{"id":"da1b5bcc-b069-4087-a4c4-4a83f47fb19d","arxiv_id":"2501.08641","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"Chain-of-thought prompting barely changes sentiment analysis accuracy for large language models, and the models lean on in-context demonstrations rather than reasoning.","lead":"This paper tests whether adding step-by-step reasoning prompts (chain-of-thought) improves how large language models judge sentiment in reviews. It finds little benefit for bigger models and argues that sentiment understanding relies more on the examples in the prompt than on reasoning.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Accuracy claims rest on proxy labels built from the very models being evaluated, so the central 'minimal impact' conclusion is not grounded in task accuracy.","rationale":"The reader's weakest assumption identifies the same load-bearing issue: Section 4.1 openly admits the absence of ground-truth overall sentiment and constructs proxy labels by weighted majority voting over the models under evaluation. This is a circularity in the accuracy measurement, not merely a missing ablation. Because the abstract's first claim, 'chain-of-thought has a minimal impact on sentiment analysis tasks,' is grounded in the RQ-1 and RQ-2 accuracy figures, a failure of those labels to represent human overall sentiment would invalidate the headline empirical result. I do not see a more load-bearing concern. The counterfactual demonstration conclusion in RQ-4 is also under-quantified, since agreement between original and modified demonstrations is reported without a baseline or a flip-rate analysis, but the proxy-label issue is more fundamental because it affects the first and most prominent claim. I also flag Section 5's self-contradictory framing, which the reviewing rule requires me to mention, but that is a presentation problem rather than the core epistemic defect. The proposed human-annotation check is concrete, feasible with a few hundred examples, and would settle whether the proxy labels are responsible for the reported pattern. Since the reader's verdict is REJECT and this concern supports rejection without changing it, the verdict should remain UNCHANGED relative to the reader's assessment.","tokens_in":22037,"tokens_out":5418,"duration_ms":56416,"concrete_test":"Recompute the RQ-1/RQ-2 accuracy comparisons on a human-validated overall-sentiment gold set. Concretely, sample at least 100 selected reviews from each dataset and split, have at least two independent annotators label overall sentiment as positive, negative, or neutral, compute inter-annotator agreement, and then score every model's standard and CoT-v1 predictions against these human labels instead of the weighted-majority proxy. If the standard-vs-CoT accuracy deltas, for example Gemma2-2b 0.24 to 0.62, and the 'minimal impact' ordering survive with human labels, the concern is resolved. If the deltas shrink, vanish, or reverse, the abstract's first claim is not supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 4.1 states that Laptop and Restaurant have no ground-truth overall sentiment and that the authors 'implemented a post-hoc analysis using a weighted majority voting method to establish proxy ground-truths.' The weights come from each evaluated model's agreement with aspect-level labels, so the proxy overall-sentiment labels are a weighted consensus of the same models whose standard-vs-CoT performance is then measured. Reported accuracies such as Gemma2-2b 0.24 vs 0.62 are therefore agreements with a model-derived consensus, not task accuracy. If the proxy labels diverge from human overall sentiment, RQ-1 and RQ-2 cannot establish that CoT has 'minimal impact' on sentiment analysis; they only show that CoT moves predictions relative to an ensemble consensus. The manual MES dataset does not repair this because no human-annotation reliability or validation of the emotion labels is reported. Section 5 also contains an explicit contradiction: it says the findings support the independence of language and thought while 'the authors still align with Wittgenstein's perspective,' but this conceptual instability is secondary to the measurement validity problem in Section 4.1.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper examines whether chain-of-thought (CoT) prompting improves sentiment analysis in large language models, using this as an indirect test of whether semantic understanding depends on reasoning, which the authors equate with thought. Experiments compare a standard prompt with three CoT prompt variants across four models (Gemma-2 2B/9B/27B and LLaMA-3 8B), multiple shot counts, and three datasets: filtered subsets of SemEval Laptop/Restaurant data and a manually constructed multi-emotion-shift (MES) dataset. The paper reports accuracy, input-output token similarity, and perturbation experiments (word-order shuffling and counterfactual demonstrations). It concludes that CoT has minimal impact on sentiment analysis, that both standard and CoT prompts focus on aspect terms, and that model behavior depends primarily on demonstration information, thereby supporting the independence of language and thought.","tokens_in":22290,"tokens_out":4285,"duration_ms":43762,"significance":"If the central claims were established, the paper would contribute a useful empirical datapoint to the discussion of CoT in sentiment analysis and to the broader language-and-thought debate. Its strengths include testing three distinct CoT formats, using multiple model families and sizes, and attempting a new emotion-shift dataset. However, the primary quantitative evidence is currently compromised by a self-referential evaluation setup: the proxy ground-truth labels for overall sentiment are derived from weighted majority voting over the same models being evaluated. The MES dataset lacks any reported human validation. These problems undermine the paper's main conclusions as they stand, though they are potentially addressable through additional human annotation and re-analysis.","major_comments":[{"comment":"The accuracy results for RQ-1 and RQ-2 (e.g., Gemma2-2b implicit Laptop 0.24 vs 0.62) are computed against proxy ground-truth labels obtained by weighted majority voting over the models under test. Because the proxy labels are a function of the models' own outputs, these numbers measure agreement with a model-derived consensus rather than accuracy relative to human overall sentiment. The paper explicitly states that Laptop and Restaurant lack ground-truth overall sentiment, and the reported 'accuracy' is therefore not task accuracy. This invalidates the central 'minimal impact' claim as currently formulated. The authors should obtain human-annotated overall sentiment labels for their test samples (or an independently validated external gold standard) and report accuracy, Cohen's kappa, or a similar metric against that standard.","section":"Section 4.1, Figures 2 and 3"},{"comment":"The manually constructed multi-emotion-shift dataset consists of only 100 texts, and the paper reports no annotation procedure, no number of annotators, no inter-annotator agreement, and no validation of the emotion labels or shift counts. Consequently, the accuracy trends in Figure 4 (e.g., Gemma2-9b decreasing from 0.92 to 0.78 with more shifts) are not verifiable. Without such validation, the MES results cannot support the RQ-2 conclusion that the findings are 'consistent' with the Laptop and Restaurant results.","section":"Section 4.2, MES dataset"},{"comment":"The counterfactual demonstration test measures the sensitivity of predictions to reversed sentiment labels in the demonstrations, but it does not measure the relative contribution of pre-training knowledge versus demonstration information. The abstract's claim that 'the model's handling of sentiment tasks primarily depends on information from demonstrations' requires a quantitative comparison of the magnitude of demonstration effects against a measure of pre-training knowledge; the current experiment provides only the former. RQ-4's conclusion is therefore overstated and should be reframed as a demonstration-sensitivity result, not a decomposition of knowledge sources.","section":"Section 4.4 and abstract"},{"comment":"The discussion contains an explicit contradiction: it states that the findings 'challenge Wittgenstein's view that language limits the boundaries of thought' and 'support the independence of language and thought,' then immediately says 'the authors still align with Wittgenstein's perspective.' This unresolved inconsistency makes the philosophical conclusion unclear. The authors need to state their actual position and explain how the experimental evidence bears on it, rather than asserting both sides in consecutive sentences.","section":"Section 5"}],"minor_comments":[{"comment":"The text refers to 'SemEval-2014' Laptop and Restaurant datasets, but the cited reference [13] is SemEval-2016 Task 5; please correct the citation or the dataset version.","section":"Section 3.2"},{"comment":"Figure 2 contains twelve panels without individual panel labels; please add labels or subfigure numbering so that the reader can map the described results to the correct panel.","section":"Figure 2"},{"comment":"The caption mentions 'upper part' and 'lower part,' but the figure contains multiple panels per row; please clarify which panels correspond to word-order shuffling and which to sentiment reversal.","section":"Figure 7"},{"comment":"The similarity heatmaps in Figures 5 and 6 are visually dense, and the claims about 'explicit split demonstrates a stronger similarity' are not supported by a quantitative summary or statistical test; please add numerical aggregates.","section":"Section 4.3"},{"comment":"The MES dataset has only 100 samples, which is small even for exploratory analysis; please include this as an explicit limitation and report confidence intervals or error bars if possible.","section":"Section 4.2"},{"comment":"The prompt examples in Table 2 refer to colored highlights, but the manuscript is not in color; please replace colors with formatting that is visible in grayscale print.","section":"Table 2"}],"recommendation":"major_revision","confidential_remarks":"The manuscript reads as unfinished in several places: Figure 1 appears hand-drawn, Section 5 contradicts the abstract's stated position, and the reference list contains a dataset/citation mismatch. The more serious issue is the proxy ground-truth construction in Section 4.1; because the 'accuracy' numbers are computed against a weighted vote of the evaluated models, the central empirical claim is not currently grounded in external validity. This is fixable in principle by human annotation of overall sentiment for the test samples, but it is a substantial piece of additional work. The paper may be better suited to a workshop venue or a short paper after the measurements are redone. I would encourage the editor to seek a revised version before considering it for publication."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"This is a broad empirical sweep of CoT prompting for sentiment analysis: three CoT formats, four model sizes (2B to 27B), six shot counts, a new 100-text multi-emotion-shift dataset, plus word-order and counterfactual demonstration tests. The sweep itself is the main attraction. The counterfactual test, which flips sentiment labels in demonstrations and measures agreement between original and flipped outputs, is the most solid part. It shows that model decisions follow demonstration content, and that is an honest, non-circular result.\n\nThe problem is that the headline accuracy claims (RQ-1 and RQ-2) are not anchored to task accuracy. As the authors state in Section 4.1, Laptop and Restaurant have no ground-truth overall sentiment, so they construct proxy ground truths using weighted majority voting over the models being evaluated. The weights are each model's Cohen's Kappa with aspect-level labels; in practice this gives Gemma2-27B a weight of 0.73 and Gemma2-2B 0.01. The proxy labels are therefore essentially the large model's opinion. When the paper reports that CoT lifts Gemma2-2B accuracy from 0.24 to 0.62, that is agreement with a model-derived consensus, not correctness against any human judgment. The 'minimal impact for large models' finding is similarly self-referential. The MES dataset does not repair this because no human-annotation reliability is reported.\n\nThe philosophical framing is also outsized. The experiments compare prompting variants, not language and thought. Section 5 literally says the findings 'challenge Wittgenstein's view' while 'the authors still align with Wittgenstein's perspective'—that is a contradiction, and it signals the conceptual mapping is not coherent. Weaker issues: no error bars, no raw data or code shared, and the word-order result largely echoes [19] and [26].\n\nWho should read it: practitioners curious about whether CoT helps sentiment analysis will find the counterfactual and word-order results suggestive, but they should not trust the accuracy numbers as they stand. The paper deserves a serious referee because the question is real and the experimental design is broad; a revision that either obtains human overall-sentiment labels or reframes the accuracy claims as self-consistency would be worth seeing. As submitted, I would not accept it.","headline":"The accuracy claims at the center of this paper rest on proxy labels built from the same models being evaluated, so the 'minimal impact of CoT' conclusion isn't grounded—though the counterfactual demonstration analysis is worth a look.","tokens_in":22771,"tokens_out":2838,"would_cite":false,"duration_ms":27390,"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":"Chain-of-thought prompting barely changes sentiment analysis results in large language models.","keywords":["chain-of-thought prompting","sentiment analysis","aspect-based sentiment analysis","in-context learning","language and thought","counterfactual experiments","large language models","demonstration dependence"],"falsifier":"Re-score the Laptop and Restaurant test sets with human annotators for overall sentiment and rerun the standard-versus-CoT comparison; if CoT changes accuracy by more than a few points on those human labels for large models in 18-shot settings, the minimal-impact claim fails. Alternatively, if sentiment-reversed demonstrations leave predictions largely unchanged, the demonstration-dependence claim fails.","tokens_in":21862,"feed_emoji":"🧠","tokens_out":5956,"duration_ms":55640,"temperature":0.7,"pith_summary":"This paper is an experimental check on whether teaching a large language model to reason step by step, through chain-of-thought prompting, actually improves its understanding of sentiment. The paper's claim is that it does not: across three CoT prompt styles, four model sizes, two standard aspect-based sentiment datasets, and a new dataset designed with frequent emotion shifts, CoT yields at most a small benefit for the smallest model in one-shot settings and essentially no benefit for larger models. The paper also reports that the text the models produce is more similar to aspect terms than to sentiment words, and that flipping the sentiment labels in the few-shot demonstrations changes predictions more than shuffling the words in the input. If correct, the paper shows that sentiment analysis is not propelled by a distinct reasoning chain, and that the demonstrations carry the task signal.","feed_headline":"Chain-of-thought prompts barely move sentiment analysis accuracy","feed_subtitle":"The real signal comes from the few-shot demonstrations, not from step-by-step reasoning.","key_machinery":"The central object is the chain-of-thought prompt, instantiated in three forms to avoid prompt-specific bias: a natural-language version (CoT-v1), a hybrid natural-symbolic version (CoT-v2), and a purely symbolic polarity-sequence version (CoT-v3). The mechanism that carries the argument is a pair of perturbation comparisons measured by Cohen's kappa agreement between original and perturbed outputs: shuffling adjacent words in the input tests reliance on word order, and reversing the sentiment polarity labels inside the demonstrations tests reliance on in-context information. The difference in agreement under these two perturbations is what licenses the paper's conclusion that demonstration information, not the reasoning path, is the operative signal for sentiment classification.","core_discovery":"On the paper's own terms, the central discovery is that chain-of-thought has minimal impact on sentiment analysis tasks. The evidence includes a notable gain for the smallest model on one hard implicit split (accuracy rising from 0.24 to 0.62), but the gain shrinks as demonstrations increase and essentially disappears for the larger models on most settings. Input-output similarity analysis shows standard and CoT prompts alike align more strongly with aspect terms than with sentiment words, indicating CoT does not shift the model toward sentiment. Counterfactual tests then reverse the sentiment labels in demonstrations and swap adjacent input words: reversing demonstrations substantially changes predictions, while shuffling the input does not. The paper reads these results as supporting the independence of language and thought in this setting, because a purely semantic task does not need an explicit thought-like reasoning path.","pith_inferences":["Editorial inference: if demonstration dependence is as strong as the counterfactual results suggest, sentiment predictions can be steered by flipping labels in the prompt, which turns demonstration selection into a practical control knob for deployment.","Editorial inference: the similarity finding points to aspect detection rather than polarity judgment as the binding constraint in aspect-based sentiment analysis; a testable next step is whether interventions that make aspects salient improve accuracy more than reasoning prompts do.","Editorial inference: because the proxy gold labels come from weighted voting over the same models, the cleanest extension is a human-annotated overall-sentiment benchmark; without it, the paper's accuracy comparisons are partly measures of model self-agreement.","Editorial inference: the input-shuffling and demonstration-reversal design could be carried to other semantic tasks such as natural language inference or factual consistency to ask whether demonstration-driven behavior is specific to sentiment or a general property of in-context learning."],"forward_implications":["Adding chain-of-thought prompts to sentiment analysis is unlikely to improve accuracy for capable language models, so effort spent designing reasoning prompts for this task has low expected payoff.","The sentiment signal in few-shot setups comes mainly from the demonstrations, so curating or correcting the example labels is a more direct lever than changing the prompt's reasoning format.","One-shot CoT can help a small model on hard implicit sentiment, but the benefit fades as more demonstrations are supplied, so the apparent CoT effect is partly a small-model and low-shot phenomenon.","For the language-and-thought question, the result is a concrete data point: a semantic classification task does not require an explicit reasoning chain, which supports the independence side of the debate."],"supporting_citations":[{"why":"Defines chain-of-thought prompting, the intervention whose minimal effect is the paper's central claim.","marker":"[20]"},{"why":"Supplies the counterfactual demonstration-reversal method used in RQ-4.","marker":"[10]"},{"why":"Supplies the adjacent word-swap input perturbation used to test word-order dependence.","marker":"[24]"},{"why":"Provides the Laptop and Restaurant datasets used for the main accuracy comparisons.","marker":"[13]"},{"why":"Identifies emotionally charged adjectives as key sentiment elements, the baseline finding that the similarity analysis extends.","marker":"[25]"},{"why":"Earlier work claiming a significant role for reasoning in implicit sentiment analysis, which the paper re-examines.","marker":"[5]"}],"fun_headline_variants":["Chain-of-thought barely moves sentiment analysis","Sentiment analysis ignores chain-of-thought prompts","Demonstrations drive sentiment, not reasoning chains","Why chain-of-thought fails to boost sentiment accuracy"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The paper assumes that the proxy overall-sentiment labels for Laptop and Restaurant, built by weighted majority voting over the same four models being evaluated, faithfully represent human overall sentiment; if that proxy is wrong, the reported accuracy differences measure model self-agreement rather than task accuracy.","fun_headline_variants_meta":{"raw":{"variants":["Chain-of-thought barely moves sentiment analysis","Sentiment analysis ignores chain-of-thought prompts","Demonstrations drive sentiment, not reasoning chains","Why chain-of-thought fails to boost sentiment accuracy"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000123,"raw_usage":{"total_tokens":1053,"prompt_tokens":854,"completion_tokens":199,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":470,"completion_tokens_details":{"reasoning_tokens":140}},"tokens_in":470,"tokens_out":199,"duration_ms":2560,"temperature":1.0,"reasoning_tokens":140,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-10T20:20:41.001706+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-score the Laptop and Restaurant test sets with human annotators for overall sentiment and rerun the standard-versus-CoT comparison; if CoT changes accuracy by more than a few points on those human labels for large models in 18-shot settings, the minimal-impact claim fails. Alternatively, if sentiment-reversed demonstrations leave predictions largely unchanged, the demonstration-dependence claim fails.","supporting_citations":[{"cited_title":"In: Findings of the Association for Computational Linguistics: EMNLP 2023","cited_arxiv_id":null,"evidence_quote":"Supplies the counterfactual demonstration-reversal method used in RQ-4."},{"cited_title":"Expert Systems with Applications 241, 122700 (2024)","cited_arxiv_id":null,"evidence_quote":"Supplies the adjacent word-swap input perturbation used to test word-order dependence."},{"cited_title":"In: ProWorkshop on Semantic Evaluation (SemEval-2016)","cited_arxiv_id":null,"evidence_quote":"Provides the Laptop and Restaurant datasets used for the main accuracy comparisons."},{"cited_title":"In: The 61st Annual Meeting Of The Association For Computational Linguistics (2023)","cited_arxiv_id":null,"evidence_quote":"Earlier work claiming a significant role for reasoning in implicit sentiment analysis, which the paper re-examines."}],"review_version":1}