{"id":"ff86f570-9ff7-4f19-86d4-1f67f6b9996b","arxiv_id":"2412.16615","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"A cross-encoder LLM prompted with a binary relevance question and scored by next-token probabilities outperforms similarity-based retrievers on hidden rationale retrieval tasks.","lead":"The authors study 'hidden rationale retrieval', where the matching document is not similar to the query but is linked by reasoning, such as picking an empathy strategy for a user's distress message. They show a 7B instruction-tuned language model using a true/false prompt and token-probability scoring outperforms standard retrievers on four strategy-retrieval datasets.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Equation (2) defines relevance as a ratio of log probabilities; taken literally, this ranking is inverted, so the paper's stated scoring rule cannot be what produced the reported results unless an exp() was omitted.","rationale":"The reader's weakest assumption (single-label ground truth) is plausible, but all compared methods share the same labels, so label ambiguity mainly adds symmetric noise; it does not single out LaHoRe's advantage. A more immediate threat to the central claim is Eq. (2): the method's score is not a well-defined relative probability. Because the binary prompt encourages P(<T>) + P(<F>) = 1, the ratio of the two log probabilities is a decreasing function of P(<T>). The reported zero-shot and fine-tuned results are therefore inconsistent with the method as written. This is an internal mathematical issue rather than a matter of external consensus, and it can be settled by checking the released implementation. If the code uses a softmax over log probabilities, the central idea survives and the paper needs only a corrected equation; if not, the empirical results are unexplained. I therefore keep the reader's CONDITIONAL verdict: the concern is load-bearing but checkable, and the paper's release makes a direct test feasible.","tokens_in":9549,"tokens_out":5800,"duration_ms":51511,"concrete_test":"Pull the released code at https://github.com/flyfree5/LaHoRe and locate the scoring function for s_rel. If the implementation computes exp(s_T) / (exp(s_T) + exp(s_F)) or directly uses probabilities, then Eq. (2) is a typo and the central method survives, but the equation must be corrected. If the implementation literally computes log p_T / (log p_T + log p_F), rerun the zero-shot ESconv evaluation: the metric should approximately invert the ranking in Table 2; if it does, the paper's stated scoring cannot account for the reported results. An independent minimal check: for p_T = 0.9, Eq. (2) yields 0.045 relative score, while the intended softmax over log probabilities yields 0.9; the repository's code should match the latter.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing concern is the scoring rule in Eq. (2). The paper defines s(A) = log P(A|input) and then s_rel = s(<T>) / (s(<T>) + s(<F>)). When the binary-choice prompt constrains P(<T>) + P(<F>) = 1, this ratio is not a monotone function of P(<T>); it is in fact decreasing in P(<T>) for p in (0,1). For example, p_T = 0.6 gives s_rel ≈ 0.36, while p_T = 0.2 gives s_rel ≈ 0.88 under Eq. (2). Thus, taken literally, the method ranks documents with a lower probability of '<T>' as more relevant, which would invert the zero-shot results in Table 2. Either Eq. (2) omits an exp() (the intended softmax over log probabilities), or the reported results were produced by a different scoring rule than the one specified. Since every zero-shot and fine-tuned result depends on this score, the central claim cannot be reproduced from the text alone. This is an internal inconsistency, not a disagreement with consensus.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces LaHoRe, a cross-encoder LLM retriever designed for \"hidden rationale retrieval,\" where query and document are not semantically similar but related by reasoning, logic, or empirical knowledge. The method appends a binary-choice instruction (\"Can Q be appropriately responded with D?\") to the query--document pair, converts retrieval into a next-token generation problem, and scores relevance from the relative next-token probabilities of the two choice tokens. The authors evaluate zero-shot and SFT/DPO fine-tuned versions on four datasets constructed from ESconv, PsyQA, and a proprietary Daily Chat corpus, reporting that LaHoRe outperforms existing bi-encoder and cross-encoder baselines on Recall and p-MRR. They also propose a query/document reordering that enables KV-cache reuse for faster inference, and report an end-to-end RAG win-rate of 90% over raw LLM generation.","tokens_in":9766,"tokens_out":4582,"duration_ms":41821,"significance":"If the empirical claims hold, the paper makes a meaningful contribution by broadening retrieval beyond semantic similarity, demonstrating that a purely generative loss can serve as an alternative to contrastive learning for relevance scoring, and providing a practical inference-speed optimization via prompt caching. The release of code, models, and datasets is a concrete strength, and the evaluation design is not circular: zero-shot results are obtained with an unmodified instruction-tuned Qwen2 and externally defined strategy/intent labels. However, the significance is conditional on resolving a formal inconsistency in the scoring rule, clarifying the evaluation protocol with respect to validation and seed variance, and specifying the DPO data construction.","major_comments":[{"comment":"Equation (2) defines the relevance score as s_rel = s(<T>) / (s(<T>) + s(<F>)), where s(A) = log P(A|input). When the binary-choice prompt is used, P(<T>) + P(<F>) = 1, so both log probabilities are negative and s_rel is decreasing in P(<T>). For example, P(<T>) = 0.6 gives s_rel ≈ 0.36, while P(<T>) = 0.2 gives s_rel ≈ 0.88. Taken literally, the stated rule would rank documents with lower probability of '<T>' as more relevant, which is the opposite of the reported zero-shot results in Table 2. Either an exp() is missing (the intended softmax over log probabilities) or the implemented scoring rule is different from the one stated. Because every zero-shot and fine-tuned result depends on this score, the central claim cannot be reproduced from the text as written. This must be corrected and clarified.","section":"Section 2.2, Eq. (2)"},{"comment":"The paper does not describe a validation split or a model-selection protocol. The train-test split is stated as 9:1, and the sensitivity analysis over positive-negative ratios (Fig. 3) appears to be conducted on the same test sets used for the fine-tuning results in Table 3. If the 1:all ratio and other hyperparameters were selected after observing test-set performance, the reported fine-tuning comparisons are optimistic and not a fair evaluation. Please specify the validation procedure, report results with multiple seeds and error bars, and, if necessary, re-run model selection on a held-out split.","section":"Sections 3.2 and 3.3"},{"comment":"The construction of DPO preference pairs is unspecified. The text explains that each query is combined with mismatched documents to create negative pairs and that labels are '<T>' for positive and '<F>' for negative pairs, which is sufficient for SFT, but DPO requires chosen/rejected pairs. The paper does not state how these pairs are formed, whether all negatives are used, or how ties are handled. This missing detail prevents reproduction of the LaHoRe-DPO results.","section":"Section 2.3"},{"comment":"The evaluation assumes that each user utterance has exactly one correct retrieval target. In emotional support conversations, strategies such as 'Reflection of feelings' and 'Affirmation and Reassurance' are not mutually exclusive, and a single response may legitimately combine several strategies. If the single-label annotations are noisy or ambiguous, the reported Recall and p-MRR values are less reliable as measures of retrieval quality. Please report label-agreement statistics or discuss how ambiguity affects the comparison.","section":"Section 3.1"}],"minor_comments":[{"comment":"The model is named 'LaHoRe' in the title and method sections, but the abstract and one introductory sentence call it 'RaHoRe.' Please unify the terminology.","section":"Abstract and Section 1"},{"comment":"The symbol A in Eq. (2) is used before being defined; please state explicitly that A is the model's next-token choice, i.e., '<T>' or '<F>'.","section":"Section 2.2"},{"comment":"The sensitivity paragraph says '1.5' where the figure and context indicate '1:5', and 'the formal choice' should read 'the former choice.' Additionally, 'positive outnumbered by negative samples' should be rephrased.","section":"Section 3.3"},{"comment":"There are spacing and formatting artifacts in the table captions ('T able 1') and in the 'RT↓' column; please clean these up and specify the hardware and measurement protocol for response time.","section":"Table 1 and Table 4"},{"comment":"The claim about 'prefix decoding or cache-sharing technology' should cite the specific mechanism used (e.g., prompt caching or KV-cache reuse) and state whether the reported RT improvements come from the reordering alone or from an additional caching implementation.","section":"Section 2.2"}],"recommendation":"major_revision","confidential_remarks":"The scoring-rule issue in Eq. (2) is serious because it affects every result, but it is plausibly a typographical omission of an exp() or an imprecise definition of s(A). I would ask the authors to confirm the exact implemented scoring rule and provide the code path. The absence of a validation split is also a concern: the sensitivity analysis appears to select 1:all on the test sets, which would invalidate the fine-tuning numbers as reported. If the authors can supply a clean validation protocol and reproduce the trends, the paper may be publishable. I did not find evidence of intentional circularity; the zero-shot evaluation uses an external instruction-tuned model and external labels."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"LaHoRe is worth a serious look. The paper names a real gap—retrieval where query and document are linked by reasoning rather than surface similarity—and shows a 7B instruction-tuned LLM, used as a generative cross-encoder with a binary-choice prompt, beats established bi- and cross-encoders on emotional-support strategy and intent retrieval. The D⇒Q prompt order with KV-cache reuse is a practical contribution, and the SFT/DPO gains are large. If the code and data are actually released, that adds real value.\n\nThe load-bearing problem is Eq. (2). It defines the relevance score as s(<T>)/(s(<T>)+s(<F>)) where s(A)=log P(A). Because both logs are negative and the denominator is log(p(1-p)), this ratio decreases as P(<T>) rises. Taken literally, the method ranks documents with lower probability of <T> higher. That would invert every zero-shot result in Table 2. The likely intended form is a softmax over the log probabilities—i.e., exp(s(<T>))/(exp(s(<T>))+exp(s(<F>))) = P(<T>). As written, the paper's stated scoring rule cannot reproduce the reported scores. This is an internal inconsistency, not a disagreement about methodology.\n\nThe other issues are secondary but real. There are no error bars or multiple seeds, so the margin over baselines is hard to judge. The sensitivity analysis for the positive-negative ratio uses what looks like the test sets, so the 1:all choice may be selected on the same data used for the final numbers. DPO pair construction is underspecified (how are negatives paired? what reference model? what beta?). The single-label evaluation on ESconv and PsyQA is also questionable because emotional-support strategies are not mutually exclusive; a response can validly combine several strategies, so Recall/p-MRR against one annotated label is a noisy measure. Two of the four datasets are proprietary, though the repo may mitigate that.\n\nMy read: the idea is solid and the authors are onto something, but the paper as written cannot be reproduced from the equations. A referee should not desk-reject; they should ask for a corrected scoring rule, re-run or explicit confirmation, error bars, validation-based hyperparameter selection, and DPO details. If Eq. (2) is just a missing exp, the paper is a likely accept after minor revision.","headline":"Useful new retrieval task and strong results, but Eq. (2) as written inverts the ranking and must be fixed before the numbers can be trusted.","tokens_in":10351,"tokens_out":4974,"would_cite":true,"duration_ms":41461,"reading_group":"yes","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that an instruction-tuned LLM used as a cross-encoder, prompted with the binary-choice question 'Can Q be appropriately responded with D?' and scored by relative next-token probabilities, outperforms existing bi-encoder…","keywords":["hidden rationale retrieval","generative retrieval","cross-encoder LLM","direct preference optimization","binary-choice prompting","emotional support conversation","prefix caching","retrieval-augmented generation"],"falsifier":"Re-annotate the ESconv and PsyQA test queries with all acceptable reply strategies rather than a single one, then compare LaHoRe against the baselines under that multi-label evaluation; if the gap shrinks or reverses, the reported superiority is an artifact of the single-label setup.","tokens_in":9341,"feed_emoji":"🎯","tokens_out":8218,"duration_ms":58559,"temperature":0.7,"pith_summary":"The paper proposes and tests the claim that an instruction-tuned LLM used as a cross-encoder, trained with a purely generative loss, can serve as a foundation for hidden-rationale retrieval—tasks where the query and the correct document are not semantically similar but are linked by reasoning chains, logic, or experience. The method, called LaHoRe, converts retrieval into a binary-choice generation problem by asking 'Can Q be appropriately responded with D?' and scoring each candidate by the relative next-token probability of <T> versus <F>. On four emotional-support and intent-retrieval datasets, LaHoRe beats all prior bi-encoder and cross-encoder baselines in zero-shot evaluation, and improves further with supervised fine-tuning or direct preference optimization. The authors also show that putting the document before the query allows offline caching of the document's key-value states, reducing per-query response time by roughly 60% without harming accuracy.","feed_headline":"Generative yes/no scoring lifts LLM retrieval beyond all baselines","feed_subtitle":"By scoring <T> vs <F> next-token probabilities, a 7B LLM tops every baseline on four rationale-retrieval datasets.","key_machinery":"The load-bearing object is the binary-choice retrieval instruction, which turns retrieval into a text-generation task: the appended prompt {Ir}='Can Q be appropriately responded with D?' together with {Ibc}='If you think the answer is true, choose <T>; otherwise choose <F>.' The relevance score is read out as the relative confidence s_rel = s(<T>)/(s(<T>)+s(<F>)) from the LLM's next-token log-probabilities. The supporting machinery is the D⇒Q input ordering, which lets the document's key-value cache be computed offline and reused across queries, making the cross-encoder retriever practical while preserving most of the zero-shot accuracy.","core_discovery":"The central discovery, stated on the paper's own terms, is that discriminative retrieval can be reformulated as a generative preference judgment. Instead of computing a similarity score or a contrastive embedding, the retriever feeds the LLM the concatenation 'document: {D}\\n query: {Q}\\n' plus a special instruction asking whether Q can be appropriately responded with D and saying 'If you think the answer is true, choose <T>; otherwise choose <F>.' The relevance score is s_rel = s(<T>)/(s(<T>)+s(<F>)), where s(A) is the log next-token probability of answer A. With this setup, a 7B instruction-tuned LLM outperforms all compared bi-encoder and cross-encoder retrievers on hidden-rationale benchmarks, both zero-shot and after fine-tuning, and DPO fine-tuning yields the strongest results. The paper further claims the same framework can be made efficient by ordering the document before the query and caching its prefix.","pith_inferences":["If the advantage transfers beyond emotional support, the same yes/no scoring could serve as a general plug-in retriever for other non-factual tasks, such as choosing an embodied subgoal from a candidate list generated by an LLM planner.","Because the method only needs next-token probabilities, any instruction-tuned LLM could be used as the retriever without architectural changes; a natural extension is to test how retrieval quality scales with model size and instruction-tuning quality.","The single-label evaluation may be hiding ambiguity: emotional-support strategies overlap, so re-annotating test queries with all acceptable strategies could show whether LaHoRe's ranking stays plausible when more than one answer is legitimately correct.","The DPO-over-SFT result suggests that preference data is a stronger training signal than binary labels for retrieval; a direct comparison of DPO against a contrastive-loss variant with identical negative sampling would isolate what the generative formulation itself contributes."],"forward_implications":["On all four datasets (ChatIntent, ChatStrategy, ESconv, PsyQA), LaHoRe attains the best or second-best zero-shot score on every metric and the best fine-tuned score on every metric among 7B-parameter baselines.","DPO fine-tuning beats SFT fine-tuning on every dataset, indicating that pairwise preference optimization aligns the model better with hidden-rationale judgments than supervised labeling.","Using every mismatched document as a negative (a 1:all ratio) gives the best accuracy, and the benefit grows with the size of the candidate pool.","Swapping the order to D⇒Q cuts response time from roughly 47.5 ms to 19.3 ms per query in the reported tests, with accuracy roughly unchanged.","In an end-to-end RAG setting, LaHoRe-retrieved strategies produce responses that human judges prefer over raw LLM output 90% of the time."],"supporting_citations":[{"why":"Supplies the ESconv dataset with its reply-strategy annotations, the primary open benchmark for hidden-rationale retrieval.","marker":"[10]"},{"why":"Supplies the PsyQA dataset, the second open benchmark used for evaluation.","marker":"[22]"},{"why":"Provides direct preference optimization, the fine-tuning method that yields LaHoRe's strongest results.","marker":"[18]"},{"why":"Introduces the binary-choice prompting technique that the retrieval instruction builds on.","marker":"[26]"},{"why":"Defines the base instruction-tuned LLM that LaHoRe is initialized from.","marker":"[17]"},{"why":"A 7B bi-encoder LLM retriever that serves as a key baseline LaHoRe must outperform.","marker":"[11]"},{"why":"A 7B instruction-tuned embedding model used as a strong bi-encoder baseline.","marker":"[23]"},{"why":"A 7B cross-encoder retriever that is the closest architectural baseline.","marker":"[8]"},{"why":"A generative cross-encoder that also combines generation and retrieval, the most relevant competitive baseline.","marker":"[13]"},{"why":"Supplies the prefix key-value cache technology that makes the D-before-Q ordering efficient.","marker":"[7]"}],"fun_headline_variants":["Yes/no token trick makes LLM the top rationale retriever","LLM yes/no scoring beats baselines on hidden-rationale retrieval","Generative preference scoring: LLM tops all on rationale retrieval","Reformulate retrieval as a yes/no choice: LLM wins rationale"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"Each user query is assumed to have exactly one correct retrieval target, because every dataset labels each query with a single strategy or intent; if those labels are noisy or the strategies overlap, the reported Recall and p-MRR numbers stop being a reliable measure of retrieval quality.","fun_headline_variants_meta":{"raw":{"variants":["Yes/no token trick makes LLM the top rationale retriever","LLM yes/no scoring beats baselines on hidden-rationale retrieval","Generative preference scoring: LLM tops all on rationale retrieval","Reformulate retrieval as a yes/no choice: LLM wins rationale"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00135,"raw_usage":{"total_tokens":5499,"prompt_tokens":980,"completion_tokens":4519,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":596,"completion_tokens_details":{"reasoning_tokens":4443}},"tokens_in":596,"tokens_out":4519,"duration_ms":28676,"temperature":1.0,"reasoning_tokens":4443,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T10:24:31.305870+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-annotate the ESconv and PsyQA test queries with all acceptable reply strategies rather than a single one, then compare LaHoRe against the baselines under that multi-label evaluation; if the gap shrinks or reverses, the reported superiority is an artifact of the single-label setup.","supporting_citations":[{"cited_title":"NAACL-HLT pp","cited_arxiv_id":null,"evidence_quote":"Introduces the binary-choice prompting technique that the retrieval instruction builds on."},{"cited_title":"Technical report, Alibaba Group (2024)","cited_arxiv_id":null,"evidence_quote":"Defines the base instruction-tuned LLM that LaHoRe is initialized from."},{"cited_title":"In: Ku, L.W., Martins, A., Srikumar, V","cited_arxiv_id":null,"evidence_quote":"A 7B instruction-tuned embedding model used as a strong bi-encoder baseline."},{"cited_title":"In: Ku, L.W., Martins, A., Srikumar, V","cited_arxiv_id":null,"evidence_quote":"A 7B cross-encoder retriever that is the closest architectural baseline."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"A generative cross-encoder that also combines generation and retrieval, the most relevant competitive baseline."}],"review_version":1}