Do not Abstain! Identify and Solve the Uncertainty

Bo Zheng; Jingquan Peng; Jingyu Liu; Tiezheng Ge; Xiaopeng Wu; Xubin Li; Yong Liu

REVIEW 5 major objections 6 minor 31 references

Do not Abstain! Identify and Solve the Uncertainty

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that an LLM's source of uncertainty can be diagnosed by generating a follow-up inquiry and testing the uniqueness of its answer: a unique, objective answer signals missing documents, multiple answers signal a vague query…

desk verdict ConfuseBench and the over-attribution-to-ambiguity finding are worth keeping, but the proposed uniqueness-of-answer probe is largely an instruction artifact from its own prompt. read the letter →

arxiv 2506.00780 v1 pith:2RAZIT43 submitted 2025-06-01 cs.AI

Jingyu Liu , Jingquan Peng , xiaopeng Wu , Xubin Li , Tiezheng Ge , Bo Zheng , Yong Liu This is my paper

classification cs.AI

keywords LLMuncertaintysourceidentificationqueryambiguitydocumentscarcitymodelcapabilitylimitsansweruniquenessConfuseBenchon-policyDPO

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Large language models answer with false confidence when they face questions they cannot resolve, and the usual remedy, saying 'I don't know,' throws away the chance to fix the underlying problem. This paper introduces ConfuseBench, a benchmark built around three sources of uncertainty: sparse documents, limited reasoning capability, and ambiguous queries, and shows that current models misdiagnose these causes, blaming vagueness in the question when the real problem is missing information or their own limits. The proposed remedy is a two-step test: generate a follow-up inquiry that isolates the confusing part of the original question, then classify the uncertainty by the uniqueness of that inquiry's answer. A unique, objective answer signals missing documents and points to retrieval; several plausible answers signal ambiguity and point to clarification; a rephrased or incoherent inquiry signals a capability gap and points to chain-of-thought reasoning. Measured on the benchmark, the test raises uncertainty-classification accuracy for GPT-4o from roughly 0.49 to 0.61 and lifts the weaker Llama-3-70B from 0.41 to 0.55.

What carries the argument

The load-bearing object is the inquiry-answer uniqueness test: a follow-up question that isolates the confusing sub-aspect of the original query, plus a preset answer the model is asked to extend or contradict. The test converts an abstract judgment ('why is this hard?') into an observable behavior, whether the model repeats the preset answer or invents a new one, and maps that behavior to a remedy: unique and factual means retrieval, multiple answers mean clarification, and a rephrased or incoherent inquiry means chain-of-thought reasoning. A formal claim (Theorem 5.2) says the uncertainty of the inquiry is close to the uncertainty of the query whenever the inquiry is meaningful, so classifying the smaller question stands in for classifying the original one. The training component, InteractDPO, generates chosen and rejected inquiry pairs by letting the live model interact with a retrieval system or a simulated user during training and keeping the inquiries that actually solved the query.

What would settle it

Take the benchmark's document-scarcity and ambiguity cases, generate their follow-up inquiries, and run the answer-uniqueness test with the preset-answer prompt stripped of any instruction about whether to repeat or diversify the answer. If the model repeats the preset answer just as often for ambiguous, subjective inquiries as for factual ones, the uniqueness signal disappears and the reported classification gains should collapse toward chance; that observation would show the method's success depends on self-fulfilling prompt instructions rather than on a stable model behavior.

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Extended reading notes

Core claim

The paper's central claim is that the source of an LLM's uncertainty can be read off from the answer to a well-chosen follow-up inquiry, because the uncertainty held by the inquiry mirrors the uncertainty of the original query (Theorem 5.2). Concretely, the model first generates a context-aware inquiry that targets the confusing aspect of the question; it then receives a preset, logically coherent answer to that inquiry and is asked to produce a further answer. If the inquiry asks for an objective fact, the model tends to repeat the preset answer, so the uncertainty is diagnosed as document scarcity and the remedy is retrieval. If the inquiry admits many reasonable answers, the model can generate a new, different one, so the uncertainty is diagnosed as query ambiguity and the remedy is clarification. If the inquiry is merely a rephrasing of the original question or is logically incoherent, the model cannot place the difficulty in either missing facts or the query, and the diagnosis is limited capability, remedied by chain-of-thought reasoning. The paper reports that this answer-uniqueness judgment raises uncertainty-classification accuracy on ConfuseBench from about 0.49 to 0.61 for GPT-4o and from 0.41 to 0.55 for Llama-3-70B, and that on-policy training of the inquiry-generation step (InteractDPO) further improves both classification and answer quality.

Load-bearing premise

The entire classifier rests on a behavioral heuristic: when handed a preset answer, an LLM repeats it if the underlying question is factual but can produce a new, different answer if the question is subjective, and this repeat-versus-diversify behavior reliably marks the boundary between missing documents and ambiguous queries. If models do not behave this way in general, the answer-uniqueness test has no signal to sort on.

Editorial extensions

If this is right

LLMs that adopt the test can route each uncertain query to a concrete remedy, retrieve, clarify, or reason longer, instead of abstaining with 'I don't know,' which should raise answer quality on questions that become answerable once the missing piece is supplied.
The largest accuracy gains appear for mid-size models such as Llama-3-70B, suggesting the test is a cheap intervention that narrows the gap between small and large models on uncertainty handling.
The benchmark's finding that models over-attribute uncertainty to ambiguity and under-acknowledge capability limits becomes a measurable, trainable failure mode rather than an anecdote.
Because the answer-uniqueness signal is generated by the model itself, the method requires no external classifier, calibrated confidence scores, or ensembles to diagnose uncertainty.
On-policy training of inquiry generation should compound with the diagnostic test: better inquiries yield more reliable diagnoses, which in turn yield better final answers.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The repeat-versus-diversify heuristic could be stress-tested as a calibrated signal: instead of a hard threshold, the probability that a model repeats a preset answer under repeated sampling could serve as a continuous measure of how objective or subjective the inquiry is.
Because the benchmark labels were produced by the same kind of generator the method relies on (GPT-4o), its accuracy on naturally occurring or adversarial queries remains open; a test on independently labelled help-desk or search logs would settle how far it generalizes.
The same inquiry-answer trick may extend to mitigation decisions the paper does not explore, such as choosing between tool use, memory lookup, and more computation, since any pair of remedies corresponds to a question whose answer uniqueness splits the two cases.
The prompt used to elicit the answer instructs the model to repeat preset answers for document/API inquiries and to diversify for subjective ones, so part of the measured gain may be prompt compliance rather than a stable behavioral property; ablating those instructions would quantify that share.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

Quick summary: the part of this paper worth keeping is ConfuseBench and the empirical observation that LLMs over-attribute uncertainty to query ambiguity. The proposed fix—classify uncertainty by whether a follow-up inquiry's answer is unique or diverse—is weaker than it looks, because the probe prompt tells the model to produce exactly that pattern.

What is new: ConfuseBench spans three uncertainty sources (document scarcity, limited capability, query ambiguity) across QA, assistant, and tool-use datasets, and it ships with code and data. That is a useful addition, and the taxonomy is sensible. The main finding is also credible: Table 4 shows all tested models, especially weak ones, have high recall on "ambig" and low recall on "doc" and "ability" when asked directly. InteractDPO, an on-policy DPO variant with retrieval/user interaction as the reward signal, is a reasonable idea and shows small but consistent gains in Table 6.

Soft spots, in order of importance. First, the load-bearing verification heuristic in Section 5.1 is instruction-induced. Appendix F's prompt explicitly requires the model to repeat Possible Answers for document/API inquiries and to generate diverse answers for subjective or clarifying inquiries. So the observed repetition-versus-diversity pattern is the model following orders, not evidence of an emergent uncertainty-diagnosis ability. A neutral-prompt replication would be the decisive test; without it, the uniqueness probe cannot support the paper's central claim. Second, benchmark labels are model-relative: cases a model can already answer are excluded from its evaluation, so UCA scores across models are not directly comparable. That matters for claims like "the best model only gets about 50%." Third, Theorem 5.2 is not a proof as written. Assumption B.1 smuggles in that the optimal model generates the optimal inquiry with probability one, and the KL algebra skips steps that do not obviously work. The theorem can be dropped or fixed without harming the empirical contribution. Fourth, the post-hoc "capacity override" is tuned on the benchmark and should be ablated and reported as a parameter, not a fixed rule.

The paper is still worth refereeing because the benchmark and the over-attribution finding are independently useful, and both can be salvaged with revision. I would not cite the uniqueness-probe method in its current form. If the authors add a neutral-prompt evaluation and fix the labels, this becomes a solid paper.

Recommendation: send to review, but expect major revision. Start the referee conversation with "confirm the uniqueness signal under a neutral prompt."

Referee Report

5 major / 6 minor

Summary. The paper introduces ConfuseBench, a benchmark covering three uncertainty sources (document scarcity, limited capability, query ambiguity), and reports that current LLMs systematically over-attribute uncertainty to ambiguity. To improve uncertainty-source identification, the authors propose generating a context-aware follow-up inquiry and classifying the source by the uniqueness of the inquiry's answer: unique objective answers indicate retrieval, multiple answers indicate clarification, and rephrased or incoherent inquiries indicate chain-of-thought. They also propose InteractDPO, an on-policy DPO variant that trains a model to generate better inquiries using interaction-based preference signals. Experiments on six LLMs show UCA improvements from about 0.49 to 0.61 for GPT-4o and from 0.41 to 0.55 for Llama-3-70B, with additional gains attributed to InteractDPO.

Significance. The problem addressed is genuinely important: moving from abstention to diagnosing and mitigating the actual source of uncertainty could make LLM assistants more useful. The benchmark spans QA, assistant, and tool-use scenarios, and the paper ships code and data. The qualitative finding that models prefer to blame ambiguity rather than capability limitations is interesting and potentially reproducible. However, the empirical protocol as written does not currently support the paper's central claim because the uniqueness signal is instruction-induced by the probe prompt, the benchmark labels are model-relative, and the reported UCA numbers include a post-hoc capacity override. If these issues are fixed, the benchmark and the general idea of using follow-up inquiry behavior to route mitigation strategies could be a useful contribution to the field.

major comments (5)

[§5.1 and Appendix F] The central classification signal—whether the inquiry's answer is unique or diverse—is instruction-induced rather than emergent. The 'Prompt to generate the answer of inquiry' in Appendix F explicitly tells the model to repeat the Possible Answers for document/API inquiries (requirements 2 and 6) and to randomly generate diverse, semantically distinct answers for subjective or clarifying inquiries (requirements 3 and 4). Thus the observed repetition-versus-diversity pattern is a direct consequence of prompt compliance, not a behavioral property of LLMs that can validate the method. The authors should re-run the verification step with a neutral prompt that simply asks for the answer to the inquiry without instructing repetition or diversity, and report whether the uniqueness signal persists across model families.
[§3] The benchmark labels are model-relative: the text states that if a model can generate a correct answer from the original query and documents, the case is excluded from evaluation for that model. This means different models are scored on different subsets of ConfuseBench, so the cross-model comparisons in Tables 3–5 and the claim that weaker models over-attribute to ambiguity may be confounded by unequal test sets. The authors should either construct a fixed evaluation set with model-independent labels or report per-model subset sizes and results on the common subset of cases.
[Theorem 5.2 and Appendix B] The proof of Theorem 5.2 is not valid as written. Assumption B.1 already stipulates p(q*|x,d,θ*)=1, which essentially assumes the optimal inquiry is generated deterministically; the proof then uses an unstated independence assumption p(c|x,d,θ*)=p(c|q) without justification. In Eq. (3), the derivation replaces a sum over y with a term involving q*, drops the integration over q/c, and substitutes q* for q in a way that does not follow. Consequently the stated bound |U(q)-U(x)| ≤ -log p(q*|...) is not established. Since this theorem is used to justify the claim that the inquiry's uncertainty mirrors the query's uncertainty, the authors should either supply a correct proof under explicit assumptions or present the method as purely heuristic.
[§6] The evaluation includes a post-hoc capacity override: 'we also use prompt judge is it lack of capacity for all 3 methods due to its high precision.' This rule is not described in the method section, the prompt is not shown, and it appears to be tuned to the benchmark's observed precision patterns. Because this override is applied to all reported UCA numbers in Tables 5 and 6, its contribution must be quantified. The authors should report results without the override, include the exact override prompt in Appendix F, and specify whether the override threshold or rule was selected after inspecting benchmark results.
[Table 6 and §6] The InteractDPO evaluation uses the finetuned Qwen2.5-7B only to generate inquiries, while 'GPT-4o to conduct classification and further answering' with the same engineered probe from Appendix F. The reported UCA gains therefore measure GPT-4o's compliance with the repetition/diversity instructions plus the quality of the generated inquiries, not the trained model's own uncertainty-diagnosis capability. The authors should report classification accuracy using the finetuned model itself, or at least ablate the classifier choice to show that the improvement is not an artifact of using GPT-4o as the classifier.

minor comments (6)

[Abstract] There are grammatical issues that should be corrected, e.g., 'overlooks the opportunity' should be 'overlooking the opportunity' and 'a benchmark mainly focus on' should be 'a benchmark mainly focusing on'.
[Table 2] The column headers 'document ambiguity ability' are unclear and do not match the terminology used elsewhere in the paper (document scarcity, limited capability, query ambiguity). The numbers in the table also do not make clear how they relate to the stated total of 5×(50+50+30)=650 benchmark cases.
[Tables 3, 5, 6] The text says experiments were repeated three times and averaged, but no variance or confidence intervals are reported; standard deviations should be included to assess the stability of the UCA differences.
[Appendix B] The proof uses the notation D_KL(P||Q) and D+_KL(Q||P) inconsistently, and integrals are used where sums over a discrete space are intended; this makes the already questionable derivation harder to follow.
[Appendix E] The section appears to be incomplete: Example 3 is introduced as 'Ambiguous query which requires interaction with the user' but no content follows it before the few-shot results are discussed.
[References] Xiong et al. (2023) and Xiong et al. (2024) appear to refer to the same paper with the same title and arXiv identifier; the duplicate reference should be removed or resolved.

Circularity Check

1 steps flagged · score 7.0 of 10

The answer-uniqueness criterion is instruction-induced by the probe prompt in Appendix F, making the central uncertainty-source classification largely self-fulfilling.

self definitional [Section 5.1 (Judge Based on Inquiry Answer) and Appendix F, 'Prompt to generate the answer of inquiry', requirements 3, 4, 6]
"we designed a verification method... For objective factual questions, the model—lacking prior knowledge—tends to directly repeat the fabricated answer. However, for open-ended subjective questions, the model recognizes the potential for diverse solutions and can still produce novel, reasonable responses... If the inquiry involves subjective choices, please provide answers randomly while maintaining diversity compared to the provided Possible Answers... If the inquiry seeks for more document/API information, and please repeat the Possible Answers if it is not empty."

The probe prompt used to produce the inquiry answer explicitly instructs the model to repeat the preset Possible Answers for document/API inquiries and to generate diverse, distinct answers for subjective-choice or clarifying inquiries. Section 5.1 then treats repetition as evidence that the uncertainty requires retrieval and diversity as evidence that clarification is needed. The uniqueness signal is thus a direct consequence of instruction-following, not an emergent LLM property that could confirm the method.

full rationale

The central circular step is the answer-uniqueness verification. Appendix F's 'Prompt to generate the answer of inquiry' commands repeat-the-preset-answer for document/API inquiries and generate-diverse-answers for subjective/clarifying inquiries, and Section 5.1 presents the resulting repetition/diversity pattern as an empirical discovery. This makes the key classification step self-fulfilling: the probe defines the very behavior the method claims to detect. The benchmark construction is model-relative but is a coherent operationalization for the stated task, and InteractDPO's chosen/rejected pairs are based on real interaction outcomes, so the training comparison has independent content. There are no load-bearing self-citations; citations to the authors' own prior work are for retrieval methods only. Theorem 5.2 is conditional on Assumption B.1 and does not by itself smuggle the conclusion, though the paper uses it loosely to justify the CoT rule. Overall, the paper's main empirical claim about uniqueness-based classification is substantially circular, but not entirely: the benchmark and on-policy training retain independent value. Score 7 reflects a central verification that reduces by construction while other contributions remain non-circular.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the assumption that uncertainty has exactly three separable sources and that LLM answer behavior can reveal the source. The formal result is underdetermined: Assumption B.1 postulates the optimal inquiry and the proof uses an unstated independence of clarifications from the query. There are no fitted numeric constants, but three hand-chosen decision rules (majority voting, disagreement switch, capacity override) shape the reported numbers.

free parameters (3)

Majority vote sample count = 3
Section 4 and 6: uncertainty classification uses 3 samples and majority voting; chosen by hand, no ablation.
Inquiry/answer disagreement switch = 2 samples (if two inquiry-based judgments differ, use answer-based judgment)
Section 5.1: hand-chosen rule to decide when to trust answer uniqueness; no ablation.
Capacity-override flag = 1 (apply prompt judge for lack of capacity to all methods)
Section 6: post-hoc rule added because the prompt judge has high precision for the ability class; this is a hand-fitted decision rule on the benchmark.

assumptions (5)

domain assumption The three uncertainty sources (document scarcity, limited capability, query ambiguity) are mutually exclusive and exhaustive for the benchmark queries.
Section 3: each query is assigned exactly one label by construction; real queries may mix sources, and the Limitations section acknowledges other sources exist (factual vs background gaps, ToT vs CoT, factually incorrect queries).
ad hoc to paper An optimal model θ* exists that answers perfectly with the right documents and clarification, and the optimal inquiry q* has probability 1 under θ*.
Appendix B, Assumption B.1; this assumption is not verified and effectively builds the conclusion of Theorem 5.2 into the setup.
ad hoc to paper The clarification c is independent of query x and documents d given the inquiry q (used in the proof as p(c|x,d,θ*) = p(c|q)).
Appendix B, derivation of Ua; the proof substitutes p(c|q) without conditioning on x,d, which is a strong unstated assumption.
domain assumption LLM response behavior distinguishes objective from subjective inquiries: models repeat a provided preset answer for objective facts and generate novel answers for subjective or ambiguous questions.
Section 5.1, answer-uniqueness verification; this is the load-bearing behavioral heuristic of the method and is stated without calibration evidence.
domain assumption The uncertainty of the generated inquiry is equivalent to the uncertainty of the original query.
Section 5.1 opening and Theorem 5.2; used to justify classifying the query by the inquiry. The proof only gives a bound under the optimal-model assumption, and the main text states it as a fact.

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Cite this review

Pith. "Pith review of Do not Abstain! Identify and Solve the Uncertainty." pith.science (2026). https://pith.science/paper/2RAZIT43

@misc{pith2026250600780,
  author       = {Pith},
  title        = {Pith review of: Do not Abstain! Identify and Solve the Uncertainty},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2RAZIT43}},
  note         = {Machine review of arXiv:2506.00780}
}

read the original abstract

Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., "I don't know") overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs' ability of recognizing and addressing the source of uncertainty, we introduce \textbf{ConfuseBench}, a benchmark mainly focus on three types of uncertainty: document scarcity, limited capability, and query ambiguity. Experiments with ConfuseBench reveal that current LLMs struggle to accurately identify the root cause of uncertainty and solve it. They prefer to attribute uncertainty to query ambiguity while overlooking capability limitations, especially for those weaker models. To tackle this challenge, we first generate context-aware inquiries that highlight the confusing aspect of the original query. Then we judge the source of uncertainty based on the uniqueness of the inquiry's answer. Further we use an on-policy training method, InteractDPO to generate better inquiries. Experimental results demonstrate the efficacy of our approach.

Figures

Figures reproduced from arXiv: 2506.00780 by the authors.

**Figure 2.** Abstract Meaning Representation for "The [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** In the left case, the model retrieved some doc [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: The inquiry score and the percentage of errors [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Judge the source of uncertainty based on [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Method Pipeline A InteractDPO In order to improve the ability of locating the uncertainty and generate the corresponding inquiry, we propose InteractDPO. Vanilla DPO use preference datasets collected ahead of training the responses in the dataset are usually generated…

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Reference graph

Works this paper leans on

31 extracted references · 29 canonical work pages

[1]

Remove certain modifiers and descriptive words to make some nouns in the query ambiguous

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[2]

Delete some key information , making the query impossible to answer

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[3]

Change the relation between nodes to make their relationship ambiguous

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[4]

Reorganize the structure of the AMR , make it less clear The following are some requirements for the obscured query

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[5]

The answer to obscured query should be wrong , or there should be no response ( NO RES )

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[6]

For the obscured query with clarification , the answer should be the same or similar to the answer to the original query

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[9]

The obscured should not be answerable without further calrification ,

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[10]

s t e p _ b y _ s t e p _ t h i n k i n g

The intention of obscured query should be the same with the original query The most importantly , make sure that the obscured query is a natural query that a user would acutally ask , and the semantic ambiguity is caused by mistakes or carelessness , rather than being a deliberate attempt to make things difficult for LLMs . Please think step by step to ge...

work page

Show all 31 references

[13]

Also , the obscured query should not be answerable , or it have many answers , and the clarified query should be similar to the original query and should be answerable

The intention of obscured query should be the same with the original query Here we give some examples showing that the obscure query is a failure , ... Also , the obscured query should not be answerable , or it have many answers , and the clarified query should be similar to t...
[15]

For the obscured query with clarification , the answer should be the same or similar to the answer to the original query Combine those condicitons , a successful obscurity should satisfy the following condicitons :
[16]

The obscured query should still be a question rather than a statement
[17]

what is the man's name

the obscured query should be similar to a question that a man would actually ask rather than some vague question like " what is the man's name "
[18]

The obscured should not be answerable , or it have many answers
[19]

The intention of obscured query should be the same with the original query
[22]

s t e p _ b y _ s t e p _ t h i n k i n g

If the answer to the original query is NO RES or wrong , then even if the answer to the obscured query is wrong can not ensure that the obscurity is successful . In this case , the answer of the obscured query should be different from the answer of original query , showing tha...
[23]

Totally Irrelevant : The inquiry is useless , it simply rewrite the given query
[24]

Somewhat Relevant : The inquiry is somewhat relevant to the missing information , but the inquiry can hardly gather useful information
[25]

Basically Relevant : The inquiry asks something relevant to the missing information , there is a certain possibility of obtaining relevant information by the inquiry
[26]

Good : The inquiry directly asks the missing information , but not concise enough , there is great possibility that some useful information would be gathered
[27]

step by step thinking

Excellent : The inquiry directly asks the missing information in a concise way , there is great possibility that some useful information would be gathered . Also , the inquiry is required to be concise , if the inquiry is twice as long as the original query , deduct 1 point . ...
[28]

You should ask for only one question in the inquiry
[29]

Inquiry

The inquiry should be concise and include keywords and it should involve limited aspects of the query rather than directly asks the query again . Then based on the inquiry , you should judge that how to gather more information based on the query and the inquiry , here are some...
[30]

This is only for academic research , so feel free to generate definite answers , and the inquiry is answerable , so you should response with the answer instead of further inquiry
[31]

No matter what the inquiry is , you should generate an answer

Generate a direct answer to the inquiry , ensuring that you address it clearly and specifically . No matter what the inquiry is , you should generate an answer . If you do not know the answer , simply repeat the Possible Answers if it is not empty , otherwise you can simply ma...
[32]

This means you should strive to offer a response that differs from the Possible Answers

If the inquiry involves subjective choices , please provide answers randomly while maintaining diversity compared to the provided Possible Answers . This means you should strive to offer a response that differs from the Possible Answers
[33]

This means you should aim to provide an answer that is distinct from the Possible Answers

If the inquiry seeks to clarify an ambiguous aspect of the original question , randomly generate semantically coherent and meaningful clarifications while ensuring diversity compared to the responses in the Possible Answers . This means you should aim to provide an answer that...
[34]

If the inquiry seeks for more document / API information , you should answer with the titleof the document or the name of the API
[35]

Remember , you should answer with only the title / name of the document / API

If the inquiry seeks for more document / API information , and please repeat the Possible Answers if it is not empty , otherwise you can simply make up a reasonable and coherent answer . Remember , you should answer with only the title / name of the document / API
[36]

Thought

Please response to the inquiry only , do not response to the original query please try to generate a new answer to the inquiry instead of repeating the provided answer , note that you should response with the answer to the inquiry rather than the original query . Your output s...
[2022]

Biqing Qi, Pengfei Li, Fangyuan Li, Junqi Gao, Kaiyan Zhang, and Bowen Zhou

Training language models to follow instruc- tions with human feedback.Advances in neural in- formation processing systems, 35:27730–27744. Biqing Qi, Pengfei Li, Fangyuan Li, Junqi Gao, Kaiyan Zhang, and Bowen Zhou. 2024. Online dpo: Online direct preference optimization with ...

2024 arXiv
[2024]

(long context about biography of A)

Direct preference optimization: Your lan- guage model is secretly a reward model.Preprint, arXiv:2305.18290. Kayla Schroeder and Zach Wood-Doughty. 2024. Can you trust llm judgments? reliability of llm-as-a-judge. arXiv preprint arXiv:2412.12509. Kaize Shi, Xueyao Sun, Li He, ...

2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Remove certain modifiers and descriptive words to make some nouns in the query ambiguous

work page

[2] [2]

Delete some key information , making the query impossible to answer

work page

[3] [3]

Change the relation between nodes to make their relationship ambiguous

work page

[4] [4]

Reorganize the structure of the AMR , make it less clear The following are some requirements for the obscured query

work page

[5] [5]

The answer to obscured query should be wrong , or there should be no response ( NO RES )

work page

[6] [6]

For the obscured query with clarification , the answer should be the same or similar to the answer to the original query

work page

[7] [9]

The obscured should not be answerable without further calrification ,

work page

[8] [10]

s t e p _ b y _ s t e p _ t h i n k i n g

The intention of obscured query should be the same with the original query The most importantly , make sure that the obscured query is a natural query that a user would acutally ask , and the semantic ambiguity is caused by mistakes or carelessness , rather than being a deliberate attempt to make things difficult for LLMs . Please think step by step to ge...

work page