pith. sign in

arxiv: 2605.06350 · v1 · submitted 2026-05-07 · 💻 cs.LG · cs.AI· cs.CL

Is Escalation Worth It? A Decision-Theoretic Characterization of LLM Cascades

Dylan Bouchard This is my paper

Pith reviewed 2026-05-08 12:51 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CL
keywords LLM cascadesmodel cascadescost-quality tradeoffdecision-theoretic frameworkthreshold deferralshadow pricesrouting policies
0
0 comments X

The pith

LLM cascades achieve their cost-quality frontier as the pointwise envelope of pairwise two-model thresholds, with performance limited by always paying for the cheap model first.

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

Model cascades route uncertain queries from a cheap LLM to an expensive one using confidence thresholds to manage deployment costs and accuracy. The paper builds a constrained optimization framework that maps out the full cost-quality frontier for any number of models. For two models the frontier is piecewise concave, with shadow prices linking budget and quality constraints. With more models the best deterministic cascade reduces to the envelope of all possible pairs, and longer fixed chains or optimized subsequences add no meaningful improvement. Tests across five benchmarks show a simple pre-generation router often beats cascades because it skips the cheap model's cost on queries sent straight to larger models.

Core claim

The paper develops a decision-theoretic framework grounded in constrained optimization and duality. For a two-model cascade it establishes piecewise concavity of the cost-quality frontier on decreasing-benefit regions of the confidence support, with reciprocal shadow prices linking budget- and quality-constrained formulations. Given a pool of k models the frontier achievable by deterministic two-model threshold cascades is the pointwise envelope over all pairwise cascades, with switching points where the optimal pair changes. For k-model cascades first-order conditions require a single shadow price that equalizes marginal quality-per-cost across stage boundaries. Empirical validation on five

What carries the argument

The pointwise envelope over all pairwise two-model threshold cascades, derived via constrained optimization and duality, that characterizes the achievable frontier for any model pool.

If this is right

  • The optimal deterministic cascade policy is always one of the pairwise threshold cascades, with the best pair selected per region.
  • Full fixed k-model chains lie strictly inside or on the pairwise envelope and therefore cannot improve the frontier.
  • Optimized subsequence cascades deliver no practically meaningful held-out gains beyond the pairwise envelope.
  • A pre-generation router that avoids running the cheap model on queries routed directly to larger models exceeds the best cascade on four of five datasets.
  • Cascade performance is limited primarily by the structural cost of always invoking the cheap model before any escalation decision rather than by a shortage of intermediate stages.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers could explore routers that choose the initial model without first paying for a cheap one, potentially shifting the frontier outward.
  • The shadow-price equalization condition suggests a natural way to set dynamic per-query pricing in multi-model serving systems.
  • The envelope result implies that exhaustive search over pairs is often enough; practitioners need not maintain long fixed chains.
  • If confidence scores are noisy, the framework predicts the frontier will flatten earlier, making early-exit routers even more attractive.

Load-bearing premise

Deterministic threshold-based deferral using confidence scores is sufficient to trace the relevant cost-quality frontier.

What would settle it

A held-out experiment in which a learned or stochastic deferral policy produces a cost-quality curve strictly above the pairwise envelope on any of the five benchmarks.

Figures

Figures reproduced from arXiv: 2605.06350 by Dylan Bouchard.

Figure 1
Figure 1. Figure 1: Optimal pair, full fixed chain, optimal subsequence, and diagnostic learned router across five view at source ↗
Figure 2
Figure 2. Figure 2: Escalation benefit mH(s) − mL(s) as a function of the cheap model’s confidence score sL, for one representative pair per dataset (the pair dominating the largest cost range on the envelope). Solid curves are medians across 50 random 50/50 splits; shaded bands are 10th–90th percentiles. The dotted line marks zero. Annotations report the expected-dominance fraction (dom) and decreasing￾benefit fraction (dec)… view at source ↗
Figure 3
Figure 3. Figure 3: Scorer choice ablation. Points show median pair-level normalized gain over a no-signal view at source ↗
Figure 4
Figure 4. Figure 4: Descriptive cost-quality Pareto frontiers per dataset. Gray curves are per-pair frontiers view at source ↗
Figure 5
Figure 5. Figure 5: Escalation benefit curves for all 28 pairs on MMLU. view at source ↗
Figure 6
Figure 6. Figure 6: Escalation benefit curves for all 28 pairs on TriviaQA. view at source ↗
Figure 7
Figure 7. Figure 7: Escalation benefit curves for all 28 pairs on MATH (levels 3–5). view at source ↗
Figure 8
Figure 8. Figure 8: Escalation benefit curves for all 28 pairs on SimpleQA. view at source ↗
Figure 9
Figure 9. Figure 9: Escalation benefit curves for all 21 pairs on LiveCodeBench (GPT-oss-20B excluded; see view at source ↗
Figure 10
Figure 10. Figure 10: Median quality gap view at source ↗
Figure 11
Figure 11. Figure 11: Median Pareto frontier (over 50 splits) for NSGA-II and random search versus the pairwise view at source ↗
read the original abstract

Model cascades, in which a cheap LLM defers to an expensive one on low-confidence queries, are widely used to navigate the cost-quality tradeoff at deployment. Existing approaches largely treat the deferral threshold as an empirical hyperparameter, with limited guidance on the geometry of the resulting cost-quality frontier over a model pool. We develop a decision-theoretic framework grounded in constrained optimization and duality. For a two-model cascade, we establish piecewise concavity of the cost-quality frontier on decreasing-benefit regions of the confidence support, with reciprocal shadow prices linking the budget- and quality-constrained formulations. Given a pool of $k$ models, we characterize the frontier achievable by deterministic two-model threshold cascades as the pointwise envelope over $\binom{k}{2}$ pairwise cascades, with switching points where the optimal pair changes. For $k$-model cascades, we derive first-order conditions in which a single shadow price equalizes marginal quality-per-cost across stage boundaries. We validate the framework on five benchmarks (MATH, MMLU, TriviaQA, SimpleQA, LiveCodeBench) across eight models from five providers. Within the deterministic threshold-cascade class, full fixed chains underperform the pairwise envelope, and optimized subsequence cascades do not deliver practically meaningful held-out gains over it. A lightweight pre-generation router exceeds the best cascade policy on four of five datasets, mainly because it avoids the cheap model's generation cost on queries sent directly to a larger model rather than because of a stronger routing signal. These results suggest that cascade performance is limited primarily by structural cost, since cascades pay the cheap model before any escalation decision, rather than by a shortage of intermediate stages.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper develops a decision-theoretic framework for LLM cascades grounded in constrained optimization and duality. For two-model cascades it establishes piecewise concavity of the cost-quality frontier on decreasing-benefit regions together with reciprocal shadow prices between budget- and quality-constrained formulations. For a pool of k models it shows that the frontier achievable by deterministic two-model threshold cascades is the pointwise envelope over all pairwise cascades, with switching points where the optimal pair changes, and derives first-order conditions that equalize marginal quality-per-cost across stage boundaries for k-model cascades. Empirically, on five benchmarks (MATH, MMLU, TriviaQA, SimpleQA, LiveCodeBench) with eight models, full fixed chains underperform the pairwise envelope, optimized subsequence cascades yield no practically meaningful held-out gains, and a lightweight pre-generation router outperforms the best cascade policy on four datasets primarily by skipping the cheap model's generation cost.

Significance. If the characterizations hold, the work supplies a precise geometric and duality-based description of cascade frontiers that directly explains why structural cost (mandatory invocation of the cheap model before any deferral decision) dominates performance limits more than the number of intermediate stages. The empirical patterns across multiple benchmarks and model providers lend concrete support to this structural-cost interpretation and suggest that future routing research should prioritize pre-generation decisions over multi-stage escalation. The explicit scoping to the deterministic threshold-cascade class and the use of standard optimization duality are strengths that make the theoretical results falsifiable and extensible.

major comments (2)
  1. [§4] §4 (multi-model characterization): the claim that the k-model frontier is exactly the pointwise envelope over pairwise cascades assumes that no three-or-more-model subsequence can improve upon the best pairwise envelope at any operating point; while the first-order conditions in §5 are consistent with this, the manuscript does not provide a formal proof that the envelope is globally optimal within the deterministic-threshold class, which is load-bearing for the conclusion that 'shortage of intermediate stages' is not the limiting factor.
  2. [Empirical results] Empirical section (results on subsequence cascades): the statement that optimized subsequence cascades 'do not deliver practically meaningful held-out gains' is central to the structural-cost claim, yet the manuscript reports only qualitative patterns without effect sizes, confidence intervals, or statistical tests on the held-out differences; this weakens the ability to rule out that small but consistent gains exist on some datasets.
minor comments (3)
  1. [§3] The abstract and §3 refer to 'decreasing-benefit regions of the confidence support' without a precise definition or example of how these regions are identified from the empirical confidence distribution.
  2. [Figures] Figure captions and axis labels for the cost-quality frontiers should explicitly state whether the plotted points are in-sample or held-out and whether thresholds were chosen by grid search or by the derived first-order conditions.
  3. [Empirical results] The pre-generation router is described as 'lightweight' but the manuscript does not report its training procedure, feature set, or computational overhead relative to the cascade policies.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. The recommendation for minor revision is appreciated, and we address each major comment below with proposed revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (multi-model characterization): the claim that the k-model frontier is exactly the pointwise envelope over pairwise cascades assumes that no three-or-more-model subsequence can improve upon the best pairwise envelope at any operating point; while the first-order conditions in §5 are consistent with this, the manuscript does not provide a formal proof that the envelope is globally optimal within the deterministic-threshold class, which is load-bearing for the conclusion that 'shortage of intermediate stages' is not the limiting factor.

    Authors: We thank the referee for this observation. Section 4 characterizes the frontier achievable by deterministic two-model threshold cascades as the pointwise envelope over all pairwise cascades, with switching points where the optimal pair changes. The first-order conditions in Section 5 for k-model cascades equalize marginal quality-per-cost across stage boundaries via a single shadow price. These conditions are consistent with the interpretation that optimal multi-stage policies effectively operate as sequences of pairwise segments. However, we agree that an explicit formal proof establishing global optimality of the pairwise envelope within the full deterministic-threshold class (i.e., that no three-or-more-model subsequence can strictly improve upon it at any operating point) is not provided. In the revised manuscript we will add a concise proof sketch in §4 or an appendix, showing that any k-stage cascade's (cost, quality) points are bounded above by the pairwise envelope using the established piecewise concavity and duality results for pairs. This will directly bolster the claim that shortage of intermediate stages is not the primary limitation. revision: yes

  2. Referee: [Empirical results] Empirical section (results on subsequence cascades): the statement that optimized subsequence cascades 'do not deliver practically meaningful held-out gains' is central to the structural-cost claim, yet the manuscript reports only qualitative patterns without effect sizes, confidence intervals, or statistical tests on the held-out differences; this weakens the ability to rule out that small but consistent gains exist on some datasets.

    Authors: We agree that the current empirical presentation of the subsequence-cascade results relies primarily on qualitative patterns across the five benchmarks. To strengthen this central claim, the revised manuscript will augment the empirical section with quantitative details: absolute and relative differences in accuracy and cost between optimized subsequence cascades and the pairwise envelope, bootstrap-derived 95% confidence intervals on the held-out sets, and results from paired statistical tests (e.g., Wilcoxon signed-rank or McNemar's test) to assess whether observed differences are statistically significant. These additions will allow readers to evaluate the practical magnitude of any gains more rigorously and will reinforce the structural-cost interpretation. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper derives its decision-theoretic results (piecewise concavity, reciprocal shadow prices, pointwise envelope over pairwise cascades, and first-order marginal conditions) directly from standard constrained optimization and duality applied to the deterministic threshold-cascade formulation. These characterizations are obtained prior to and independently of any data fitting; the empirical sections optimize thresholds per pair and validate on benchmarks but do not redefine or force the theoretical claims by construction. No self-citations are invoked as load-bearing uniqueness theorems, no ansatz is smuggled, and no fitted quantity is relabeled as a prediction. The structural-cost conclusion follows from the model structure (always invoking the cheap model first) rather than from circular reduction to the observed performance numbers.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework applies standard constrained optimization and duality to the cascade setting; the main added elements are the empirical fitting of per-pair thresholds on the listed benchmarks and the assumption that deterministic thresholds suffice to achieve the frontier.

free parameters (1)
  • deferral thresholds
    Optimized per model pair to trace the cost-quality frontier in the constrained problems and validation experiments.
axioms (1)
  • domain assumption LLM confidence scores are sufficiently calibrated to support meaningful deferral decisions
    Required for the threshold-based cascade model to produce the described frontier.

pith-pipeline@v0.9.0 · 5594 in / 1404 out tokens · 46609 ms · 2026-05-08T12:51:51.530529+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

115 extracted references · 115 canonical work pages

  1. [1]

    , title =

    Viola, Paul and Jones, Michael J. , title =. Int. J. Comput. Vision , month = may, pages =. 2004 , issue_date =. doi:10.1023/B:VISI.0000013087.49260.fb , abstract =

    work page doi:10.1023/b:visi.0000013087.49260.fb 2004

  2. [2]

    2024 , eprint=

    When Does Confidence-Based Cascade Deferral Suffice? , author=. 2024 , eprint=

    work page 2024

  3. [3]

    2024 , eprint=

    Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning , author=. 2024 , eprint=

    work page 2024

  4. [4]

    Kusner and Kilian Q

    Zhixiang (Eddie) Xu and Matt J. Kusner and Kilian Q. Weinberger and Minmin Chen and Olivier Chapelle , title =. Journal of Machine Learning Research , year =

    work page

  5. [5]

    Proceedings of the 37th International Conference on Machine Learning , pages =

    Consistent Estimators for Learning to Defer to an Expert , author =. Proceedings of the 37th International Conference on Machine Learning , pages =. 2020 , editor =

    work page 2020

  6. [6]

    and Pratap, A

    Deb, K. and Pratap, A. and Agarwal, S. and Meyarivan, T. , title =. Trans. Evol. Comp , month = apr, pages =. 2002 , issue_date =. doi:10.1109/4235.996017 , abstract =

    work page doi:10.1109/4235.996017 2002

  7. [7]

    2021 , eprint=

    Measuring Massive Multitask Language Understanding , author=. 2021 , eprint=

    work page 2021

  8. [8]

    2021 , eprint=

    Measuring Mathematical Problem Solving With the MATH Dataset , author=. 2021 , eprint=

    work page 2021

  9. [9]

    2017 , eprint=

    TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension , author=. 2017 , eprint=

    work page 2017

  10. [10]

    2024 , eprint=

    Measuring short-form factuality in large language models , author=. 2024 , eprint=

    work page 2024

  11. [11]

    2024 , eprint=

    Language Model Cascades: Token-level uncertainty and beyond , author=. 2024 , eprint=

    work page 2024

  12. [12]

    2023 , eprint=

    FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance , author=. 2023 , eprint=

    work page 2023

  13. [13]

    2025 , eprint=

    C3PO: Optimized Large Language Model Cascades with Probabilistic Cost Constraints for Reasoning , author=. 2025 , eprint=

    work page 2025

  14. [14]

    2025 , eprint=

    RouteLLM: Learning to Route LLMs with Preference Data , author=. 2025 , eprint=

    work page 2025

  15. [15]

    2026 , eprint=

    Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraints , author=. 2026 , eprint=

    work page 2026

  16. [16]

    2025 , eprint=

    OmniRouter: Budget and Performance Controllable Multi-LLM Routing , author=. 2025 , eprint=

    work page 2025

  17. [17]

    2026 , eprint=

    Knowledge Access Beats Model Size: Memory Augmented Routing for Persistent AI Agents , author=. 2026 , eprint=

    work page 2026

  18. [18]

    2026 , eprint=

    Pay for Hints, Not Answers: LLM Shepherding for Cost-Efficient Inference , author=. 2026 , eprint=

    work page 2026

  19. [19]

    2025 , eprint=

    Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning , author=. 2025 , eprint=

    work page 2025

  20. [20]

    2024 , eprint=

    Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning , author=. 2024 , eprint=

    work page 2024

  21. [21]

    2026 , eprint=

    Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey , author=. 2026 , eprint=

    work page 2026

  22. [22]

    2025 , eprint=

    A Unified Approach to Routing and Cascading for LLMs , author=. 2025 , eprint=

    work page 2025

  23. [23]

    2025 , eprint=

    BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute , author=. 2025 , eprint=

    work page 2025

  24. [24]

    2025 , eprint=

    When to Reason: Semantic Router for vLLM , author=. 2025 , eprint=

    work page 2025

  25. [25]

    2024 , eprint=

    EmbedLLM: Learning Compact Representations of Large Language Models , author=. 2024 , eprint=

    work page 2024

  26. [26]

    2025 , eprint=

    GraphRouter: A Graph-based Router for LLM Selections , author=. 2025 , eprint=

    work page 2025

  27. [27]

    2025 , eprint=

    CP-Router: An Uncertainty-Aware Router Between LLM and LRM , author=. 2025 , eprint=

    work page 2025

  28. [28]

    2025 , eprint=

    AutoMix: Automatically Mixing Language Models , author=. 2025 , eprint=

    work page 2025

  29. [29]

    Portfolio Selection , urldate =

    Harry Markowitz , journal =. Portfolio Selection , urldate =

    work page

  30. [30]

    Journal of Machine Learning Research , year =

    Dylan Bouchard and Mohit Singh Chauhan and David Skarbrevik and Ho-Kyeong Ra and Viren Bajaj and Zeya Ahmad , title =. Journal of Machine Learning Research , year =

    work page

  31. [31]

    2025 , eprint=

    Atomic Calibration of LLMs in Long-Form Generations , author=. 2025 , eprint=

    work page 2025

  32. [32]

    2026 , eprint=

    Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification , author=. 2026 , eprint=

    work page 2026

  33. [33]

    Scikit-learn: Machine Learning in Python , journal =

    Fabian Pedregosa and Ga. Scikit-learn: Machine Learning in Python , journal =. 2011 , volume =

    work page 2011

  34. [34]

    2024 , eprint=

    Calibration and Correctness of Language Models for Code , author=. 2024 , eprint=

    work page 2024

  35. [35]

    2025 , eprint=

    UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models , author=. 2025 , eprint=

    work page 2025

  36. [36]

    2025 , eprint=

    Reconsidering LLM Uncertainty Estimation Methods in the Wild , author=. 2025 , eprint=

    work page 2025

  37. [37]

    2025 , eprint=

    Assessing Correctness in LLM-Based Code Generation via Uncertainty Estimation , author=. 2025 , eprint=

    work page 2025

  38. [38]

    2020 , eprint=

    CodeBLEU: a Method for Automatic Evaluation of Code Synthesis , author=. 2020 , eprint=

    work page 2020

  39. [39]

    2023 , eprint=

    Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback , author=. 2023 , eprint=

    work page 2023

  40. [40]

    2025 , eprint=

    NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions , author=. 2025 , eprint=

    work page 2025

  41. [41]

    2019 , eprint=

    DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs , author=. 2019 , eprint=

    work page 2019

  42. [42]

    2023 , eprint=

    FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation , author=. 2023 , eprint=

    work page 2023

  43. [43]

    2018 , eprint=

    HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering , author=. 2018 , eprint=

    work page 2018

  44. [44]

    2026 , version =

    Open R1 , author =. 2026 , version =

    work page 2026

  45. [45]

    2024 , eprint=

    LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code , author=. 2024 , eprint=

    work page 2024

  46. [46]

    Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box,

    Dylan Bouchard and Mohit Singh Chauhan , journal=. Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box,. 2025 , url=

    work page 2025

  47. [47]

    2026 , eprint=

    Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study , author=. 2026 , eprint=

    work page 2026

  48. [48]

    2025 , eprint=

    EAGER: Entropy-Aware GEneRation for Adaptive Inference-Time Scaling , author=. 2025 , eprint=

    work page 2025

  49. [49]

    Benchmarking Uncertainty Quantification Methods for Large Language Models with LM -Polygraph

    Vashurin, Roman and Fadeeva, Ekaterina and Vazhentsev, Artem and Rvanova, Lyudmila and Vasilev, Daniil and Tsvigun, Akim and Petrakov, Sergey and Xing, Rui and Sadallah, Abdelrahman and Grishchenkov, Kirill and Panchenko, Alexander and Baldwin, Timothy and Nakov, Preslav and Panov, Maxim and Shelmanov, Artem , year=. Benchmarking Uncertainty Quantificatio...

    work page doi:10.1162/tacl_a_00737

  50. [50]

    2024 , eprint=

    RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science , author=. 2024 , eprint=

    work page 2024

  51. [51]

    2025 , eprint=

    Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency , author=. 2025 , eprint=

    work page 2025

  52. [52]

    Akiba, S

    Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori , booktitle =. doi:10.1145/3292500.3330701 , pages =

    work page doi:10.1145/3292500.3330701

  53. [53]

    OpenAI.com , author=

    work page

  54. [54]

    Google Cloud , author=

    work page

  55. [55]

    2025 , eprint=

    UQLM: A Python Package for Uncertainty Quantification in Large Language Models , author=. 2025 , eprint=

    work page 2025

  56. [56]

    OpenAI.com , author=

    Introducing GPT-4.5 | OpenAI , url=. OpenAI.com , author=

    work page

  57. [57]

    2024 , eprint=

    Graph-based Uncertainty Metrics for Long-form Language Model Outputs , author=. 2024 , eprint=

    work page 2024

  58. [58]

    Hughes, Simon and Bae, Minseok and Li, Miaoran , month = nov, title =

    work page

  59. [59]

    2024 , eprint=

    DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models , author=. 2024 , eprint=

    work page 2024

  60. [60]

    2024 , eprint=

    A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice , author=. 2024 , eprint=

    work page 2024

  61. [61]

    2024 , eprint=

    A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions , author=. 2024 , eprint=

    work page 2024

  62. [62]

    2023 , eprint=

    A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions , author=. 2023 , eprint=

    work page 2023

  63. [63]

    2024 , eprint=

    A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models , author=. 2024 , eprint=

    work page 2024

  64. [64]

    2025 , eprint=

    A Survey on LLM-as-a-Judge , author=. 2025 , eprint=

    work page 2025

  65. [65]

    2023 , eprint=

    Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness , author=. 2023 , eprint=

    work page 2023

  66. [66]

    Farquhar, J

    Farquhar, Sebastian and Kossen, Jannik and Kuhn, Lorenz and Gal, Yarin , title=. Nature , year=. doi:10.1038/s41586-024-07421-0 , url=

    work page doi:10.1038/s41586-024-07421-0

  67. [67]

    2024 , eprint=

    LUQ: Long-text Uncertainty Quantification for LLMs , author=. 2024 , eprint=

    work page 2024

  68. [68]

    2023 , eprint=

    SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models , author=. 2023 , eprint=

    work page 2023

  69. [69]

    2024 , eprint=

    Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs , author=. 2024 , eprint=

    work page 2024

  70. [70]

    2024 , eprint=

    Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models , author=. 2024 , eprint=

    work page 2024

  71. [71]

    2023 , eprint=

    Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation , author=. 2023 , eprint=

    work page 2023

  72. [72]

    2024 , eprint=

    Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space , author=. 2024 , eprint=

    work page 2024

  73. [73]

    2023 , eprint=

    Selectively Answering Ambiguous Questions , author=. 2023 , eprint=

    work page 2023

  74. [74]

    2024 , eprint=

    CLUE: Concept-Level Uncertainty Estimation for Large Language Models , author=. 2024 , eprint=

    work page 2024

  75. [75]

    ROUGE : A Package for Automatic Evaluation of Summaries

    Lin, Chin-Yew. ROUGE : A Package for Automatic Evaluation of Summaries. Text Summarization Branches Out. 2004

    work page 2004

  76. [76]

    Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , pages =

    Papineni, Kishore and Roukos, Salim and Ward, Todd and Zhu, Wei-Jing , title =. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics , pages =. 2002 , publisher =. doi:10.3115/1073083.1073135 , abstract =

    work page doi:10.3115/1073083.1073135 2002

  77. [77]

    , booktitle=

    Qurashi, Abdul Wahab and Holmes, Violeta and Johnson, Anju P. , booktitle=. Document Processing: Methods for Semantic Text Similarity Analysis , year=

    work page

  78. [78]

    METEOR : An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments

    Banerjee, Satanjeev and Lavie, Alon. METEOR : An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments. Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. 2005

    work page 2005

  79. [79]

    2019 , eprint=

    Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks , author=. 2019 , eprint=

    work page 2019

  80. [80]

    2020 , eprint=

    BERTScore: Evaluating Text Generation with BERT , author=. 2020 , eprint=

    work page 2020

Showing first 80 references.