{"id":"e27bad24-91d1-4979-a929-2387814c3d86","arxiv_id":"2507.18802","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"Breaking LLM responses into linked, relevance-ranked claims improves human pairwise preference accuracy by roughly 5% versus a standard text interface in a crowdsourcing study, with larger gains for uncertain annotators.","lead":"This paper introduces DxHF, an interface that splits long AI responses into individual claims so human annotators can compare them more easily. In a 160-participant crowdsourcing study, DxHF improved agreement with existing preference labels by about 5%, especially for users who were less certain, at the cost of about 18 extra seconds per comparison.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 4.7% accuracy gain may stem from GPT-4-generated claims and highlighting that encode the HH-RLHF label, not from improved human comparison; no bias or fidelity check is reported.","rationale":"The reader's weakest assumption is the validity of HH-RLHF labels as ground truth. I regard that as secondary: for a preference-collection tool, agreement with a large-scale human preference dataset is a defensible operationalization of accuracy. The more internal threat is that DxHF is not a pure re-presentation; it is a machine-generated transformation of the text. The authors themselves flag the bias risk in Sec 8 and the fidelity risk in Sec 4.2, but do not test either. Their ablation keeps the decomposition constant and only removes linking/ranking, so it cannot isolate the effect of decomposition, and the simulation in Sec 5 uses GPT-4 both to decompose and to score claims, making it insensitive to this bias. The proposed probe is cheap, uses the already-filtered dataset, and directly tests whether the highlighted-claim signal encodes the labels. If it does, the 66% human accuracy is unsurprising, and the contribution should be reframed from 'decomposition improves human judgment' to 'LLM-guided highlighting improves agreement with prior labels.' That would still be useful for RLHF data collection, but the current claim would be too strong. The conditional verdict remains appropriate; no reason to move away from it.","tokens_in":18653,"tokens_out":5084,"duration_ms":58735,"concrete_test":"Run a label-predictability probe: for all 50 filtered HH-RLHF instances, apply the exact DxHF decomposition/ranking pipeline and determine whether the relative top-claim scores (e.g., mean GPT-4 helpfulness of claims above the 0.3 relevance threshold) recover the HH-RLHF label. If this machine-only probe matches the label as often as DxHF users did (66%), the user-study gains are explained by the LLM surfacing label-bearing claims rather than by improved human comparison; if the probe is at or near chance, cueing is not an obvious confound.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that DxHF improves human comparative feedback accuracy (Sec 6.2). In the user study, accuracy is agreement with HH-RLHF labels, and the DxHF arm is not merely a re-presentation of the original text: GPT-4 rewrites each sentence into atomic claims, a cross-encoder relevance score drives opacity and ordering, and embeddings/GPT-4 keywords link claims (Sec 4.2). The paper concedes that the model 'may occasionally add or modify words' and, in Sec 8, that DxHF 'might introduce its own bias... attributable to the LLM that is used to pick the highlighted points.' No fidelity or bias evaluation is reported. If the decomposition/ranking pipeline systematically surfaces claims whose aggregate score predicts the HH-RLHF label, then the 4.7% gain could reflect participants following machine-generated cues rather than making better independent judgments. The ablation (Sec 7) removes linking/ranking but not the decomposition itself and includes no no-highlight control, so it cannot separate attention cueing from decomposition benefits. Without this separation, the claim that decomposition improves human feedback quality is not established.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This paper proposes DxHF, a user interface for pairwise comparative feedback in LLM alignment. DxHF decomposes each long-form response into atomic claims via GPT-4, ranks claims by query relevance using a cross-encoder, links similar claims across responses with summary keywords, and visually encodes relevance with opacity and hover highlighting. The authors evaluate DxHF in three ways: a simulation with LLM-as-a-judge synthetic annotators over HH-RLHF data, a within-subjects crowdsourcing study with 160 Prolific participants on 10 tasks, and an ablation study with 36 participants comparing full DxHF with versions lacking linking or ranking. The user study reports 66.3% versus 61.6% accuracy relative to HH-RLHF labels (p=0.0293), with a larger 6.4% advantage for the 138 participants with lower certainty (p=0.0059), at a cost of 18 seconds of additional average feedback time. The central claim is that the decomposition principle improves the quality of human comparative feedback for LLM alignment.","tokens_in":18889,"tokens_out":7289,"duration_ms":74250,"significance":"If the central claim holds, the paper makes a useful HCI contribution to LLM alignment: it demonstrates that interface design can improve preference data quality, and the tool is open-sourced. The study has real strengths: a genuine crowdsourcing experiment with native speakers, counterbalanced interface order, a significant main effect and a significant subgroup effect for uncertain annotators, an ablation with a statistically tested usefulness rating, and an unusually candid discussion of the risk that the LLM-based pipeline introduces its own bias. However, the evidence as presented does not yet separate the effect of decomposition from machine-generated attention cueing, and the metric is agreement with crowd labels rather than an independent measure of feedback quality. The paper's value therefore depends on additional validity checks.","major_comments":[{"comment":"The main accuracy result treats HH-RLHF preference labels as ground truth. These labels are crowd preferences, not objective ground truth, so \"accuracy\" in Fig. 6 is agreement with the original crowd. Because the DxHF pipeline (GPT-4 decomposition, cross-encoder relevance scoring, keyword linking; Sec 4.2) may systematically surface cues that correlate with those labels, the 4.7% gain could reflect participants following machine-selected highlights rather than providing better independent judgments. The paper itself concedes in Sec 8 that DxHF \"might introduce its own bias... attributable to the LLM that is used to pick the highlighted points,\" and in Sec 4.2 it concedes the decomposition \"may occasionally add or modify words,\" yet no fidelity or bias check is reported. Please provide a no-highlight/no-decomposition control, an independent expert-label evaluation on a subset of items, or a per-task analysis showing that the improvement is not driven by items where the pipeline's aggregate claim scores predict the HH-RLHF label.","section":"Sec 6.1-6.2"},{"comment":"The ablation cannot attribute the accuracy gain to decomposition. The two ablated conditions (\"w/o linking\" and \"w/o ranking\") keep the decomposed claims; there is no condition that presents the original text with the same highlighting and linking, and no no-highlight control. In addition, \"w/o ranking\" removes only the color encoding, not the relevance-sorting interaction, so it is not a clean ablation of ranking. Accuracy differences (66.7%, 65.7%, 63.%) are reported without inferential statistics; only the usefulness ANOVA is tested. Please report statistical tests for accuracy and include a control that removes decomposition while retaining the visual cues, or the conclusion that decomposition itself improves feedback quality is not supported.","section":"Sec 7"},{"comment":"The simulation uses GPT-4 both to decompose the text and to judge helpfulness of full texts and claims, and it measures agreement with HH-RLHF labels. The advantage of the decomposition strategies in Fig. 5 could therefore be an artifact of the judge scoring decomposed claims more consistently with the HH-RLHF labels than full texts, rather than evidence about human comparison behavior. The statement in Sec 5.3 that the simulation \"theoretically confirms\" the advantage is overstated for an LLM simulation; please validate the simulated strategies against the user-study data or present the simulation only as a hypothesis-generating check.","section":"Sec 5"},{"comment":"The paper does not report how the 10 tasks were assigned to the two interface conditions. It states that the order of interfaces was counterbalanced, but not that task assignment was counterbalanced or randomized. If the same five tasks were always paired with one interface, task difficulty differences alone could produce the observed 4.7% gap. Please clarify the task-condition assignment and report per-task accuracy or a mixed-effects model with task as a random effect.","section":"Sec 6.1"}],"minor_comments":[{"comment":"The phrase \"highly relys\" should be \"highly rely.\"","section":"Sec 2.1"},{"comment":"The sentence \"The key challenge in achieving this goal is that is that comparing long text snippets\" contains a duplicated \"is that.\"","section":"Sec 3.2"},{"comment":"\"63.%\" appears to be missing a digit, and \"Partcipant\" should be \"Participant.\"","section":"Sec 7.2"},{"comment":"The sampling procedure says 50 instances are sampled, but the user study uses 10; please report how many were excluded by the domain-knowledge filter and how the final 10 tasks were chosen.","section":"Sec 5.2"},{"comment":"The number of participants excluded for responding in under 10 seconds is not reported; please state it and note whether the results are sensitive to this exclusion.","section":"Sec 6.1"},{"comment":"The claim that there are no existing studies on improving human feedback quality \"beyond simply comparing two texts\" is too strong given the cited MOCHA and LLM Comparator work; please qualify the statement.","section":"Sec 2.1"}],"recommendation":"major_revision","confidential_remarks":"This is a well-executed HCI paper with a real user study and a useful open-source tool, but the central claim rests on agreement-with-crowd labels and the ablation does not isolate decomposition. I recommended major revision because the needed controls and analyses are feasible within the paper's scope. I would not reject on the current evidence."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The paper introduces DxHF, an interface that decomposes long LLM responses into atomic claims, ranks them by relevance, and links similar claims across two responses. The main result: 160 crowdsourced participants choose the HH-RLHF-preferred response 4.7% more often with DxHF than with a plain text interface (66.3% vs 61.6%, p=0.0293), and the gain is 6.4% for the 138 participants who reported low certainty (p=0.0059). That is a small but real effect, and the study is honestly run: within-subjects, counterbalanced, open-sourced tool, and an ablation study.\n\nWhat is new is the combination of known techniques — claim decomposition from FActScore, relevance-based opacity from the text saliency literature, and similarity linking — into a single comparative interface for RLHF data collection. That specific artifact does not appear in the prior work they cite. The paper also gives credit where due: it cites Armstrong's decomposition principle, and it is transparent about the trade-offs, including the extra 18 seconds per comparison and the fact that the tool is not suited for holistic judgments.\n\nThe soft spots are real, and the stress-test concern lands. The accuracy metric is agreement with HH-RLHF labels, which is a standard but limited proxy: it measures agreement with the original crowd, not absolute feedback quality. More importantly, the decomposition pipeline is not neutral. GPT-4 generates the atomic claims and the relevance scores, and the same model may surface claims that correlate with the label. The paper itself concedes the model \"may occasionally add or modify words\" and that DxHF \"might introduce its own bias.\" There is no human fidelity check on the decomposition, and no bias check on whether the highlighted claims predict the HH-RLHF label independently of the participant. The ablation removes linking and ranking but never removes the decomposition itself, so the 4.7% gain could be partly due to participants following machine-generated cues rather than making better independent judgments. That is not a fatal flaw, but it is a gap that needs closing.\n\nMinor issues: only 10 filtered tasks were used, fast participants were excluded, and the ablation accuracy differences were not significance-tested. These are minor and addressable.\n\nWho is this for: HCI researchers building crowdsourcing interfaces for LLM alignment, and anyone who collects pairwise preference data. The paper deserves a serious referee. I would ask for a bias/fidelity check — a no-decomposition control with similar highlighting, or a human evaluation of whether the claims preserve the original text's meaning — before treating the 4.7% as evidence that decomposition itself helps. The core idea is plausible and the evaluation is close to convincing; it just needs one more control.","headline":"A genuinely useful interface study with a modest but real effect, undercut by a missing bias check on the LLM-generated claims.","tokens_in":19415,"tokens_out":2275,"would_cite":true,"duration_ms":25370,"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":"Decomposing LLM responses into atomic claims improves the accuracy of human preference feedback, especially for uncertain annotators.","keywords":["AI alignment","human feedback","RLHF","decomposition","atomic claims","user interface","crowdsourcing","LLM evaluation"],"falsifier":"A direct test would recruit an independent expert panel to label the same 50-100 comparison pairs, then measure whether DxHF's accuracy advantage over the baseline persists when the reference labels come from the experts rather than from HH-RLHF. A second test would replace GPT-4's decomposition with a randomized or naive sentence splitter; if the accuracy gain disappears, the effect is attributable to the decomposition fidelity itself rather than the interface layout.","tokens_in":18480,"feed_emoji":"🧩","tokens_out":6470,"duration_ms":57086,"temperature":0.7,"pith_summary":"The paper argues that the bottleneck in RLHF-style alignment is not the learning algorithm but the interface through which humans express preferences. Its central claim is that decomposing two long LLM responses into atomic claims—single-information sentences—and presenting them with relevance ranking and semantic links improves the accuracy of pairwise preference judgments. In a crowdsourced study of 160 participants, the DxHF interface reached 66.3% agreement with the reference labels versus 61.6% for a plain side-by-side baseline, a gain driven by annotators who reported low certainty. The cost is an average of 18 additional seconds per judgment. If the finding holds, better annotation interfaces are a direct lever on alignment quality.","feed_headline":"Splitting LLM answers into claims lifts feedback accuracy by 4.7%","feed_subtitle":"An interface that ranks and links atomic claims helps uncertain annotators pick the better response.","key_machinery":"The load-bearing mechanism is decomposition of long responses into atomic claims, following the idea that dividing a judgment into smaller sub-judgments improves accuracy. Each response is broken into claims by GPT-4, copying words from the original text; a Cross-Encoder scores each claim's relevance to the conversation query, and claims are linked across the two responses using cosine similarity of BERT embeddings, with a keyword label summarizing each linked pair. The DxHF interface displays the two claim lists side by side with relevance encoded as text opacity, hover highlighting to reveal linked claims, and accordion folding so annotators can toggle between full text and decomposition.","core_discovery":"On the paper's own terms, DxHF establishes that the decomposition principle improves human comparative feedback for LLM alignment. The user study reports 66.3% accuracy with DxHF versus 61.6% with a baseline that mirrors the standard ChatGPT-style comparison page (Wilcoxon signed-rank, p=0.0293). For the 138 participants who rated their certainty below 5, accuracy is 6.4 percentage points higher with DxHF (p=0.0059), matching the simulation prediction that decomposition helps most when judges are uncertain. A separate ablation study with 36 participants finds the full interface is rated most useful and scores highest accuracy, with ranking supporting attention and linking reducing comparison effort. The paper also acknowledges the design is suited to factual or task-oriented comparisons, not holistic judgments of coherence, tone, or style.","pith_inferences":["If the reported agreement with HH-RLHF labels reflects genuine judgment quality, reward models trained on DxHF-collected preferences should produce measurably different alignment outcomes than models trained on baseline preferences; this is a direct, testable consequence.","The method's ceiling is set by decomposition fidelity, so adding an automatic check that each claim is semantically entailed by its source sentence would separate the interface's contribution from the decomposer's.","The same decomposition-plus-linking display could be applied to other pairwise text judgments, such as comparing two news articles or judging whether an AI edit preserved a writer's intent, wherever the texts are long enough to overwhelm working memory.","For expert annotators who are already confident, the benefit may be small, meaning the interface is most valuable in high-throughput crowd pipelines with heterogeneous effort rather than in expert review."],"forward_implications":["Preference data for RLHF and DPO can be made more accurate by changing only the annotation display, without modifying the learning algorithm.","Low-certainty annotators, who are common on crowdsourcing platforms, receive the largest accuracy boost from decomposition; the paper reports 6.4% higher accuracy for this group.","The average 18-second increase in feedback time is an explicit speed-accuracy trade-off that practitioners can adjust by task complexity.","The ablation study indicates both design features earn their place: ranking focuses attention on key claims, and linking lowers the effort needed to compare across the two responses."],"supporting_citations":[{"why":"Provides the HH-RLHF dataset used as ground truth and the standard pairwise-comparison feedback workflow this paper improves.","marker":"[10]"},{"why":"Supplies the decomposition principle that motivates breaking comparison tasks into smaller sub-judgments.","marker":"[7]"},{"why":"Defines the standard RLHF annotation interface that DxHF extends and compares against.","marker":"[48]"},{"why":"Defines atomic claims, the decomposition unit DxHF uses to break down long-form responses.","marker":"[45]"},{"why":"Prior system decomposing LLM generations into atomic claims for evaluation; DxHF extends this decomposition approach to pairwise comparison.","marker":"[17]"},{"why":"Source of the opacity-based relevance encoding and the word-fidelity constraint used in the decomposition prompt.","marker":"[29]"},{"why":"Provides the Cross-Encoder architecture used to rank claims by contextual relevance to the conversation.","marker":"[51]"},{"why":"Provides the embeddings whose cosine similarity drives the linking of similar claims across the two responses.","marker":"[19]"},{"why":"Documents judgment bias in LLM evaluation, supporting the premise that higher-quality human feedback matters for alignment.","marker":"[16]"}],"fun_headline_variants":["Claim decomposition lifts LLM feedback accuracy 5% for uncertain judges","DxHF: Interactive claim breakdown improves human feedback for LLMs","Splitting responses into claims boosts annotator accuracy, but adds 18s","For uncertain raters, claim-based comparison beats paragraph-level review"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The evaluation treats the HH-RLHF crowd labels as ground truth for helpfulness, so the reported accuracy is agreement with those labels rather than an objective measure of better feedback, and it further assumes that GPT-4's decomposition preserves the original text's meaning.","fun_headline_variants_meta":{"raw":{"variants":["Claim decomposition lifts LLM feedback accuracy 5% for uncertain judges","DxHF: Interactive claim breakdown improves human feedback for LLMs","Splitting responses into claims boosts annotator accuracy, but adds 18s","For uncertain raters, claim-based comparison beats paragraph-level review"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000217,"raw_usage":{"total_tokens":1430,"prompt_tokens":934,"completion_tokens":496,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":550,"completion_tokens_details":{"reasoning_tokens":419}},"tokens_in":550,"tokens_out":496,"duration_ms":5153,"temperature":1.0,"reasoning_tokens":419,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T18:07:48.068720+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A direct test would recruit an independent expert panel to label the same 50-100 comparison pairs, then measure whether DxHF's accuracy advantage over the baseline persists when the reference labels come from the experts rather than from HH-RLHF. A second test would replace GPT-4's decomposition with a randomized or naive sentence splitter; if the accuracy gain disappears, the effect is attributable to the decomposition fidelity itself rather than the interface layout.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the decomposition principle that motivates breaking comparison tasks into smaller sub-judgments."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Source of the opacity-based relevance encoding and the word-fidelity constraint used in the decomposition prompt."}],"review_version":1}