two distributions being compared. These concerns are fixable, but they require additional validation and analysis rather than presentation edits alone.

major comments (3)
  1. [§2.3, Appendix B.2] The central claim that all models systematically over-represent Negative and Early and under-represent Building-Rapport and Directives requires that the GPT-4o labeler measure the same latent categories with errors that are not systematically different on long conversational turns than on extracted propositions. The published validation reports only an aggregate 93.68% agreement with a single human annotator on 1,000 turn-proposition pairs; it gives no per-dimension accuracy, no confusion matrices, no inter-annotator agreement, and no breakdown by input unit type. A labeler can reach 93.68% overall agreement while applying, say, a lower threshold for 'Negative' on short context-free propositions than on multi-clause turns; under that scenario the Fidelity Gap and Coverage numbers would partly measure the labeler's differential sensitivity rather than the summarizer's distortion. Please report per-dimension agreement with the human annotations, and either validate the labeler on summary propositions separately or provide a per-unit-type error analysis showing that label bias does not vary with unit type.
  2. [§2.2, §2.3 (Equations defining Pi,d and Qi,d)] FidelityGap compares a distribution Pi,d computed over transcript turns with a distribution Qi,d computed over summary propositions, after only a turn-to-proposition mapping for turn-dependent dimensions. Because a turn can contain multiple claims and a proposition is, by construction, a single atomic claim, the two distributions are not defined over comparable units; even a verbatim-copy summarizer would generally produce a different label distribution over propositions than over turns. The paper does not establish that the metrics are invariant under proposition decomposition, so part of the measured 'bias' may be an artifact of unit granularity rather than a property of the summarizer. Please add a synthetic or controlled experiment (e.g., decompose transcripts turn-by-turn into propositions and show that JSD between turn-level and proposition-level distributions of the same source is near zero), or otherwise justify the comparability of the two distributions.
  3. [Table 21, Table 2, §4] GPT-4o is simultaneously the zero-shot labeler for all dimensions, the LLM Judge for holistic quality, and one of the 20 summarizers under evaluation. If the labeler shares the summarizer's distributional preferences, then the bias estimates for GPT-4o (and, to a lesser degree, for all models sharing the labeler's family) are partially confounded with the labeler's own biases. The paper's Section 6 acknowledges in general terms that LLM labeling can propagate model biases, but it does not test the impact on the empirical conclusions. Concretely, please report the main systemic-bias results with GPT-4o's summaries excluded, and/or re-label a subsample of GPT-4o summaries with an independently implemented labeler (e.g., a different model family) to show that the qualitative pattern of over- and under-represented labels is unchanged.
minor comments (6)
  1. [Table 2 / §4] The claim that the two Gemini models are notable outliers (average JSD 0.119 vs. 0.077–0.087 for other models) is reported without any statistical test or confidence interval; with 15 dimensions and 20 models, a per-model or per-dimension significance test (or at least bootstrap intervals) would strengthen this assertion.
  2. [Figure 3 and Figure 6] The definition of a label being 'over-represented' or 'under-represented' is not fully specified; please state whether the comparison is between average summary-distribution mass and average transcript-distribution mass, and how the two annotated percentages in Figure 6 are computed.
  3. [Appendix B.2] The phrase '1,000 turn-proposition pairs' is ambiguous: it should be clarified whether each pair is a transcript turn plus its associated summary proposition, and whether the human annotator validated all 15 dimensions on each pair or only a subset; a per-dimension table would resolve this.
  4. [Table 23] The table header reads 'T otal V ariation Distance' in the appendix; this is a typo that should be corrected to 'Total Variation Distance'.
  5. [Appendix E, Summary Labeling prompt] The prompt text includes the literal template placeholder '{len(summary_propositions)}' inside the user prompt; if this is a real artifact of the prompt construction, it should be replaced with the actual count in the presented example, or the placeholders should be explicitly described as variables.
  6. [§4, 'Influence of Compression on Bias'] The Pearson correlations (r = 0.76 for JSD, r = -0.88 for coverage) are computed over only 20 model-level points; please report the number of points, p-values, and ideally a per-transcript or per-dimension correlation to show that the relationship is not driven by the two Gemini outliers alone.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the bias measurements are empirical comparisons of independently validated label distributions, not reductions to the framework's inputs.

full rationale

The paper's derivation chain is self-contained. The BlindSpot framework defines a taxonomy, labels transcript turns and summary propositions with GPT-4o (validated at 93.7% against human annotations in Appendix B.2), aggregates these into distributions P_d and Q_d, and computes Jensen-Shannon divergence and coverage. No parameter is fitted to the target result, and no prediction is equivalent to an input by construction. The central finding—that summaries over-represent Negative and Early labels while under-representing Building-Rapport and Directives—is an empirical comparison of measured label distributions, not a consequence of the metric definitions alone. Although any lossy summary yields nonzero JSD, the specific directional skews in Figure 3 are data-dependent rather than forced by the equations. The only same-author citation (Sachdeva et al., 2023) is background context for LLM call summarization and is not load-bearing. The paper's own Limitations section acknowledges that LLM labeling may propagate model biases into annotations; this is a validity threat for the absolute bias magnitudes, especially because GPT-4o also serves as one of the 20 summarizers in Table 21, but it is a measurement concern, not a circular derivation. The human validation provides independent grounding for the labeler, and no uniqueness theorem or ansatz is imported from prior work. Therefore, no circular step can be exhibited.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the normative assumption that summaries should preserve label distributions, and on the validity of an LLM as the measurement instrument. The only hand-set numeric parameters are the binning thresholds for Turn Length and Position. No new physical entities are introduced.

free parameters (2)
  • Turn Length bucket boundaries = 0-5, 5-15, 15-50, 50-100, >100 tokens
    The thresholds in Table 11 are hand-chosen. Different boundaries would change the Turn Length label distributions, and therefore the measured Turn Length bias.
  • Position quintile boundaries = 20%, 40%, 60%, 80% of turns
    Position labels are computed as quintiles (Table 4). This arbitrary discretization defines the Position bias metric.
assumptions (3)
  • domain assumption A faithful summary should approximately preserve the label distribution of the source transcript
    This is the normative core of both metrics. Invoked in Section 2.2 (Problem Formulation) and Section 2.3 (Bias Quantification). If summaries are allowed to curate intentionally, high Fidelity Gap is not necessarily a defect.
  • domain assumption The GPT-4o LLM Labeler produces sufficiently accurate labels for the 15 dimensions
    Validated on 1000 pairs by one human annotator (93.68%, Appendix B.2), but with no per-dimension error table and no inter-annotator agreement; the labeler is also one of the models under evaluation.
  • ad hoc to paper Chronological fidelity is the only valid ideal for Temporal Sequence; the reference distribution is a one-hot In-order vector
    Introduced in Section 2.3 and B.1.3. This imposes a normative ideal rather than measuring the actual transcript ordering distribution.

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Cite this review

Pith. "Pith review of Spot the BlindSpots: Systematic Identification and Quantification of Fine-Grained LLM Biases in Contact Center Summaries." pith.science (2026). https://pith.science/paper/XL7N3S2E

@misc{pith2026250813124,
  author       = {Pith},
  title        = {Pith review of: Spot the BlindSpots: Systematic Identification and Quantification of Fine-Grained LLM Biases in Contact Center Summaries},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XL7N3S2E}},
  note         = {Machine review of arXiv:2508.13124}
}
read the original abstract

Abstractive summarization is a core application in contact centers, where Large Language Models (LLMs) generate millions of summaries of call transcripts daily. Despite their apparent quality, it remains unclear whether LLMs systematically under- or over-attend to specific aspects of the transcript, potentially introducing biases in the generated summary. While prior work has examined social and positional biases, the specific forms of bias pertinent to contact center operations - which we term Operational Bias - have remained unexplored. To address this gap, we introduce BlindSpot, a framework built upon a taxonomy of 15 operational bias dimensions (e.g., disfluency, speaker, topic) for the identification and quantification of these biases. BlindSpot leverages an LLM as a zero-shot classifier to derive categorical distributions for each bias dimension in a pair of transcript and its summary. The bias is then quantified using two metrics: Fidelity Gap (the JS Divergence between distributions) and Coverage (the percentage of source labels omitted). Using BlindSpot, we conducted an empirical study with 2500 real call transcripts and their summaries generated by 20 LLMs of varying scales and families (e.g., GPT, Llama, Claude). Our analysis reveals that biases are systemic and present across all evaluated models, regardless of size or family.

Figures

Figures reproduced from arXiv: 2508.13124 by the authors.

Figure 1
Figure 1. A call transcript (left) with mixed sentiment is [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The BlindSpot framework evaluates bias in call summaries. The transcript pipeline (red) creates a reference bias distribution by labeling turns for each dimension with an LLM Labeler. The summary pipeline (green) creates a summary distribution by labeling propositions. Bias is quantified by comparing these distributions using Fidelity Gap (JSD) and Coverage %. White boxes provide examples. (Best viewed in color.) Th… view at source ↗
Figure 4
Figure 4. Change in JSD (top) and Coverage % (bottom) [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (3 more)
Figure 3
Figure 3. Figure 3: Specific labels that are over- or under [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 5
Figure 5. Figure 5: PCA projection of the 20 LLMs based on their 15-dimensional JSD bias vectors. The tight clusters towards [PITH_FULL_IMAGE:figures/full_fig_p028_5.png]
Figure 6
Figure 6. Figure 6: Systematic over- and under-representation of specific labels across all 20 LLMs. The top plot depicts [PITH_FULL_IMAGE:figures/full_fig_p029_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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  1. When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    LLM compression of filings and earnings calls often changes the source-implied bear/neutral/bull decision; agentic multi-candidate auditing against the source reduces those flips.

Reference graph

Works this paper leans on

61 extracted references · 60 canonical work pages · cited by 1 Pith paper

  1. [1]

    Biases in this class directly compromise the summary’s primary function as a reliable source of truth

    Class: Content & Information Fidelity Core Purpose: To ensure the summary is a factually ac- curate and actionable record of the conversation’s substance. Biases in this class directly compromise the summary’s primary function as a reliable source of truth. Dimensions within this Class: • Entity Type: This dimension tracks the pres- ence of key named enti...

    work page

  2. [2]

    story” of the call is often as important as its individual facts. Dimensions within this Class: • Position: This dimension addresses the well- known “lead bias,

    Class: Conversational Structure & Flow Core Purpose: To assess the summary’s narrative integrity, ensuring the chronological and causal se- quence of events is preserved. The “story” of the call is often as important as its individual facts. Dimensions within this Class: • Position: This dimension addresses the well- known “lead bias,” where models favor ...

    work page

  3. [3]

    This is essential for accountability and performance evaluation

    Class: Speaker & Role Representation Core Purpose: To focus on the fair and accurate attribu- tion of utterances and actions to the conversational participants. This is essential for accountability and performance evaluation. Dimensions within this Class: • Speaker: This dimension reflects the balance in prominence between the customer and agent voices. A...

    work page

  4. [4]

    sanitizes

    Class: Linguistic & Stylistic Dimensions Core Purpose: To target distortions in the manner and tone of the conversation. These stylistic features carry significant diagnostic information about the customer experience and agent professionalism that is lost if a summary only reports literal content. Dimensions within this Class: • Language Complexity: This ...

    work page

  5. [5]

    unhappiness

    Class: Affective & Pragmatic Interpretation Core Purpose: To address the emotional and in- tentional subtext of the conversation, which is often more critical for business outcomes than the raw facts. Dimensions within this Class: • Sentiment: This dimension captures the emo- tional valence of the interaction. Its importance for risk management cannot be ...

    work page

  6. [6]

    simple_syntax Predominantly short, declarative sentences

    lang - Language Complexity (multi-select) Code Description standard_clear Clear, direct, and easily understood language. simple_syntax Predominantly short, declarative sentences. complex_syntax Long, multi-clause, or convoluted sentences. technical_terms Specialized terms related to a specific domain. industry_jargon Terms/phrases specific to an industry/...

    work page

  7. [7]

    I can help. What is your name? The account is open

    lang - Language Complexity (multi-select) Code Description Example standard_clear Clear, direct, and easily understood language. The default if no other specific complexities are prominently fea- tured. simple_syntax Predominantly short, declarative sentences. “I can help. What is your name? The account is open.” complex_syntax Long, multi-clause, or con-...

    work page

  8. [8]

    Content & Information Fidelity Entity Type People, Identifiers, Phone Number, Email, Time Info, Date, Location Info, Products/Services, Mon- etary, Company/Organization, Other Over/underrepresentation of key factual data required for action. Topic Greeting/Introductions, Identity Verification, Is- sue, Information Gathering, Product/Service In- quiry, Dia...

    work page

Show all 61 references
  1. [9]

    Turn Length (computed) Very Short, Short, Mid, Long, Very Long Variation in summary fidelity across dia- logues of different length

    Conversational Structure & Flow Position (computed) Very Early, Early, Mid, Late, Very Late Preference for information from specific seg- ments of the conversation. Turn Length (computed) Very Short, Short, Mid, Long, Very Long Variation in summary fidelity across dia- logues ...

  2. [10]

    customer voice and contribution

    Speaker & Role Representation Speaker (computed) Agent, Customer Unequal representation of agent vs. customer voice and contribution. Agent Action Request Information, Provide Information, Con- firm Understanding, Build Rapport, Acknowledge, Escalate, Compliance, Other Misrepr...

  3. [11]

    Linguistic & Stylistic Dimensions Language Complexity (Multiselect) Simple/Clear, Declarative, Long/Multi-Clause, Technical, Jargon, Abbreviations, Dense, Wordy/- Vague, Formal, Informal, Empathic, Blunt, Slang, Passive V oice Disproportionate simplification or complica- tion ...

  4. [12]

    okay,” “fine,

    Affective & Pragmatic Interpretation Sentiment Very Positive, Positive, Neutral, Negative, Very Negative Divergence in emotional tone, masking cus- tomer satisfaction or churn risk. Emotion Shift (derived) Balanced, Amplified, Attenuated, Inverted, Spuri- ous How the summary d...

  5. [13]

    , Sk} of 50 turns each

    Transcript Segmentation To manage long contexts and ensure consistent JSON output from the LLM Labeler, each transcript T is first parti- tioned into sequential, non-overlapping segments {S1, . . . , Sk} of 50 turns each. This segmentation mitigates potential performance degra...

  6. [14]

    Turn-level Annotation We employ a hybrid ap- proach to annotate every turn in the transcript across all bias dimensions. The annotation source depends on the nature of the dimension: • LLM-Annotated (Semantic Dimensions): For dimensions requiring semantic understanding, we use...

  7. [15]

    ground truth

    Reference Distribution (Pd) Generation The turn-level annotations are aggregated across the en- tire transcript to form a normalized categorical distri- bution Pd for each dimension d, which serves as our reference or “ground truth.” B.1.2 Stage 2: Summary Pipeline (Generating...

  8. [16]

    This mirrors real-world usage where the model processes the entire conversation at once

    Summary Generation The summarization model under evaluation,M, generates an abstractive summary S from the full, unsegmented transcript T . This mirrors real-world usage where the model processes the entire conversation at once

  9. [17]

    Proposition Extraction To enable fine-grained, sentence-level analysis, the generated summary S is decomposed into a set of minimal semantic units, or propositions{p1, . . . , pm}. This is performed by an LLM instructed to isolate each atomic fact or claim, creating a standard...

  10. [18]

    Proposition Labeling and Mapping Each proposition is then labeled using the same hybrid methodology as the transcript turns. For turn- dependent dimensions like Position or Urgency, a crucial mapping step is performed where the LLM Labeler identifies the set of source turn ind...

  11. [19]

    B.1.3 Stage 3: Bias Quantification and Interpretation

    Summary Distribution (Qd) Generation The proposition-level labels are aggregated to form the summary’s categorical distribution Qd for each di- mension d. B.1.3 Stage 3: Bias Quantification and Interpretation

  12. [20]

    A score of 0 indicates identical distributions, while higher values indicate greater divergence

    Metric Calculation With both Pd and Qd com- puted, we quantify bias using two complementary metrics: • Fidelity Gap: We use Jensen-Shannon (JS) Di- vergence between Pd and Qd to measure the overall distributional distortion. A score of 0 indicates identical distributions, whil...

  13. [21]

    Interpreting Results The combination of our two metrics provides a nuanced view of a summary’s faithfulness. For each dimension, we interpret the pair as follows: • Low Fidelity Gap & High Coverage: A faith- ful summary that retains nearly all source labels and preserves their...

  14. [22]

    Dataset Creation: We randomly sampled 1,000 turn-proposition pairs from our dataset, ensuring coverage across all 15 bias dimensions. A human annotator trained in contact center an- alytics and familiar with the operational context, independently validated each label assigned ...

  15. [23]

    LLM-as-a-Judge

    Evaluation: The LLM Labeler (L) achieved an accuracy of 93.68% against human annotation. As expected, performance varied slightly by dimension, with higher accuracy on objective dimensions like Entity Type and slightly lower, yet still high, accuracy on more subjective di- men...

  16. [24]

    It penalizes any hallucina- tions or contradictions

    Factual Consistency: This criterion ensures that all claims, facts, and events mentioned in the summary are factually supported by the source transcript. It penalizes any hallucina- tions or contradictions

  17. [25]

    Completeness: This assesses whether the sum- mary includes all critical information from the conversation without significant omissions of key events, decisions, or outcomes

  18. [26]

    Presence of irrelevant information,

    Succinctness and Relevance: This criterion, framed in the prompt as “Presence of irrelevant information,” penalizes summaries that include extraneous details, conversational filler, or other information not directly relevant to the core pur- pose of the interaction. The judge ...

  19. [27]

    Factual Consistency - Are the facts and claims in the summary correct?

  20. [28]

    Completeness - Is all necessary infor- mation included?

  21. [29]

    Presence of irrelevant information - Does the summary stay focused on the task? Output Format: Score: [1-5] Reason: [Feed- back on prompt] Acknowledged Limitations While scalable and effective for capturing general quality, we acknowl- edge the known limitations of the LLM-as-...

    2024

  22. [30]

    Use the original words where possible; do not paraphrase unnecessarily

  23. [31]

    Resolve pronouns if possible

  24. [32]

    A proposition should typically follow the (subject; predicate; object/modifier) structure

  25. [33]

    Include time, place, and recipient details as separate propositions when appropriate

  26. [34]

    1", "2") and values are the proposition texts. • entities: an object with the exact keys listed above, each containing a list of extracted entities (even if empty). •

    Do not explain or justify. Just return the list of propositions. Next, extract entities from the summary and categorize them into the following predefined types: • people: Agent name, Customer name, 3rd parties • identifiers: Ticket ID, Account No., Policy No. • phone_number: ...

  27. [35]

    <dialog> {transcript} </dialog>

    Summarize the following dialog. <dialog> {transcript} </dialog>

  28. [36]

    <transcript> {transcript} </transcript>

    Please provide a summary of the contact-center conversation transcript. <transcript> {transcript} </transcript>

  29. [37]

    1: Speaker:

    Generate a summary of the conversation. <conversation> {transcript} </conversation> Prompt for Transcript Labeling You are a transcript analysis assistant. Your task is to annotate each turn in a conversation transcript using a fixed set of linguistic and conversational dimens...

  30. [38]

    okay,” “fine,

    sent - Sentiment Code Meaning very_pos Strongly positive tone pos Moderately positive tone neg Moderately negative tone very_neg Strongly negative tone info Information content or presence of factual tokens (dates, names, IDs) – high priority over neutral neutral Does not have...

  31. [39]

    Could you confirm your order?

    topic - Topic Category Code Description greet Greetings, introductions id_verif ID or account verification issue Customer’s reason for contact info_gath Agent probing/investigating prod_inq Product or service questions diag Diagnosis or troubleshooting soln Proposing a solutio...

  32. [40]

    uh”, “um

    rep - Repetition Code Description no_rep No repetition present cust_self Customer repeats self agent_self Agent repeats self cust_echo Customer echoes agent agent_echo Agent echoes customer 6.disf - Disfluencies (multi-select) Code Description filled “uh”, “um”, etc. silent Si...

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    thank you

    polite - Politeness Code Description none No politeness cues (no please/thank you/etc.) minimal One-off courtesy (“thank you”, “please”) standard Expected level (“please let me know”, “thanks for waiting”) elevated Multiple markers + honorifics (“sir/madam”, “kindly”) impolite...

  34. [43]

    when you can

    urgency - Urgency Code Description none No urgency language low Mild timeframe hints (“when you can”, “at your convenience”) moderate Moderate urgency (“soon”, “shortly”) high Strong urgency (“ASAP”, “urgent”) critical Extreme immediacy (“immediately”, “right now”, “without de...

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    A set of numbered summary propositions

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    X: Speaker:

    A transcript segment containing turns, each starting with a turn number like"X: Speaker: ..." , where X is the turn number. Your Task: • For each turn, identify which summary propositions (by their original number) are semantically expressed in that turn. • A proposition match...

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    0": [0, 1],

    The grass looks dead. • Transcript: 0: Agent: Hi, I am Sarah. Beautiful blue sky today! 1: Customer: The grass looks dead. Example Output: { "0": [0, 1], "1": [2] } User Prompt: Map the following dialogue turns to the summary proposi- tions:\n<propositions>{summary_proposition...

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    sent - Sentiment Code Meaning very_pos Strongly positive tone pos Moderately positive tone neg Moderately negative tone very_neg Strongly negative tone info Information content or presence of factual tokens (dates, names, IDs) – high priority to this over neutral neutral Does ...

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    spk - Speaker agent, customer, misc

  40. [49]

    topic - Topic Category Code Description greet Greetings, introductions id_verif ID or account verification issue Customer’s reason for contact info_gath Agent probing/investigating prod_inq Product or service questions diag Diagnosis or troubleshooting soln Proposing a solutio...

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    Could you confirm your order?

    agent - Agent Action Code Category Notes ask_info Request Information “Could you confirm your order?” give_info Provide Information Facts or explanations not tied to a fix check_under Confirm Understanding “Do you see the change on your end?” rapport Build Rapport Empathy, fri...

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    sol - Solution Type (multi-select) Code Description diag_expl Diagnostic explanation advisory General advice root_cause Explaining root cause directive Concrete steps or commands preventive Prevent future issues escalate Escalation or transfer self_help Do-it-yourself instruct...

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    thank you

    polite - Politeness Code Description none No politeness cues (no please/thank you/etc.) minimal One-off courtesy (“thank you”, “please”) standard Expected level (“please let me know”, “thanks for waiting”) elevated Multiple markers + honorifics (“sir/madam”, “kindly”) impolite...

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    when you can

    urgency - Urgency Code Description none No urgency language low Mild timeframe hints (“when you can”, “at your convenience”) moderate Moderate urgency (“soon”, “shortly”) high Strong urgency (“ASAP”, “urgent”) critical Extreme immediacy (“immediately”, “right now”, “without de...

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    • Specifically Include: Positive sentiments expressed by the customer, especially those related to agreement or satisfaction with a solution

    Sentiment Balance: • Ensure both positive and negative sentiments are represented if they appear in the transcript. • Specifically Include: Positive sentiments expressed by the customer, especially those related to agreement or satisfaction with a solution

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    • Specifically Include: Key agent responses, clarifying questions, and de-escalation efforts

    Speaker Representation: • Provide a balanced representation of contributions from both the customer and the agent. • Specifically Include: Key agent responses, clarifying questions, and de-escalation efforts

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    • Specifically Include: Details from the Mid, Late, and Very Late segments of the conversa- tion, which often contain resolution steps and final agreements

    Positional Coverage: • Draw information equitably from all parts of the conversation. • Specifically Include: Details from the Mid, Late, and Very Late segments of the conversa- tion, which often contain resolution steps and final agreements

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    • Topics to Include: Information gathering/probing by agent, Call closure, ID verification, and Expression of empathy

    Topic and Activity Coverage: • Broaden the scope of topics and activities included in the summary. • Topics to Include: Information gathering/probing by agent, Call closure, ID verification, and Expression of empathy. • Agent Activities to Include: Rapport-building, Asking for...

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    • Repetition Types: Include all forms of significant repetition, such as: – customer repeating self, – agent repeating customer, and – customer repeating agent

    Solution and Repetition Types: • Solution Types: Ensure representation of directive solutions (concrete, actionable steps). • Repetition Types: Include all forms of significant repetition, such as: – customer repeating self, – agent repeating customer, and – customer repeating agent

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    • Turn Length: Represent information from both very long and very short conversational turns if they are relevant

    Linguistic and Structural Elements: • Disfluencies: Include meaningful interjections (oh!, hmm) and incomplete starts if they indicate hesitation or a change of thought. • Turn Length: Represent information from both very long and very short conversational turns if they are re...

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    • Specifically Include: Dates, Locations, Product/Case IDs, Monetary values, Times, Phone numbers, and Emails

    Factual and Emotional Fidelity: • Entity Representation: Include a wider range of entities beyond people and organizations. • Specifically Include: Dates, Locations, Product/Case IDs, Monetary values, Times, Phone numbers, and Emails. • Emotional Tone: Reflect the emotional st...

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    Journal of the Franklin Institute, 334(2):307–318

    The jensen-shannon divergence. Journal of the Franklin Institute, 334(2):307–318. Moin Nadeem, Anna Bethke, and Siva Reddy. 2021. StereoSet: Measuring stereotypical bias in pretrained language models. In Proceedings of the 59th Annual Meeting of the Association for Computation...

    2021 arXiv

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    Preprint, arXiv:2410.23609

    On positional bias of faithfulness for long-form summarization. Preprint, arXiv:2410.23609. Chao Wang, Neo Wu, Lin Ning, Jiaxing Wu, Luyang Liu, Jun Xie, Shawn O’Banion, and Bradley Green. 2024. Usersumbench: A benchmark framework for evaluat- ing user summarization approaches...

    2024 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.