{"id":"d75c706b-d4f8-4d72-af48-ae808f072eb1","arxiv_id":"2606.22792","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper introduces a four-source uncertainty taxonomy for LLMs and finds that consensus-based UQ methods outperform others while larger models show lower uncertainty estimates.","lead":"This paper proposes a granular taxonomy attributing LLM uncertainty to input-level, parameter-level, token-level, and decoding-process sources, categorizes UQ methods into four types, and empirically tests 21 methods across Qwen3, Llama 3.2, and DeepSeek-V3 on TriviaQA, GSM8K, and HumanEval. A smart generalist might read it to understand practical ways to measure and manage uncertainty when deploying LLMs in applications where reliability matters.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Four-source taxonomy lacks demonstrated non-overlap, so method categorization and 'consensus-based outperform' claim rest on unverified partition","rationale":"Reader's weakest assumption directly identifies the partition that must hold for the strongest_claim to be interpreted as a category comparison rather than a collection of individual method results. No other internal inconsistency is visible from the supplied abstract and claim wording.","tokens_in":1754,"tokens_out":331,"duration_ms":14442,"concrete_test":"Extract the exact definitions and method-to-source assignments from the taxonomy and categorization sections; for each of the 21 methods, test whether it can be reassigned to a second source under the given definitions; recompute the per-category win rates on TriviaQA/GSM8K after any reassignments; if the consensus-based advantage disappears or reverses for >3 methods, the category-level conclusion is taxonomy-dependent.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim—that Deg and EigV (consensus-based) consistently beat the other three categories—requires the taxonomy (input-level, parameter-level, token-level, decoding-process) to assign every UQ method to exactly one source without overlap or omission. If token-level and decoding-process sources share stochasticity mechanisms, or if a single-pass method can be re-interpreted as parameter-level, then the four-way grouping is arbitrary; observed performance gaps could reflect implementation details or benchmark artifacts rather than the taxonomy-derived category. The abstract states the taxonomy 'systematically attributes' uncertainty but supplies no formal disjointness argument or exhaustive mapping of the 21 methods.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper proposes a four-source taxonomy for uncertainty in LLMs (input-level, parameter-level, token-level, decoding-process sources) and correspondingly categorizes 21 UQ methods into Bayesian, ensemble, consensus-based, and single-pass approaches. It presents an evaluation framework and reports empirical results on Qwen3, Llama 3.2, and DeepSeek-V3 across TriviaQA, GSM8K, and HumanEval, claiming that (i) UQ effectiveness is sensitive to task type and generation settings, (ii) consensus-based methods (Deg, EigV) consistently outperform the other categories, and (iii) larger model scales correlate with lower uncertainty estimates, suggesting an empirical scaling law.","tokens_in":1898,"tokens_out":527,"duration_ms":21564,"significance":"If the taxonomy can be shown to provide a non-overlapping partition and the empirical comparisons are placed on a statistically sound footing, the work would supply a structured diagnostic lens for LLM uncertainty that could guide method selection and connect theoretical sources to practical UQ performance. The reported scaling observation would also be of interest if replicated.","major_comments":[{"comment":"Abstract and taxonomy section: the claim that the four sources 'systematically attribute' uncertainty without overlap is load-bearing for the subsequent categorization of the 21 methods and for the interpretation that consensus-based methods outperform because they target a distinct source; no formal disjointness argument, exhaustive mapping, or check for re-interpretability (e.g., a single-pass method also being parameter-level) is supplied.","section":"Abstract and taxonomy section"},{"comment":"Experimental results section: the statement that Deg and EigV 'consistently outperform' other approaches requires, at minimum, per-benchmark tables with means, standard deviations or error bars, and a statistical test across the three model families; the abstract supplies none of these, leaving open whether observed gaps are significant or artifacts of implementation details within each category.","section":"Experimental results section"}],"minor_comments":[{"comment":"Ensure every abbreviation (Deg, EigV, etc.) is defined on first use and that the exact assignment of each of the 21 methods to one of the four categories is tabulated for reproducibility.","section":"Notation and tables"},{"comment":"Clarify the precise generation settings (temperature, top-p, etc.) and the exact metrics used for each benchmark so that the sensitivity claim in (i) can be verified.","section":"Evaluation framework"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments, which help clarify the presentation of our taxonomy and strengthen the empirical claims. We address each major comment below.","responses":[{"response":"We agree that the manuscript does not supply a formal proof of disjointness or an exhaustive re-interpretability check. The taxonomy is motivated by the sequential stages of LLM generation (input encoding, parameter sampling during inference, per-token distribution, and decoding strategy), which we treat as primary attribution sources. While conceptual overlaps are possible in edge cases, the categorization of the 21 methods follows these primary attributions. We will add a dedicated subsection in the taxonomy section that discusses potential overlaps, provides an explicit mapping table, and acknowledges limitations in strict disjointness.","revision_made":"yes","referee_comment":"[Abstract and taxonomy section] Abstract and taxonomy section: the claim that the four sources 'systematically attribute' uncertainty without overlap is load-bearing for the subsequent categorization of the 21 methods and for the interpretation that consensus-based methods outperform because they target a distinct source; no formal disjointness argument, exhaustive mapping, or check for re-interpretability (e.g., a single-pass method also being parameter-level) is supplied."},{"response":"We acknowledge that the current presentation of results does not include the requested statistical rigor in the reported tables or abstract. The full experimental section contains per-benchmark scores, but we will revise it to include (i) expanded tables with means and standard deviations computed over multiple runs, (ii) error bars in figures, and (iii) paired statistical tests (e.g., Wilcoxon signed-rank) across the three model families to assess whether performance gaps are significant. These additions will be reflected in both the results section and a revised abstract.","revision_made":"yes","referee_comment":"[Experimental results section] Experimental results section: the statement that Deg and EigV 'consistently outperform' other approaches requires, at minimum, per-benchmark tables with means, standard deviations or error bars, and a statistical test across the three model families; the abstract supplies none of these, leaving open whether observed gaps are significant or artifacts of implementation details within each category."}],"tokens_in":1467,"tokens_out":473,"duration_ms":15637,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main things here are a four-way taxonomy splitting LLM uncertainty into input-level, parameter-level, token-level, and decoding-process sources, plus a side-by-side run of 21 UQ methods on Qwen3, Llama 3.2, and DeepSeek-V3 using TriviaQA, GSM8K, and HumanEval. They map methods to Bayesian, ensemble, consensus-based, and single-pass groups and report that consensus ones (Deg and EigV) do better while larger models show lower uncertainty.\n\nThe broad empirical sweep across models and tasks is the part that holds up. Testing that many methods in one place and noting sensitivity to task type and generation settings gives a practical snapshot that could help someone picking a method for deployment.\n\nThe soft spot is the taxonomy. The abstract claims it systematically attributes uncertainty but supplies no argument or mapping showing the four sources are non-overlapping or that every method fits exactly one bucket. If token-level and decoding-process share mechanisms, or if a method can be read two ways, the category rankings become arbitrary and the performance gaps could come from implementation details instead. The abstract also gives no error bars, exclusion rules, or statistical tests, so the \"consistently outperform\" statement is hard to evaluate.\n\nThis is for people working on reliable LLM applications who want a diagnostic checklist rather than pure theory. A reader who needs a recent empirical comparison of UQ techniques would get something usable from the tables even if the taxonomy needs tightening.\n\nIt deserves peer review. The scale of the experiments is large enough to be worth referee time, and the taxonomy idea is worth pressure-testing even if the current version has the overlap issue.","headline":"The four-source taxonomy and 21-method comparison are the actual contribution, but the 'consensus-based win' claim rests on an unshown disjoint partition.","tokens_in":2385,"tokens_out":416,"would_cite":false,"duration_ms":32233,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Consensus-based uncertainty quantification methods outperform other approaches for large language models, with larger models showing lower uncertainty.","keywords":["uncertainty quantification","large language models","stochasticity","consensus-based methods","scaling law","uncertainty taxonomy","LLM evaluation","model uncertainty"],"falsifier":"An observation that the uncertainty sources overlap significantly or that consensus-based methods fail to outperform on new tasks would falsify the main results.","tokens_in":2680,"feed_emoji":"📊","tokens_out":512,"duration_ms":32583,"temperature":0.7,"pith_summary":"The paper proposes a granular taxonomy that attributes LLM uncertainty to four sources: input-level, parameter-level, token-level, and decoding-process. It categorizes UQ methods into Bayesian, ensemble, consensus-based, and single-pass approaches based on this taxonomy. Empirical evaluations of 21 methods across multiple LLMs and benchmarks reveal that consensus-based methods consistently perform best and that uncertainty decreases with model scale. This provides a practical way to assess and manage uncertainty in LLM applications.","feed_headline":"Consensus methods outperform other UQ approaches for LLMs","feed_subtitle":"Experiments across model families show Deg and EigV lead while larger scales reduce uncertainty estimates.","key_machinery":"The four-source uncertainty taxonomy (input, parameter, token, decoding-process) that supports categorizing and evaluating UQ methods.","core_discovery":"The paper claims that its four-source uncertainty taxonomy allows for a systematic categorization of UQ methods, and that experiments demonstrate consensus-based methods outperform others while larger model scales correlate with lower uncertainty estimates, suggesting an empirical scaling law.","pith_inferences":["The taxonomy might enable targeted improvements in UQ by addressing specific sources separately.","The scaling observation could imply that uncertainty issues diminish naturally with model advancement.","Consensus methods may be preferred in applications where reliability is critical."],"forward_implications":["Effectiveness of UQ methods is sensitive to task types and generation settings.","Consensus-based methods like Deg and EigV consistently outperform other UQ approaches.","Larger model scales correlate with lower uncertainty estimates.","This indicates an empirical scaling law for LLM uncertainty."],"fun_headline_variants":["Consensus UQ methods outperform in LLM benchmarks","Larger LLMs yield lower uncertainty estimates","Four-level taxonomy maps LLM uncertainty sources","Deg and EigV excel among UQ techniques tested","Empirical scaling law ties model size to uncertainty"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The proposed four-source taxonomy systematically and non-overlappingly attributes uncertainty sources in LLM generation.","fun_headline_variants_meta":{"raw":{"variants":["Consensus UQ methods outperform in LLM benchmarks","Larger LLMs yield lower uncertainty estimates","Four-level taxonomy maps LLM uncertainty sources","Deg and EigV excel among UQ techniques tested","Empirical scaling law ties model size to uncertainty"]},"model":"grok-4.3","cost_usd":0.003904,"raw_usage":{"total_tokens":2008,"prompt_tokens":678,"num_sources_used":0,"completion_tokens":67,"cost_in_usd_ticks":39037000,"prompt_tokens_details":{"text_tokens":678,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1263,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":678,"tokens_out":67,"duration_ms":9311,"temperature":1.0,"reasoning_tokens":1263,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T09:02:51.537982+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An observation that the uncertainty sources overlap significantly or that consensus-based methods fail to outperform on new tasks would falsify the main results.","supporting_citations":[],"review_version":1}