{"id":"58edb8e9-f99b-4e98-8fd0-81096a331d90","arxiv_id":"2507.15114","paper_version":2,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"The paper argues that NLI models should detect and classify content-based ambiguity before inference, treating disagreement as signal rather than noise.","lead":"This position paper argues that annotator disagreement in natural language inference often reflects genuine ambiguity in the text, not just noise. It proposes an NLI pipeline that detects and classifies ambiguity before making entailment judgments, and introduces a unified taxonomy of ambiguity types.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The asserted 'process-independent' status of content-based ambiguity conflicts with the paper's own proposed detection cues, which rely on annotation patterns and labeled disambiguation data; without resolving this tension, the two-stage framework's advantage over distributional disagreement…","rationale":"The reader's weakest assumption centers on the prevalence and text-only detectability of content-based ambiguity. My concern is broader: the paper's Section 4 explicitly asserts process-independence and freedom from external supervision, yet Sections 5.1 and 6 propose methods that depend on annotation patterns, disambiguation-labeled datasets, explanation-augmented data, and LLM evaluators. That internal tension is load-bearing because the framework's first two stages—ambiguity detection and classification—are exactly what separate it from prior work on modeling opinion distributions. If those stages require ambiguity labels or disagreement statistics, the signal is not process-independent and the framework loses its practical novelty. I credit the paper for honestly stating in its Limitations section that empirical validation is future work and that no ambiguity-annotated datasets exist; these concessions are evidence of good-faith writing, not misconduct. Nevertheless, they mean the central claim is not empirically established. The proposed test would settle whether text-only ambiguity detection can actually stand alone. This does not change the reader's CONDITIONAL verdict: the framework is a plausible research direction, but its core premise needs empirical confirmation before the claimed advantages are treated as established.","tokens_in":9935,"tokens_out":3427,"duration_ms":40886,"concrete_test":"Take a random sample of high-disagreement ChaosNLI/MNLI pairs from Jiang and de Marneffe (2022); have linguists annotate each pair for ambiguity presence and type from the text alone, blinded to labels, guidelines, and disagreement statistics. Then compare (a) the prevalence of content-based ambiguity among disagreement cases, (b) agreement between text-only annotations and the Jiang/de Marneffe taxonomy labels, and (c) the performance of an ambiguity detector that sees only the premise and hypothesis (e.g., an LLM prompted with the unified taxonomy) against a disagreement-based baseline. If text-only detection does not reach useful accuracy or agrees poorly with taxonomy labels, the process-independence claim and the framework's first stage lack empirical support.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The load-bearing premise is that content-based ambiguity is detectable from the text pair alone. Section 4 claims it 'requires no external filtering or supervision' and is a 'process-independent signal'; this is what distinguishes the proposed two-stage pipeline from existing disagreement detectors. The paper does not support that premise. Section 4 cites Jiang and de Marneffe (2022) for the claim that content-based ambiguity is the most common disagreement source, but gives no prevalence figures. More seriously, the paper's own proposals contradict process-independence: Section 6 calls for using 'annotation patterns' as cues for ambiguity detection, Section 5.1 proposes leveraging datasets annotated with disambiguations and explanations and using LLMs as evaluators, and Section 3.1 notes the taxonomy is based on manually analyzed samples by linguistically trained annotators. If detecting ambiguity requires disagreement labels, guidelines, or ambiguity annotations, then it is not process-independent, and the framework reduces to existing disagreement detection plus a post-hoc labeling step. The Limitations section concedes that empirical validation is future work and that no ambiguity-labeled datasets exist, and Section 5.1 reports current models perform below human-level accuracy. The central causal claim—that ambiguity is a prevalent, separable, and reliably detectable driver of disagreement—is therefore asserted rather than demonstrated.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This position paper argues that annotation disagreement in NLI often reflects genuine interpretive variation, specifically content-based ambiguity in the premise or hypothesis, rather than mere noise. The authors propose a two-stage framework in which NLI systems first detect whether an input pair is ambiguous, then classify the ambiguity type into a unified taxonomy (Lexical, Syntactic, Semantic, Pragmatic), generate disambiguated versions, and only then perform inference. The paper reviews existing work on annotator distribution modeling and disagreement sources, synthesizes three prior ambiguity taxonomies into Figure 7, and lays out a research agenda for creating ambiguity-annotated datasets and developing detection/classification methods.","tokens_in":10220,"tokens_out":2909,"duration_ms":32049,"significance":"If the central claim is correct, the paper identifies a concrete gap in current NLI research: existing systems model disagreement distributions but do not explain why interpretations diverge. The proposed framework and unified taxonomy are a useful synthesis that could support more explainable, human-aligned NLI systems. The paper is honest about its limitations, explicitly stating that empirical validation remains future work. Its main value is as a position piece that organizes existing evidence and provides a structured agenda for ambiguity-aware NLI. However, the strength of the load-bearing claims—that content-based ambiguity is prevalent, separable, and reliably detectable without external supervision—currently exceeds the evidence provided.","major_comments":[{"comment":"The claim that content-based ambiguity is a 'process-independent signal' that 'requires no external filtering or supervision' is contradicted by the paper's own proposed detection methodology. Section 6 recommends using cues from 'annotation patterns,' and Section 5.1 proposes leveraging datasets annotated with disambiguations and explanations, and using LLMs as evaluators. Section 3.1 also notes that the taxonomy is based on manually analyzed samples by linguistically trained annotators. If detecting ambiguity requires disagreement labels, ambiguity annotations, or manual analysis, then the distinguishing advantage of the two-stage framework over existing disagreement-detection methods is not established. The authors should either weaken the process-independence claim or specify precisely what information is available to the ambiguity detection stage in the intended setting.","section":"Section 4 and Section 6"},{"comment":"The assertion that 'Jiang and de Marneffe (2022)'s findings indicate that the most common sources of disagreement fall under content-based ambiguity' is not supported by any prevalence figures in this paper. No quantitative data are provided to show that ambiguity is a major driver of disagreement relative to guideline underspecification or annotator behavior. Since the paper's central motivational premise is that ambiguity is prevalent enough to warrant a dedicated framework, the authors should report the relevant statistics from the cited work (e.g., the proportion of disagreement instances attributed to each source) or explicitly qualify the claim as an interpretation of that work.","section":"Section 4"},{"comment":"The paper concedes that no datasets exist that are annotated for ambiguity and that current models perform below human-level accuracy on ambiguity detection (Section 5.1, Limitations). As a position paper, this is an acceptable limitation, but the abstract and Section 6 make causal claims that go beyond this: e.g., that ambiguity detection and classification 'enable' more robust, explainable, and human-aligned NLI. The central claim—that ambiguity is a separable, reliably detectable driver of disagreement—is asserted rather than demonstrated. I recommend framing the framework explicitly as a hypothesis to be tested, and providing a more concrete falsifiable prediction, such as an expected performance improvement on ambiguity-aware benchmarks once such annotations exist.","section":"Section 5.1 and Limitations"}],"minor_comments":[{"comment":"Typo: 'disambigutations' should be 'disambiguations'.","section":"Section 5.1"},{"comment":"Typo: 'hybid' should be 'hybrid'.","section":"Limitations"},{"comment":"The dataset referred to as 'Ambient dataset' is more commonly known as 'AmbiEnt' (Liu et al., 2023); please standardize the name for reader searchability.","section":"Section 5.1"},{"comment":"The mapping from the source taxonomies to the four broad categories is not explained. For example, 'Coreferential' is placed under Pragmatic, and 'Presupposition' under Semantic, but the criteria for these assignments are not stated. Even a brief description of the organizing principle would improve the taxonomy's usability and reproducibility.","section":"Figure 7"},{"comment":"The paper mentions 'within-label variation' in Section 5 but does not define it until later; consider defining it at first use or adding a pointer.","section":"Section 2"}],"recommendation":"major_revision","confidential_remarks":"This is a competent and well-organized position paper. The main issue is not the lack of empirical results, which the authors acknowledge, but the internal tension between the process-independence claim and the proposed detection cues. The paper would be acceptable at a journal that publishes position papers, provided the authors revise the claims to match the evidence and clarify the framework's practical assumptions. The taxonomy synthesis is useful but needs a clearer derivation to be genuinely novel."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this is a genuinely useful position paper, not a disguised empirical claim. The contribution is the unified taxonomy in Figure 7 and the explicit detect-classify-disambiguate-infer framework. That synthesis is new and worth having, even though each component comes from prior work. The literature review is accurate, the writing is clear, and the Limitations section is honest: validation is explicitly deferred.\n\nThe main soft spot is the 'process-independent' claim for content-based ambiguity. Section 4 asserts that ambiguity requires no external filtering or supervision and is the only disagreement source computable from the text alone. But the paper's own proposals cut against that. Section 6 suggests using annotation patterns as cues, Section 5.1 proposes leveraging datasets with disambiguations/explanations and using LLMs as evaluators, and the taxonomy in Section 3.1 rests on manual analysis by linguistically trained annotators. If detecting ambiguity needs disagreement labels or hand-built resources, the framework collapses into disagreement detection plus a post-hoc labeling step, and the claimed advantage over distributional modeling disappears. The paper also cites Jiang and de Marneffe for the prevalence claim without giving a single figure.\n\nNone of this makes the paper bad. It is a position paper and it says as much. The tension is real but repairable: the authors need to say whether ambiguity detection is supposed to be annotation-free or annotation-assisted, and if the latter, why that still beats just modeling the disagreement distribution. A referee should push on exactly that. I would not block revision over it, but it is the difference between a strong position paper and a merely programmatic one.\n\nThe citation pattern is fine; the one self-citation is peripheral and relevant. No equations or data, so no circularity issues.\n\nWho gets value: anyone working on NLI disagreement, perspectivist NLP, or annotation variation. The taxonomy alone is worth a cite. I'd send it to a serious referee, with the request to strengthen the section on the dependency of detection on annotations. It deserves to be published as a position paper, not desk-rejected.","headline":"A genuinely useful position paper with a valuable unified taxonomy and framework, but the central 'process-independent' claim is in tension with its own proposed detection methods and remains empirically untested.","tokens_in":10695,"tokens_out":2258,"would_cite":true,"duration_ms":25551,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Annotation disagreement in NLI is not noise: the paper argues it often reflects ambiguity in the premise or hypothesis, and that inference systems should detect and classify that ambiguity before judging entailment.","keywords":["natural language inference","annotation disagreement","ambiguity detection","ambiguity classification","label variation","perspectivism","textual entailment","ambiguity taxonomy"],"falsifier":"Take a sample of high-disagreement premise-hypothesis pairs, have linguistically trained annotators independently mark whether disagreement traces to ambiguity in the text and which subtype, then check whether content-based ambiguity accounts for most cases and whether automated detectors can recover those labels from the text alone; if most high-disagreement pairs are judged free of content-based ambiguity, or if the ambiguity types cannot be identified without extra context, the paper's central claim fails.","tokens_in":9728,"feed_emoji":"🧩","tokens_out":6394,"duration_ms":64687,"temperature":0.7,"pith_summary":"This position paper argues that annotator disagreement in Natural Language Inference is not mostly random noise but a meaningful signal, especially when it comes from ambiguity in the premise or hypothesis itself. It proposes reordering the NLI pipeline: first detect whether an input pair is ambiguous, then classify the type of ambiguity, then generate disambiguated readings, and only then perform inference. The paper synthesizes existing taxonomies into four broad ambiguity categories and argues that content-based ambiguity is the only disagreement source that can be addressed directly from the text, without needing annotation guidelines or annotator metadata. If the argument holds, NLI systems would move from picking a single majority label toward representing multiple coexisting human interpretations.","feed_headline":"Flag ambiguity before judging entailment","feed_subtitle":"Divergent NLI labels often trace to ambiguous text, not noise, so systems should detect ambiguity first.","key_machinery":"The load-bearing machinery is the four-stage pipeline in Figure 1: ambiguity detection, ambiguity classification, disambiguation generation, and inference classification, with linguistic and background knowledge informing each stage. The second main component is the unified ambiguity taxonomy, which organizes subtypes such as Lexical, Scopal, Presupposition, Implicature, and Imperfections under four broad categories. This taxonomy carries the argument by giving detection and classification a common target language, making the proposed shift from label-distribution modeling to ambiguity-aware inference concrete enough to operationalize.","core_discovery":"The central claim is that content-based ambiguity in the premise or hypothesis is a root cause of many reproducible human label differences in NLI, distinct from unclear guidelines and annotator behavior. Because this ambiguity lives in the language itself, it is a process-independent signal: detecting it does not require knowing which annotator responded how or which instructions they saw. The paper concludes that ambiguity detection and classification should be explicit first stages of NLI modeling, followed by generating disambiguated versions and then inferring entailment for each interpretation. It also introduces a unified taxonomy grouping ambiguity into Lexical, Syntactic, Semantic, and Pragmatic types, and argues that current systems that only model annotator label distributions miss the reasons why interpretations diverge.","pith_inferences":["The paper leaves open how to obtain training data for the detection stage; one testable extension is to use annotator agreement statistics as noisy supervision and check whether the resulting ambiguity labels match expert-annotated subtypes.","Because a single pair can exhibit several ambiguity types at once, the unified taxonomy likely needs to be treated as multi-label in evaluation, a case the paper does not discuss.","If the framework works, ambiguous pairs become a diagnostic tool: models that can hold multiple readings simultaneously would pass tests that single-label benchmarks cannot express.","The taxonomy's fuzzy boundaries between types such as Lexical and Syntactic ambiguity may make classification harder than the four broad categories suggest, so subtype-level inter-annotator agreement would be a natural early experiment."],"forward_implications":["If ambiguity is detected before inference, majority-vote preprocessing becomes unnecessary for ambiguous pairs and can be replaced by multiple interpretable labels, each tied to a distinct reading.","A unified taxonomy of ambiguity types gives detection methods a shared vocabulary across datasets, making it possible to compare systems on whether they identify the same kinds of ambiguity.","Fact-verification pipelines that use NLI could flag claims whose wording is ambiguous or intentionally misleading instead of silently committing to one interpretation.","New datasets annotated for ambiguity presence and type become a necessary prerequisite, reshaping how NLI benchmarks are constructed and evaluated.","Explanations of model predictions could name a specific ambiguity type, giving a more direct account of why a premise-hypothesis pair admits multiple judgments."],"supporting_citations":[{"why":"Supplies the Triangle of Reference framework separating ambiguity, guideline underspecification, and annotator behavior as sources of disagreement.","marker":"Aroyo and Welty (2015)"},{"why":"Establishes that human inference disagreements are inherent and reproducible rather than random noise, a premise the paper builds on.","marker":"Pavlick and Kwiatkowski (2019)"},{"why":"Provides the fine-grained taxonomy of NLI disagreement sources and the finding that content-based ambiguity is the most common source.","marker":"Jiang and de Marneffe (2022)"},{"why":"Contributes the Ambient ambiguous-pair dataset and evidence that language models detect ambiguity below human level, motivating the framework.","marker":"Liu et al. (2023)"},{"why":"Refines ambiguity types such as Type/Token and Collective/Distributive and is used to build the paper's unified four-category taxonomy.","marker":"Li et al. (2024)"},{"why":"Shows that some label variation is annotation error, supporting the paper's claim that not all disagreement is meaningful and that ambiguity is the more reliable signal.","marker":"Weber-Genzel et al. (2024)"}],"fun_headline_variants":["Detect ambiguity first, then judge entailment","NLI disagreements often stem from ambiguity","Ambiguity-aware NLI: a new framework","From disagreement to understanding via ambiguity","Why NLI models should detect ambiguity first"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The argument assumes ambiguity in the wording itself is common enough to explain a meaningful share of NLI disagreement and can be detected and classified from the premise-hypothesis text alone, without needing guideline information or annotator metadata.","fun_headline_variants_meta":{"raw":{"variants":["Detect ambiguity first, then judge entailment","NLI disagreements often stem from ambiguity","Ambiguity-aware NLI: a new framework","From disagreement to understanding via ambiguity","Why NLI models should detect ambiguity first"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000206,"raw_usage":{"total_tokens":1344,"prompt_tokens":843,"completion_tokens":501,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":459,"completion_tokens_details":{"reasoning_tokens":435}},"tokens_in":459,"tokens_out":501,"duration_ms":5024,"temperature":1.0,"reasoning_tokens":435,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-06T15:39:59.447699+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take a sample of high-disagreement premise-hypothesis pairs, have linguistically trained annotators independently mark whether disagreement traces to ambiguity in the text and which subtype, then check whether content-based ambiguity accounts for most cases and whether automated detectors can recover those labels from the text alone; if most high-disagreement pairs are judged free of content-based ambiguity, or if the ambiguity types cannot be identified without extra context, the paper's central claim fails.","supporting_citations":[],"review_version":1}