{"id":"b6343513-ec6a-4ef2-8d42-e576e54b7081","arxiv_id":"2501.01332","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A sampling-based framework classifies LLM knowledge into six correctness-confidence categories and applies them to measure how chain-of-thought prompting, instruction tuning, and layer depth reshape model knowledge.","lead":"This paper proposes K-(CSA)², a six-category system for labeling what a large language model knows by combining whether answers are correct with how consistently the model repeats them across samples. The authors use it to trace how chain-of-thought prompting, instruction tuning, and model depth change the knowledge structure of seven LLM families.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The abstract's layer-wise knowledge claim rests on an undescribed probing procedure in Section 4.1.","rationale":"The reader's REJECT is correct. The weakest assumption identified in the reader's verdict is the sampling-based confidence proxy, which is indeed a real limitation and is even conceded in the paper's Limitation section. However, the single most load-bearing concern for the central claim as stated is the missing methodology for the layer-wise analysis. The abstract's second headline sentence depends entirely on Section 4.1 and Figure 8, yet no probing method is described. A reader cannot verify the numbers, and without them the claim that higher layers encode high-confidence knowledge while lower layers encode low-confidence knowledge is unsupported. This is more specific and more easily settled than the sampling-confidence issue: it requires either a described and reproducible procedure or a retraction of the claim. The sampling-confidence issue affects the framework broadly, but the layer-wise derivation is the weakest link for the abstract's most distinctive empirical assertion. I therefore agree with REJECT and do not propose a different verdict. The proposed test is a direct reproducibility check that would decide whether the concern lands.","tokens_in":18880,"tokens_out":6092,"duration_ms":62401,"concrete_test":"Ask the authors to provide the exact procedure and code used to compute the layer-wise ground-truth probabilities in Figure 8 (e.g., early exit at each transformer layer with the same decoding, or a hidden-state probing method). Then independently re-run the analysis for Llama-2-7b using that procedure. If the heatmap values cannot be reproduced, or if no procedure is specified, the layer-wise finding should be retracted or explicitly relabeled as a hypothesis rather than an empirical result.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's headline finding that 'higher layers in LLMs encode more high-confidence knowledge, while low-confidence knowledge tends to emerge in middle-to-lower layers' (Abstract) is supported only by Figure 8 and a single vague sentence: 'Ground truth probabilities are calculated by measuring the frequency of correct responses at each layer when the model was queried multiple times with the same input' (Section 4.1). No method is given for how a layer of an autoregressive transformer is 'queried'—whether via early exiting, a linear probe on hidden states, a logit lens, or some other readout—nor how category labels (assigned from full-model responses) are aligned with layer-level probabilities. Without this, the heatmap values cannot be checked, interpreted, or reproduced, making the abstract's central empirical claim a claim-without-derivation. This is distinct from the acknowledged sampling fragility of the six-category framework, but it is more immediately disqualifying for the specific finding advertised in the abstract.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces K-(CSA)², a framework that classifies LLM responses to knowledge-based QA into six categories (Highly Known, Maybe Known, Weakly Known, Unconfident Unknown, May Confident Unknown, Confident Unknown) using one greedy decode and six temperature-sampled decodes per question. Correctness is measured by exact match against ground truth; confidence is measured by the maximum frequency of an incorrect sampled response. The framework is applied to evaluate internal (parametric) and external (context-grounded) knowledge across seven LLMs, to compare base, instruction-tuned, and chain-of-thought variants, and to track category distributions over Pythia training checkpoints. The paper reports that stronger models are more assertive, that CoT produces synergistic gains with instruction tuning, and that layer-wise ground-truth probabilities are higher in upper layers for high-confidence knowledge and peak in middle-to-lower layers for low-confidence knowledge.","tokens_in":19025,"tokens_out":4899,"duration_ms":47620,"significance":"If the framework's measurement assumptions were validated, the six-category taxonomy would offer a practical way to evaluate LLM knowledge beyond binary accuracy, and the reported CoT/IT patterns would be informative for model alignment and evaluation. The paper has strengths: it uses a reproducible public benchmark (HaluEval), spans a range of open- and closed-source models, and explicitly separates correctness from confidence. However, the central layer-wise claim is not reproducible as written, the abstract's RLHF claim is not tested by the experiments, and the category definitions lack specified thresholds, so the framework's outputs and all findings built on them rest on ambiguous and under-validated measurements.","major_comments":[{"comment":"The layer-wise analysis is critically underspecified. The text states that \"Ground truth probabilities are calculated by measuring the frequency of correct responses at each layer when the model was queried multiple times with the same input,\" but it does not explain what it means to query a layer of a transformer: no early-exit strategy, probing head, logit-lens readout, or other mechanism is described. It also does not state how the category labels, which are assigned from full-model final responses, are aligned to layer-level probabilities, nor how many queries per layer were used. Without this information, the abstract's headline finding that \"higher layers in LLMs encode more high-confidence knowledge\" cannot be checked or reproduced, and the heatmap values in Figure 8 have no clear interpretation.","section":"Section 4.1, Figure 8"},{"comment":"The abstract claims that \"chain-of-thought prompting and reinforcement learning with human feedback fundamentally alter the knowledge structures\" of LLMs, but the experiments in Section 3 do not isolate or test RLHF. The comparisons are among base, instruction-tuned (IT), and CoT variants; instruction tuning is not equivalent to RLHF, and no RLHF-specific checkpoint or fine-tuning run is used anywhere in the reported evaluations. This claim should either be removed from the abstract and introduction or supported by an actual RLHF comparison.","section":"Abstract and Section 1 Introduction"},{"comment":"The boundaries among the six categories are not fully defined. For the unknown categories (UU, MU, CU), Appendix A.3 gives example values of PConfidence (1/5, 2/5, 5/5) but never states the threshold values that separate the categories. For the known categories, Table 3 uses informal quantifiers such as \"few correct sampling answers\" and \"not all\" without numerical criteria. Because every finding in Sections 3 and 4 depends on these category assignments, the missing thresholds make the entire framework non-reproducible and also prevent a sensitivity analysis. The Limitation section itself concedes that sample count and temperature affect categorization results, which further highlights the need for an explicit decision procedure and robustness checks.","section":"Section 2.1, Appendix A.3, Table 3"},{"comment":"Category ratios and their differences are reported without error bars, confidence intervals, or significance tests. With 3000 questions and only six sampled responses per question, many categories will have small counts, and the reported patterns (e.g., \"the ratio of 6.CU rises\" in Section 3.1, or \"4.UU+5.MU decreases\" in Section 4.2) may not be statistically stable. The paper should provide bootstrap confidence intervals or per-model variance estimates for the category ratios and for the transition ratios in Figures 4 and 5.","section":"Section 3, Figures 3, 7, 9, 11"},{"comment":"The Category Score assigns weights w_i = 7 - i to the six categories, from 6 for Highly Known to 1 for Confident Unknown, but the paper provides no justification for this linear weighting scheme. Since the Category Score is used as a headline evaluation metric (e.g., in Figure 2), the choice of weights directly affects the conclusions drawn from score differences. A sensitivity analysis over alternative weightings, or a principled justification, is needed before the score can support the paper's comparative claims.","section":"Section 2.2, Eq. (1)"}],"minor_comments":[{"comment":"There is a typo: \"For close-source models, T = 1\" should likely be \"For open-source models, T = 1,\" and the temperature settings for each model type should be stated more clearly, since the text currently says both \"T = 0.5\" for closed-source models and \"T = 1\" for close-source models in the same paragraph.","section":"Section 2.1"},{"comment":"The caption says models are \"sorted left to right by increasing combined accuracy (ratios of top-2 layers 1.HK + 2.MK)\"; this should be \"top-2 categories\" rather than \"top-2 layers.\"","section":"Figure 3 caption"},{"comment":"There is a typo: \"model's responses concentrated in unkonwn categories\" should be \"unknown categories.\"","section":"Appendix A.4"},{"comment":"The phrase \"accuracy is closely instructed with category scores\" appears to be a typo; it should likely read \"accuracy is closely correlated with category scores.\"","section":"Section 4.2, Finding 8"},{"comment":"Several figures (especially Figures 8, 13, and 14) have very small text and dense numerical labels that are difficult to read; larger fonts and a clearer color scale would improve interpretability.","section":"General"}],"recommendation":"major_revision","confidential_remarks":"The manuscript appears to be an early preprint with several under-specified methodological details, particularly the layer-wise probing procedure and the category threshold definitions. The authors' own Limitation section undercuts the reliability of the core measurement, so a revision that simply adds clarifications may not be sufficient; the authors should also provide a sensitivity analysis and either remove or test the RLHF claims. The novelty relative to Gekhman et al. (2024), which the paper explicitly builds on, should be clarified in the revision."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: the K-(CSA)² framework is a legitimate, modest extension of Gekhman et al.'s known/unknown categories, and the CoT/instruction-tuning results are plausible enough to be worth a look. But the paper as submitted has two load-bearing problems: the layer-wise analysis in Section 4.1 has no methodology, and the abstract claims RLHF effects that the experiments don't test.\n\nWhat's new: three unknown categories (UU, MU, CU), the confidence metric for incorrect answers, and the weighted Category Score. These are simple but not silly. The experiments span seven models and internal vs. external knowledge, and the finding that CoT helps base models more than instruction-tuned ones, and that IT+CoT is synergistic, is a genuine empirical observation. The limitation section is honest about sampling sensitivity.\n\nWhere it falls down. First, the layer-wise claim in the abstract is not supportable from the text. Section 4.1 says ground truth probabilities are 'the frequency of correct responses at each layer when the model was queried multiple times,' but never says how a layer is queried. Early exit? A probe on hidden states? Logit lens? No code, no method. The heatmaps in Figure 8 are simply unreproducible. This is not a minor gap; it's the paper's advertised finding. Second, the abstract says RLHF 'fundamentally alters' knowledge structures, but no RLHF experiment appears anywhere. Instruction tuning is not RLHF, and several of the 'instructed' models are chat models whose training mix is opaque. Third, the UU/MU/CU boundaries are underspecified. With six samples, PConfidence can be 1/6, 2/6, 3/6, ...; the paper only illustrates 1/6, 2/6, and 1.0. Where do 3/6, 4/6, 5/6 land? Same for 'few' in WK. The limitation acknowledges boundary ambiguity, so the authors know.\n\nThese are fixable. A revised version that spells out the layer-wise probing, adds a real RLHF comparison or drops the claim, and defines the boundary thresholds would make the framework usable. I wouldn't trust the layer-wise results as they stand, and the abstract overstates scope.\n\nWho is this for? People building evaluation toolkits for LLM knowledge might find the six-category labels handy, especially the unknown subcategories. It deserves a serious referee, not a desk reject: the core framework is clear and the empirical sweep is substantial. But my recommendation is reject in current form, with an invitation to resubmit after the methodology and claims are fixed.","headline":"A useful six-category evaluation framework, but the abstract's layer-wise and RLHF claims are unsupported as written.","tokens_in":19555,"tokens_out":3677,"would_cite":false,"duration_ms":35196,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Sampling test sorts LLM knowledge into six states, from well-known to confidently wrong.","keywords":["knowledge categorization","confidence estimation","chain-of-thought prompting","instruction tuning","layer-wise analysis","sampling consistency","HaluEval","knowledge representation"],"falsifier":"Run the same 3,000 HaluEval questions through the protocol with 6 versus 60 samples and with temperatures 0.2 versus 1.0, and count how often a knowledge point changes category; a large reassignment rate would show the confidence axis is an artifact of sampling settings rather than a stable property of the model's knowledge.","tokens_in":18655,"feed_emoji":"🧠","tokens_out":4392,"duration_ms":39147,"temperature":0.7,"pith_summary":"The paper introduces K-(CSA)^2, a framework that sorts what a large language model knows into six categories by asking two questions at once: is the answer right, and is the model sure? Correctness is checked by exact match against ground truth; confidence is measured by prompting the same question several times and seeing how often the same answer comes back. The categories run from Highly Known (correct and consistent) to Confident Unknown (wrong and consistent). The paper then uses the categories to show that chain-of-thought prompting and instruction tuning reshape knowledge structures in different ways for internal versus context-provided knowledge, and that high-confidence knowledge of either sign concentrates in upper model layers. The point of the framework is that accuracy alone cannot distinguish true knowledge from confidently held misconceptions, so a metric like Category Score, which weights the six categories, gives a different and richer picture of model comprehension.","feed_headline":"Sampling test sorts LLM knowledge into six states","feed_subtitle":"Correctness plus answer consistency reveals confident misconceptions and shows where knowledge lives in the model.","key_machinery":"The load-bearing object is the six-category mapping K-(CSA)^2 plus the confidence proxy that feeds it. Given a question, the model is queried seven times: once with greedy decoding ($T=0$) and six times with sampling. Correctness is exact match with the ground truth; confidence among wrong answers is $P_{\\text{Confidence}} = \\max_i (f_i/n)$, the largest fraction of identical responses. The two dimensions combine into the six categories of Table 3, and the categories in turn feed a Category Score (sum of six weights $6..1$ times category ratios) and transition ratios (upgrade, downgrade, stable) that track how a model's knowledge structure changes under training or prompting.","core_discovery":"The central claim is that a model's knowledge of a fact can be read off a small matrix of sampled responses: one greedy output plus six temperature-sampled outputs. If at least one output is correct, the knowledge point is 'known' and falls into Highly Known, Maybe Known, or Weakly Known depending on whether the greedy answer is correct and how much the sampled answers agree; if none is correct, it is 'unknown' and falls into Unconfident Unknown, May Confident Unknown, or Confident Unknown depending on how often the same wrong answer repeats. Confidence for wrong answers is the maximum frequency of any single answer among the samples. Applying this scheme across the HaluEval knowledge QA dataset, the paper reports that stronger models are more assertive in both correct and incorrect directions, that chain-of-thought prompting improves category structure especially on instruction-tuned models, that instruction tuning alone can initially hurt internal-knowledge scores, and that in layer-wise probes high-confidence categories (HK and CU) are encoded most strongly in upper layers while low-confidence categories (UU and MU) peak in middle-to-lower layers.","pith_inferences":["One direct extension the authors do not spell out: the same sampling consistency measure could be turned into a hallucination detector for open-book QA, since a knowledge point that falls in CU (confidently wrong) despite attached context is exactly a case of overriding evidence.","The framework's confidence axis is behavior-based only; pairing PConfidence with the model's own token log-probabilities or with semantic-entropy estimates on the same questions would test whether 'confidence' tracks anything beyond repetition.","Because the paper shows CU knowledge concentrates in upper layers, a testable next step is to ablate or steer upper-layer representations and ask whether CU knowledge is more easily shifted than HK knowledge, which would give a causal handle on misconceptions.","The category transition ratios could be used as a fine-tuning early-stopping signal: stop when downgrade ratio starts to rise, rather than waiting for accuracy to saturate."],"forward_implications":["Category Score provides an evaluation number that penalizes confidently wrong answers more than uncertain wrong ones, which accuracy cannot do.","The finding that CoT and instruction tuning are complementary suggests model developers should evaluate and tune internal knowledge and context understanding separately.","Layer-wise patterns imply that interventions to correct confident misconceptions may need to act on upper layers, while low-confidence knowledge can be worked on in middle layers.","Training-stage curves show accuracy rising while Category Score dips, so monitoring only accuracy can miss temporary degradations in knowledge structure.","The six-category scheme gives a direct way to compare base, instruction-tuned, and chain-of-thought versions of the same model on the same knowledge points."],"supporting_citations":[{"why":"Supplies the correctness-based definitions of Highly Known, Maybe Known, and Weakly Known that the framework extends with a confidence axis.","marker":"(Gekhman et al., 2024)"},{"why":"The HaluEval dataset provides the knowledge-based QA questions and ground-truth answers used in all experiments.","marker":"(Li et al., 2023)"},{"why":"Defines the chain-of-thought prompting technique whose effect on knowledge categories is the paper's central intervention study.","marker":"(Wei et al., 2022)"},{"why":"Defines the instruction tuning with human feedback that the paper compares against base and chain-of-thought conditions.","marker":"(Ouyang et al., 2022)"},{"why":"The Pythia suite supplies the pretraining checkpoints used to trace knowledge category transitions across training steps.","marker":"(Biderman et al., 2023)"},{"why":"Llama-2 models are one of the model families analyzed for knowledge category structure and layer-wise knowledge distribution.","marker":"(Touvron et al., 2023)"}],"fun_headline_variants":["Six-state framework maps LLM knowledge by correctness and confidence","Correctness and consistency reveal six LLM knowledge states","Sampling scores reveal six-way split of LLM knowledge","Six-category LLM knowledge map from greedy plus sampled answers","Confidence-correctness grid sorts LLM knowledge into six states"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole framework treats how often a model repeats the same answer across six temperature-sampled responses to an identical prompt as a measure of how confident the model is in that answer; if sampling variability reflects decoding randomness or question wording rather than knowledge confidence, every category and downstream finding built on it loses its footing.","fun_headline_variants_meta":{"raw":{"variants":["Six-state framework maps LLM knowledge by correctness and confidence","Correctness and consistency reveal six LLM knowledge states","Sampling scores reveal six-way split of LLM knowledge","Six-category LLM knowledge map from greedy plus sampled answers","Confidence-correctness grid sorts LLM knowledge into six states"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000909,"raw_usage":{"total_tokens":3886,"prompt_tokens":903,"completion_tokens":2983,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":519,"completion_tokens_details":{"reasoning_tokens":2900}},"tokens_in":519,"tokens_out":2983,"duration_ms":21047,"temperature":1.0,"reasoning_tokens":2900,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T22:29:21.728361+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the same 3,000 HaluEval questions through the protocol with 6 versus 60 samples and with temperatures 0.2 versus 1.0, and count how often a knowledge point changes category; a large reassignment rate would show the confidence axis is an artifact of sampling settings rather than a stable property of the model's knowledge.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the instruction tuning with human feedback that the paper compares against base and chain-of-thought conditions."}],"review_version":1}