Pith. sign in

REVIEW 2 major objections 1 minor 113 cited by

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

T0 review · 2 major / 1 minor · reviewed 2026-05-12 · grok-4.3

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

Pith's one-line read Semantic entropy, which groups model outputs by shared meaning before measuring uncertainty, predicts answer accuracy more reliably than token-level entropy on question answering tasks.

desk verdict Semantic entropy improves uncertainty estimates by grouping generations by meaning instead of wording, but the clustering step may undermine results when the model is already uncertain. read the letter →

arxiv 2302.09664 v3 pith:C3UQ73X5 submitted 2023-02-19 cs.CL cs.AIcs.LG

Lorenz Kuhn , Yarin Gal , Sebastian Farquhar This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords semanticentropyuncertaintyestimationnaturallanguagegenerationquestionansweringlargemodelsequivalencelinguisticinvariancemodelcalibration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proposes a way to quantify uncertainty in large language models when they generate natural language answers, such as to questions. Conventional entropy calculations treat every distinct sentence as unique, even when different wordings convey identical information, which distorts the uncertainty signal. To address this, the method first clusters generated sentences into semantic equivalence classes using the model itself without supervision, then computes entropy over the probabilities of these meaning-based clusters. Experiments across question answering datasets demonstrate that this semantic entropy correlates more strongly with whether the model’s answer is correct than several baseline uncertainty measures. The result matters because accurate uncertainty estimates let users know when to trust or disregard a model’s output in practical settings.

What carries the argument

Semantic entropy: entropy computed over clusters of semantically equivalent generations identified unsupervised by the model itself.

What would settle it

A dataset or experiment in which the unsupervised clustering places semantically distinct answers into the same class (or vice versa) and semantic entropy loses its advantage in predicting accuracy over baselines.

Watch

Extended reading notes

Core claim

The authors introduce semantic entropy as an entropy measure over semantic equivalence classes of generated sentences rather than over individual token sequences. Sentences are grouped into classes that share the same meaning through an unsupervised procedure that queries the language model itself; the entropy is then taken with respect to the total probability mass assigned to each class. This construction is invariant to linguistic rephrasings that preserve meaning and requires no model modifications, additional training data, or auxiliary models. Ablation studies on multiple question answering benchmarks show that semantic entropy is more predictive of model accuracy than comparable token

Load-bearing premise

Semantic equivalence classes among generated sentences can be reliably identified in an unsupervised manner using the language model itself.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The manuscript introduces semantic entropy, an uncertainty measure for natural language generation that incorporates linguistic invariances arising from semantic equivalence among different phrasings. The approach is unsupervised, relies on a single off-the-shelf language model without modifications, and is evaluated via ablation studies claiming superior predictive power for model accuracy on question-answering datasets relative to standard baselines.

Significance. If the central empirical claim holds after addressing the clustering validation, the work would offer a practical advance in uncertainty estimation for NLG by handling semantic equivalence without external supervision or model changes. The unsupervised single-model design is a notable strength that could facilitate broader adoption in reliability-critical applications.

major comments (2)
  1. [Ablation studies] The ablation studies' claim of superior predictive performance for semantic entropy depends on the reliability of the unsupervised semantic equivalence clustering step, yet no details are supplied on the exact prompting/embedding procedure used to form clusters or on any independent validation of cluster quality (e.g., human agreement rates stratified by model confidence level).
  2. [Method] Because equivalence judgments are obtained from the same language model whose uncertainty is being quantified, the clustering step risks producing unreliable or inconsistent partitions precisely when the model is uncertain about the answer; this directly affects the entropy calculation and could inflate the reported advantage over baselines.
minor comments (1)
  1. [Abstract] The abstract states empirical superiority on QA datasets but omits any mention of the statistical tests employed or controls for confounding factors such as generation length or sampling temperature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify important aspects of our work on semantic entropy. We address each major point below and will revise the manuscript to improve transparency and robustness.

read point-by-point responses

  1. Referee: [Ablation studies] The ablation studies' claim of superior predictive performance for semantic entropy depends on the reliability of the unsupervised semantic equivalence clustering step, yet no details are supplied on the exact prompting/embedding procedure used to form clusters or on any independent validation of cluster quality (e.g., human agreement rates stratified by model confidence level).

    Authors: We agree that greater detail on the clustering procedure is needed for reproducibility. In the revised manuscript, we will expand the methods section to fully specify the prompting strategy for equivalence judgments and the embedding approach used to form clusters. We will also add a human evaluation of cluster quality, reporting agreement rates and stratifying results by model confidence levels to directly validate this component of the method. revision: yes

  2. Referee: [Method] Because equivalence judgments are obtained from the same language model whose uncertainty is being quantified, the clustering step risks producing unreliable or inconsistent partitions precisely when the model is uncertain about the answer; this directly affects the entropy calculation and could inflate the reported advantage over baselines.

    Authors: This is a substantive methodological concern. Using the same model for equivalence judgments introduces a potential dependency that could affect cluster reliability in low-confidence regimes. We will add a dedicated discussion section in the revision addressing this limitation, including analysis of how the entropy measure behaves under varying confidence levels and why the observed performance gains are not solely attributable to this effect. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected in semantic entropy derivation

full rationale

The paper defines semantic entropy by extending standard entropy to group generations into semantic equivalence classes identified unsupervised via the same model. No equations or steps in the provided text reduce the final measure to a fitted parameter, self-referential definition, or load-bearing self-citation by construction. The method is explicitly described as model-agnostic and unsupervised without modifications, and ablation results are presented as empirical comparisons to baselines rather than forced outcomes. This satisfies the default expectation of a non-circular paper; the clustering step is a methodological choice whose quality is not shown to be tautological with the uncertainty output.

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

Abstract-only review yields no information on free parameters, axioms, or invented entities; the method is described at a high level without implementation specifics.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation." pith.science (2026). https://pith.science/paper/C3UQ73X5

@misc{pith2026230209664,
  author       = {Pith},
  title        = {Pith review of: Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C3UQ73X5}},
  note         = {Machine review of arXiv:2302.09664}
}
read the original abstract

We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence" -- different sentences can mean the same thing. To overcome these challenges we introduce semantic entropy -- an entropy which incorporates linguistic invariances created by shared meanings. Our method is unsupervised, uses only a single model, and requires no modifications to off-the-shelf language models. In comprehensive ablation studies we show that the semantic entropy is more predictive of model accuracy on question answering data sets than comparable baselines.

Discussion (0). Continue with ORCID to comment.

Forward citations

Showing 60 of 113 Pith papers that cite this

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. See all 113 Pith citations

  1. REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations

    cs.CL 2026-05 unverdicted novelty 8.0 of 10

    REALISTA optimizes continuous combinations of valid editing directions in latent space to produce realistic adversarial prompts that elicit hallucinations more effectively than prior methods, including on large reason...

  2. ACS: An interactive framework for conformal selection

    stat.ME 2025-07 accept novelty 8.0 of 10

    ACS provides finite-sample false discovery rate control for interactive, adaptive selection of promising candidates from unlabeled pools.

  3. CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

    cs.CL 2026-08 conditional novelty 7.0 of 10

    A single instruction-tuned LLM can recover much of the diversity lost during alignment by learning to emit special creativity tokens, without using a second model at inference.

  4. Routing Is Least Learnable Where It Is Most Valuable: Bounds on Representation Routing for Web Agents

    cs.CL 2026-08 accept novelty 7.0 of 10

    Per-task routing between text, image, and hybrid observations of a browser page does not currently beat one fixed choice, because the labels needed to learn routing exist only where the agent already succeeds; only a ...

  5. Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading

    cs.LG 2026-08 conditional novelty 7.0 of 10

    An error is invisible to an edit exactly when it commutes with the edit's answer transform, so the missed errors of any edit suite form its joint centralizer.

  6. PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs

    cs.CV 2026-08 conditional novelty 7.0 of 10

    PhyCheck is a 69,825-pair video QA benchmark that tests and improves Video-LLMs' ability to judge whether events obey physical laws, with fine-grained evidence questions and a context-sensitivity pilot.

  7. Calibrating Semantic Uncertainty from Observable Language-Model Probabilities

    stat.ME 2026-07 conditional novelty 7.0 of 10

    A prespecified semantic map plus held-out calibration can turn LLM token probabilities into calibrated posterior estimates over declared states, with bounded error and valid coverage in tested settings.

  8. SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

    cs.CL 2026-07 conditional novelty 7.0 of 10

    A DETR-style probe distills multi-sample claim uncertainty into single-pass span detection and continuous Mixture-of-Beta scores, outperforming baselines on a new 293K-span benchmark.

  9. An Empirical Study of Security Calibration in Large Language Models for Code

    cs.SE 2026-06 unverdicted novelty 7.0 of 10

    Empirical evaluation of three LLMs finds prevalent overconfidence in insecure code generation, with security calibration outperforming functional calibration but both degrading in repository-level settings.

  10. Delayed Verification Destabilizes Multi-Agent LLM Belief: Instability Thresholds and Optimal Corrector Placement

    cs.MA 2026-06 unverdicted novelty 7.0 of 10

    Models delayed verification in multi-agent LLMs as graph consensus, derives stability thresholds (inverse golden ratio for delay two) via grounded Laplacian, and gives a supermodular greedy rule for corrector placemen...

  11. SPOT-E: Test-Time Entropy Shaping with Visual Spotlights for Frozen VLMs

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    SPOT-E uses entropy shaping on answer predictions with low-entropy anchors to optimize visual spotlights at test time via GRPO for better VLM performance on evidence-intensive tasks.

  12. MortarBench: Evaluating Mortgage Loan Origination Agents

    cs.LG 2026-06 unverdicted novelty 7.0 of 10

    MortarBench benchmark shows LLMs achieve ≤77.1% accuracy on loan origination; CRIT calibration raises accuracy to 80.5% and reduces bias.

  13. ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies

    cs.RO 2026-06 unverdicted novelty 7.0 of 10

    ActProbe is an action-space detector that uses temporal consistency error and action chunk magnitude from policy outputs, mapped via LSTM-MLP, to predict failures earlier than baselines across policies and real-robot tasks.

  14. Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    Introduces object-level semantic uncertainty for VLM memory, the UQ-DAAAM refinement system, and probabilistic guarantees that selected high-quality views reduce uncertainty more effectively.

  15. Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning

    cs.CL 2026-06 unverdicted novelty 7.0 of 10

    Chunk-Level Guided Generation uses off-the-shelf large LLMs to score fixed-length chunks from small models via likelihoods, matching trained PRM performance on math benchmarks without reward-model training.

  16. Before and After Temperature: A Distributional View of Creative LLM Generation

    cs.CL 2026-05 unverdicted novelty 7.0 of 10

    A per-token feature from temperature-induced changes in LLM token distributions predicts within-prompt creativity rank at Spearman rho 0.918 vs LLM judges and 0.870 vs humans, outperforming perplexity, entropy, top-1 ...

  17. Not All Uncertainty Is Equal: How Uncertainty Granularity Shapes Human Verification in LLM-Assisted Decision Making

    cs.HC 2026-05 unverdicted novelty 7.0 of 10

    A between-subjects experiment (N=192) finds that token-level uncertainty increases agreement with LLM answers while relation-level uncertainty reduces external verification in medical decision tasks.

  18. Proper Scoring Rules for Agentic Uncertainty Quantification

    cs.AI 2026-05 unverdicted novelty 7.0 of 10

    Introduces Trajectory Proper Score (TPS) as a strictly proper family of trajectory-level scoring rules that elicits the complete prefix-conditioned success probability process.

  19. CaMo: Camera Motion Grounded Evaluation and Training for Vision-Language Models

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    Proposes Spatial Narrative Score (SNS) evaluation for VLMs' camera motion understanding and introduces CaMo model achieving consistent performance on SNS and direct QA.

  20. Inducing Artificial Uncertainty in Language Models

    cs.CL 2026-05 unverdicted novelty 7.0 of 10

    Inducing artificial uncertainty on trivial tasks allows training probes that achieve higher calibration on hard data than standard approaches while retaining performance on easy data.

  21. Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation

    cs.CL 2026-05 unverdicted novelty 7.0 of 10

    Evidence utility is defined as information gain on the model's output distribution, with ranking by gain on a latent helpfulness variable shown equivalent to answer-space utility under mild assumptions, enabling a tra...

  22. Grounded or Guessing? LVLM Confidence Estimation via Blind-Image Contrastive Ranking

    cs.CL 2026-05 unverdicted novelty 7.0 of 10

    BICR uses blind-image contrastive ranking on frozen LVLM hidden states to train a lightweight probe that penalizes confidence on blacked-out inputs, yielding top calibration and discrimination across five models and m...

  23. Active Testing of Large Language Models via Approximate Neyman Allocation

    cs.AI 2026-05 unverdicted novelty 7.0 of 10

    Proposes surrogate semantic entropy stratification followed by approximate Neyman allocation for active testing of LLMs on generative benchmarks, reporting up to 28% MSE reduction and 22.9% average budget savings vers...

  24. Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations

    cs.AI 2026-04 conditional novelty 7.0 of 10

    GROVE visualizes distributions of language model generations as overlapping paths through a text graph, with user studies showing that graph summaries aid structural judgments like diversity assessment while raw outpu...

  25. Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models

    cs.CL 2026-04 unverdicted novelty 7.0 of 10

    A new Latent Imagination Module uses cross-attention to predict latent visual embeddings from text, improving accuracy and calibration of vision-language models on text-only inputs.

  26. U-Lens: Supporting User Uncertainty Management in Long-Form LLM Responses

    cs.HC 2026-07 conditional novelty 6.5 of 10

    U-Lens organizes long-form LLM uncertainty into prioritized multi-granular targets with evaluative explanations and response guidance, improving limited-budget verification over a confidence-cue baseline.

  27. When Does Consensus Mean Correctness? Measuring the Agreement-Accuracy Coupling with Semantics-Preserving Re-Rendering

    cs.LG 2026-08 conditional novelty 6.0 of 10

    On three open-weight VLMs, agreement across semantically equivalent re-rendered charts beats resampling as a reliability signal, and fine-tuning on a model's own consensus labels cuts accuracy in all five replication runs.

  28. Epistemic Trustworthiness in Generative AI: A Normative Framework for Warranted Reliance in High-Stakes Workflows

    cs.AI 2026-08 conditional novelty 6.0 of 10

    Epistemic warranted reliance on generative AI requires, jointly and non-fungibly, epistemic humility, epistemic access, and resistance to epistemic injustice.

  29. Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRA

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Replacing fixed top-k routing in MoE-LoRA with router-confidence-based nucleus admission plus an expert-disagreement extension improves accuracy and OOD detection at matched average compute.

  30. Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    In two small VLMs, internal token probability detects errors with AUROC up to 0.99 while verbalized confidence stays near 0.9 and performs near chance, except under severe low light where both fail.

  31. Post-Training Shifts Confidence: A Three-Stage Analysis of How SFT, RL, and OPD Shape CoT Calibration

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Post-training changes where confidence is trustworthy in reasoning traces: OPD best gauges difficulty before thinking, SFT best supports early stopping, RL best selects final answers, and confidence should be read onl...

  32. Graded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal Steering

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Prompt-point activations carry a graded, steerable entity-familiarity signal that is robust to Polish/English stem changes and is stronger in Polish-adapted models than in base models.

  33. Confidently Wrong: Detecting Hallucinations in Financial Question Answering from LLM Internal States

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Among 8/8 self-consistent answers on FinQA, residual-stream probes detect wrong answers at 0.68–0.77 AUROC versus 0.55–0.63 for the best cheap output baselines across three 8–9B models.

  34. Reliability Scaling Laws for Quantized Large Language Models

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Reliability of quantized LLMs peaks nonlinearly at 4-bit precision under fixed total model bits, while accuracy scales monotonically, and quantization can improve robustness to natural perturbations.

  35. Hallucination Detection in Large Language Models Using Diversion Decoding

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Forcing an LLM away from its greedy answer yields resistance features that train a classifier detecting hallucinations more accurately and cheaply than semantic entropy.

  36. When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Self-consistency is a weak, regime-dependent proxy for correctness: positive but small correlations (rho 0.20–0.59), with the most self-consistent frontier model over-confident and wrong 48% of the time at high agreement.

  37. Does Bielik Know What It Doesn't Know? Activation Dispersion Separates Entity Familiarity from Factual Reliability Across Model Scale

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Unsupervised MLP activation dispersion separates known from fabricated entities at AUROC 0.95–1.00 across Bielik scales, while factual reliability scales separately and refusals stay near zero.

  38. Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A large-scale multilingual evaluation of LLM uncertainty estimation methods across 22 languages and 9 models finds that English reasoning closes the UE gap for low-resource languages and that optimal UE method choice ...

  39. Reliability Scales Inversely: Hallucinations Snowball Faster in Bigger Language Models

    cs.LG 2026-06 conditional novelty 6.0 of 10

    Hallucination in LLMs is driven by an oracle-invisible “decoding risk” term that grows with scale and causally compounds errors within a response.

  40. CALIBER: Calibrating Confidence Before and After Reasoning in Language Models

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    CALIBER elicits and supervises pre-reasoning confidence with prompt-level success probability and post-reasoning confidence with answer-level correctness, cutting ECE by 52.5% on BigMathDigits for a 7B model while rem...

  41. FlowEdit: Information-Theoretic Control of LLM Reasoning Flows for Ill-posed Problems Involving Conflicts

    cs.AI 2026-06 conditional novelty 6.0 of 10

    FlowEdit fine-tunes LLMs so they enumerate all self-consistent answers to conflicting-condition problems, improving exact-set-match accuracy by 68% on a synthetic benchmark.

  42. The Score Granularity Gap in Black-Box LLM Classification: A Comparative Study of Confidence Constructions

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    Comparative evaluation of seven confidence constructions across 25 LLM-dataset pairs reveals that verbalized scores provide good ranking but coarse granularity for thresholding, while multi-query aggregation helps wea...

  43. Right Knowledge, Wrong Answer: Characterizing Parametric Temporal Conflict in Open-Weight Language Models

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    Temporal Attractor Steering resolves 29-57% of parametric temporal conflicts in open-weight LLMs while preserving 85-99% accuracy on non-conflict queries.

  44. DeepLook: Deeper Thinking with Lookahead

    cs.AI 2026-06 conditional novelty 6.0 of 10

    A training-free decoder that branches only at low-confidence reasoning segments and ranks continuations by average lookahead confidence reports equal or better accuracy at roughly 1/8 of a confidence-filtered baseline...

  45. Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    Agent systems lose uncertainty at decision handoffs, causing downstream over-trust; the paper proposes latent uncertainty as a carrier to preserve pre-commitment fragility across interfaces.

  46. FASE: Fast Adaptive Semantic Entropy for Code Quality

    cs.SE 2026-06 unverdicted novelty 6.0 of 10

    FASE approximates functional correctness via MST on structural and semantic dissimilarity graphs, reporting 25% better Spearman correlation and 19% better ROCAUC than LLM-based semantic entropy at 0.3% runtime cost on...

  47. CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts

    cs.CL 2026-06 unverdicted novelty 6.0 of 10

    CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.

  48. Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    RLVR exhibits correct-set turnover where solved problems regress during training, and a periodic review mechanism exploiting a repair-window principle improves retention and performance over baselines.

  49. TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection

    cs.AI 2026-05 unverdicted novelty 6.0 of 10

    TriLens detects hallucinations via per-layer entropy trajectories of logit-lens readouts from three internal modules across LLMs and QA benchmarks.

  50. Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    Introduces functional equivalence methods and functional entropy to predict functional correctness of LLM-generated code via uncertainty quantification, outperforming NLI-based baselines in most tested settings.

  51. Reading Calibrated Uncertainty from Language Model Trajectories

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    Trajectory geometry of MLP write-vectors, summarized by 11 scale-invariant features in a sparse linear probe, beats MSP for selective abstention in LLM multiple-choice QA.

  52. BalanceRAG: Joint Risk Calibration for Cascaded Retrieval-Augmented Generation

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    BalanceRAG uses sequential graphical testing on a 2D lattice of threshold pairs to certify safe operating points that meet target risk levels in cascaded RAG while increasing coverage.

  53. LLMs as Implicit Imputers: Uncertainty Should Scale with Missing Information

    stat.ML 2026-05 unverdicted novelty 6.0 of 10

    Response entropy in LLMs rises with missing context on SQuAD while sampling-based confidence stays high, supporting the multiple imputation criterion and introducing a diagnostic for uncertainty reduction by context level.

  54. Uncertainty Quantification for LLM-based Code Generation

    cs.SE 2026-05 unverdicted novelty 6.0 of 10

    RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.

  55. Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning

    cs.CL 2026-04 unverdicted novelty 6.0 of 10

    LUCID detects hallucinations in LLM-KG reasoning by extracting node/edge features from attention and semantics then integrating them with KG structure in a GNN, achieving SOTA on nine new benchmark datasets versus 15 ...

  56. Ensemble-Based Uncertainty Estimation for Code Correctness Estimation

    cs.SE 2026-03 unverdicted novelty 6.0 of 10

    Ensemble Semantic Entropy improves correlation with code correctness over single-model methods and powers a cascading scaling system that cuts FLOPs by 64.9% while preserving performance on LiveCodeBench.

  57. ETS: Energy-Guided Test-Time Scaling for Training-Free RL Alignment

    cs.LG 2026-01 unverdicted novelty 6.0 of 10

    ETS performs training-free RL alignment for language models by energy-guided test-time scaling with Monte Carlo energy estimation and importance sampling acceleration.

  58. A Geometric Taxonomy of Hallucinations in LLMs

    cs.AI 2026-01 unverdicted novelty 6.0 of 10

    Embedding geometry on the unit hypersphere distinguishes detectable query-proximate unfaithfulness and confabulations from undetectable factual errors sharing vocabulary with correct answers.

  59. GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts

    cs.AI 2026-01 unverdicted novelty 6.0 of 10

    GlimpRouter uses the entropy of the first token in each reasoning step to decide whether to invoke a large model, yielding 10.7% higher accuracy and 25.9% lower latency than a standalone large model on AIME25.

  60. Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety

    cs.CL 2025-10 conditional novelty 6.0 of 10

    Explicitly instructing LLM agents to quit uncertain or risky tasks improves their safety scores by 0.39 on average in ToolEmu simulations, with a negligible average helpfulness decrease of 0.03.

See all 113 Pith citations

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · cited by 113 Pith papers (see all)

  1. [1]

    Language models are few-shot learners

    6 Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901,

    work page 1901

  2. [2]

    PaLM: Scaling Language Modeling with Pathways

    1 Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311,

    work page Pith review arXiv

  3. [3]

    Calibration of pre-trained transformers

    6 Shrey Desai and Greg Durrett. Calibration of pre-trained transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pp. 295– 302,

    work page 2020

  4. [4]

    Unsupervised quality estimation for neural machine translation

    7 10 Published as a conference paper at ICLR 2023 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Fr´ed´eric Blain, Francisco Guzm´an, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics , 8: 539–555,

    work page 2023

  5. [5]

    Uncertainty-aware ma- chine translation evaluation

    1, 2 Taisiya Glushkova, Chrysoula Zerva, Ricardo Rei, and Andr ´e FT Martins. Uncertainty-aware ma- chine translation evaluation. arXiv preprint arXiv:2109.06352,

    work page arXiv

  6. [6]

    DeBERTa: Decoding-enhanced BERT with Disentangled Attention

    6 Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654, 2020a. 5 Ruining He, Anirudh Ravula, Bhargav Kanagal, and Joshua Ainslie. Realformer: Transformer likes residual attention. arXiv preprint arXiv:2012.11747, 2020b. 6 Dan Hendrycks, Nicholas Carlini, Joh...

    work page Pith review arXiv 2006

  7. [7]

    Training Compute-Optimal Large Language Models

    1 Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Train- ing compute-optimal large language models. arXiv preprint arXiv:2203.15556,

    work page Pith review arXiv

  8. [8]

    Abstract meaning repre- sentation for paraphrase detection

    1 Fuad Issa, Marco Damonte, Shay B Cohen, Xiaohui Yan, and Yi Chang. Abstract meaning repre- sentation for paraphrase detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol- ume 1 (Long Papers), pp. 442–452,

    work page 2018

Show all 27 references
  1. [9]

    Deup: Direct epistemic uncertainty prediction

    6 Moksh Jain, Salem Lahlou, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, and Yoshua Bengio. Deup: Direct epistemic uncertainty prediction. arXiv preprint arXiv:2102.08501,

  2. [10]

    Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

    6 Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551,

    arXiv

  3. [11]

    Language mod- els (mostly) know what they know

    7 Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield Dodds, Nova DasSarma, Eli Tran-Johnson, et al. Language mod- els (mostly) know what they know. arXiv preprint arXiv:2207.05221,

    arXiv

  4. [12]

    Teaching models to express their uncertainty in words

    7 Stephanie Lin, Jacob Hilton, and Owain Evans. Teaching models to express their uncertainty in words. arXiv preprint arXiv:2205.14334, 2022a. 2, 3, 6 11 Published as a conference paper at ICLR 2023 Zi Lin, Jeremiah Zhe Liu, and Jingbo Shang. Towards collaborative neural-symbo...

    2023

  5. [13]

    Uncertainty estimation in autoregressive structured prediction

    3 Andrey Malinin and Mark Gales. Uncertainty estimation in autoregressive structured prediction. arXiv preprint arXiv:2002.07650,

    2002

  6. [14]

    Regression prior networks

    1, 2, 3, 4, 5, 7, 8 Andrey Malinin, Sergey Chervontsev, Ivan Provilkov, and Mark Gales. Regression prior networks. arXiv preprint arXiv:2006.11590,

    2006

  7. [15]

    Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness

    3 Sabrina J Mielke, Arthur Szlam, Y-Lan Boureau, and Emily Dinan. Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness. arXiv preprint arXiv:2012.14983,

    2012

  8. [16]

    Correcting length bias in neural machine translation

    6 Kenton Murray and David Chiang. Correcting length bias in neural machine translation. arXiv preprint arXiv:1808.10006,

  9. [17]

    Charformer: Fast character transformers via gradient-based subword tokenization

    3 Yi Tay, Vinh Q Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Si- mon Baumgartner, Cong Yu, and Donald Metzler. Charformer: Fast character transformers via gradient-based subword tokenization. arXiv preprint arXiv:2106.12672,

  10. [18]

    Entailment as few-shot learner

    6 Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma. Entailment as few-shot learner. arXiv preprint arXiv:2104.14690,

  11. [19]

    Bilateral multi-perspective matching for natural language sentences

    6 Zhiguo Wang, Wael Hamza, and Radu Florian. Bilateral multi-perspective matching for natural language sentences. arXiv preprint arXiv:1702.03814,

  12. [20]

    A broad-coverage challenge corpus for sentence understanding through inference

    6 Adina Williams, Nikita Nangia, and Samuel R Bowman. A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426,

  13. [21]

    Deep learning for answer sentence selection

    5 12 Published as a conference paper at ICLR 2023 Lei Yu, Karl Moritz Hermann, Phil Blunsom, and Stephen Pulman. Deep learning for answer sentence selection. arXiv preprint arXiv:1412.1632,

    2023

  14. [22]

    Opt: Open pre-trained transformer language models

    6 Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christo- pher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068,

    arXiv

  15. [23]

    Work with foundation mod- els implicitly focuses on lexical equivalence, which entails the others, but we usually care about semantic equivalence

    2, 7 13 Published as a conference paper at ICLR 2023 Table 3: Illustration of semantic, syntactic, and lexical equivalence. Work with foundation mod- els implicitly focuses on lexical equivalence, which entails the others, but we usually care about semantic equivalence. Equiva...

    2023

  16. [24]

    <g/>”,x, s(m))) ⊿ Does old sequence entail new one? right ← M(cat(x, s(m), “<g/>

    Lexically equivalent sequences use exactly the same symbols. They are always also semantically and syntactically equiv- alent (in a given context). Syntactically equivalent sentences have the same grammatical form. But they can have different meanings (not semantically equival...

    2023

  17. [25]

    Additionally, we investigate, why the semantic entropy underperforms the length-normalised entropy at high temperatures

    As in the main body of the paper, we measure diversity as the average lexical overlap of the answers in the answer set. Additionally, we investigate, why the semantic entropy underperforms the length-normalised entropy at high temperatures. To that end, we manually inspect and...

    2023

  18. [26]

    We find that on CoQA, we obtain accurate model results with zero-shot prompting

    We use the following prompts on CoQA and TriviaQA. We find that on CoQA, we obtain accurate model results with zero-shot prompting. While we have to use few-shot prompting to obtain accurate answers on closed-book TriviaQA. We use the following prompts for each of the settings:...

    2023

  19. [27]

    brainstormed answers

    except for the exact matching accuracy criterion which is too demanding because of the much larger variety of possible answers for this task. 17 Published as a conference paper at ICLR 2023 Table 7: CoQA: the exact choice of the accuracy metric for the free-form open-book QA t...

    2023

Pith tools

Reviewed May 12, 2026 · model on record in the stance chip above.