Reasoning about Intent for Ambiguous Requests

Irina Saparina; Mirella Lapata

arxiv: 2511.10453 · v3 · submitted 2025-11-13 · 💻 cs.CL · cs.AI

Reasoning about Intent for Ambiguous Requests

Irina Saparina , Mirella Lapata This is my paper

Pith reviewed 2026-05-17 22:08 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords ambiguous requestsintent reasoningreinforcement learningconversational question answeringsemantic parsingstructured outputdual reward

0 comments

The pith

Training models with dual recall and precision rewards lets them output one structured response that lists multiple valid interpretations and answers for ambiguous requests.

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

The paper argues that large language models can be trained to respond to ambiguous requests by explicitly enumerating different possible interpretations, each paired with its corresponding answer, instead of silently committing to one reading. This is done through reinforcement learning that rewards broad coverage of valid answers on ambiguous inputs while penalizing extra interpretations on clear ones. Supervision comes solely from having multiple valid answers available per input, with no need for labeled interpretations or clarification questions. A sympathetic reader would care because this setup could make AI behavior more transparent, reduce frustration from mismatched assumptions, and lower safety risks when an implicit choice turns out wrong.

Core claim

The central claim is that reinforcement learning with a dual reward objective—maximizing recall of valid answers on ambiguous inputs to ensure coverage and maximizing precision on unambiguous inputs to suppress spurious alternatives—enables models to produce structured outputs that enumerate distinct interpretations together with their answers, and that this training succeeds using only sets of multiple valid answers as the supervision signal.

What carries the argument

The dual-reward reinforcement learning objective that balances recall on ambiguous inputs with precision on unambiguous inputs to learn structured multi-interpretation outputs.

If this is right

Higher coverage of valid answers is achieved on conversational question answering and semantic parsing tasks compared with baseline approaches.
Human evaluation shows the predicted interpretations are meaningful and directly explain their paired answers.
The single generation step produces a structured output that makes interpretations explicit while remaining efficient.
The structured format enables easier use in downstream applications that can consume enumerated interpretations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Making interpretations explicit in this way could allow users or downstream systems to review and select among alternatives before committing to an action.
The method might extend naturally to multi-turn conversations where ambiguity accumulates across turns.
Lower annotation cost from using only multiple answers could make the approach easier to apply to new domains or languages.

Load-bearing premise

That multiple valid answers per input alone provide sufficient supervision to train the dual-reward RL objective without needing explicit interpretation labels or clarification questions.

What would settle it

An evaluation on ambiguous requests where the model covers fewer valid answers than baselines or produces extra spurious interpretations on clearly unambiguous requests would show the dual-reward training does not deliver the claimed coverage and suppression.

Figures

Figures reproduced from arXiv: 2511.10453 by Irina Saparina, Mirella Lapata.

**Figure 1.** Figure 1: Reasoning length (number of characters) vs. coverage (ambiguous subsets). our approach. As can be seen, thinking models frequently overthink on Abg-CoQA, getting stuck on the same interpretation rather than considering alternatives. Traces from our model occasionally contain repetitions on Abg-CoQA, but they are significantly more concise. On Ambrosia, thinking models sometimes miss interpretations entir… view at source ↗

read the original abstract

Large language models often respond to ambiguous requests by implicitly committing to one interpretation, frustrating users and creating safety risks when that interpretation is wrong. We propose generating a single structured response that enumerates the different ways an ambiguous request can be interpreted, each coupled with a corresponding answer. Our models are trained with reinforcement learning using a dual reward objective: recall on ambiguous inputs to maximise coverage of valid interpretations, and precision on unambiguous ones to suppress spurious alternatives. Training requires only multiple valid answers per input as supervision, no clarification questions or explicit interpretations are needed. Experiments on conversational question answering and semantic parsing demonstrate that our method achieves higher coverage of valid answers than baseline approaches. Human evaluation confirms that predicted interpretations are meaningful and explain their corresponding answers. Our approach promotes transparency with explicit interpretations, achieves efficiency by requiring only one generation step, and supports downstream applications through its structured output format.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a clean RL recipe for generating multiple interpretations plus answers on ambiguous inputs using only multi-answer supervision, with coverage gains and human confirmation that the interpretations hold up.

read the letter

The punchline is that this work gives a practical way to train models to output multiple interpretations for ambiguous user requests in one go, using reinforcement learning with recall and precision rewards based only on having multiple valid answers available. What the paper does well is demonstrate higher coverage of valid answers on conversational question answering and semantic parsing compared to baselines. The dual-reward setup seems effective for balancing coverage on ambiguous cases with restraint on unambiguous ones. The human evaluation is a plus, showing that the predicted interpretations are meaningful and actually explain the corresponding answers. This addresses a real issue with LLMs committing to single interpretations too quickly. On the soft spots, the stress-test concern about achieving coverage without genuinely distinct interpretations is reasonable to raise, given that supervision is only on the answer side. However, the human study provides evidence against it being purely superficial, as judges found the interpretations useful. That said, the abstract lacks details on exact reward computation or statistical significance, which might be clarified in the full paper but leaves the empirical support a bit thin for now. No major issues with circularity or invented entities here. This paper is for researchers and practitioners in conversational AI and LLM safety who need better ways to manage ambiguity without extra data collection. Readers working on dialogue systems or uncertainty in generation would find the structured output format and efficiency claims useful. It deserves serious peer review because the core idea is grounded in existing RL methods but applied in a targeted way with supporting experiments and human validation. I would recommend sending it to referees for a full evaluation.

Referee Report

3 major / 1 minor

Summary. The paper proposes training LLMs to respond to ambiguous requests with a single structured output that enumerates multiple interpretations, each paired with a corresponding answer. Models are optimized via reinforcement learning with a dual-reward objective: a recall reward on ambiguous inputs to maximize coverage of provided valid answers, and a precision reward on unambiguous inputs to suppress extraneous alternatives. Supervision consists solely of multiple valid answers per input; no explicit interpretation labels or clarification questions are required. Experiments on conversational question answering and semantic parsing report higher coverage of valid answers relative to baseline approaches, and a human evaluation finds the predicted interpretations to be meaningful and explanatory of their answers.

Significance. If the central empirical claims are substantiated, the approach provides a transparent and efficient mechanism for handling ambiguity in a single generation step while producing structured outputs usable by downstream systems. The reliance on answer sets alone as supervision is a notable strength that could reduce annotation costs. However, the current manuscript leaves the robustness of the coverage gains and the distinctness of the learned interpretations insufficiently demonstrated.

major comments (3)

[Experiments] Experiments section: the manuscript states that the method achieves higher coverage than baselines but supplies no details on baseline implementations, the precise formulas used to compute the recall and precision rewards from the answer sets, or any statistical significance tests. These omissions make it impossible to evaluate whether the reported improvements are attributable to the dual-reward objective or to differences in prompting or decoding.
[Method] Method section (dual-reward formulation): because the reward signal is defined exclusively over the answer component of the structured output, it remains unclear whether the model is induced to produce causally distinct interpretations or merely answer variants that happen to match the supervision set. No ablation or analysis is presented that isolates the contribution of the interpretation component.
[Human Evaluation] Human evaluation subsection: the protocol for judging whether interpretations are 'meaningful' and 'explain their corresponding answers' is not described, nor is inter-annotator agreement reported. Without these details the claim that the interpretations are non-trivial cannot be assessed.

minor comments (1)

[Abstract] The abstract and introduction use the term 'coverage of valid answers' without an explicit definition or reference to the corresponding metric in the experimental section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major point below and will revise the paper to improve reproducibility, clarity, and evaluation details.

read point-by-point responses

Referee: [Experiments] Experiments section: the manuscript states that the method achieves higher coverage than baselines but supplies no details on baseline implementations, the precise formulas used to compute the recall and precision rewards from the answer sets, or any statistical significance tests. These omissions make it impossible to evaluate whether the reported improvements are attributable to the dual-reward objective or to differences in prompting or decoding.

Authors: We agree these implementation and evaluation details are necessary for assessing the results. In the revised manuscript we will add: (1) full descriptions of all baseline implementations including prompting and decoding strategies, (2) the exact reward formulas (recall as the fraction of gold answers covered by the model's enumerated answers; precision as the fraction of model answers that match any gold answer), and (3) statistical significance tests (paired bootstrap or McNemar tests with p-values) comparing coverage metrics. revision: yes
Referee: [Method] Method section (dual-reward formulation): because the reward signal is defined exclusively over the answer component of the structured output, it remains unclear whether the model is induced to produce causally distinct interpretations or merely answer variants that happen to match the supervision set. No ablation or analysis is presented that isolates the contribution of the interpretation component.

Authors: The reward operates only on answers, yet the model must emit a structured output containing both interpretations and answers. To achieve high recall across multiple distinct gold answers, the model is incentivized to generate interpretations that lead to different answers rather than mere paraphrases. Human evaluation already indicates the interpretations are meaningful and explanatory. Nevertheless, we will add an ablation that compares the full model against a variant trained to output only answer sets (without interpretations) to quantify the contribution of the interpretation component. revision: yes
Referee: [Human Evaluation] Human evaluation subsection: the protocol for judging whether interpretations are 'meaningful' and 'explain their corresponding answers' is not described, nor is inter-annotator agreement reported. Without these details the claim that the interpretations are non-trivial cannot be assessed.

Authors: We will expand the human evaluation section to describe the full annotation protocol, including the exact instructions and rating scales provided to annotators, the number of annotators per example, and how 'meaningful' and 'explanatory' were operationalized. We will also report inter-annotator agreement (e.g., Fleiss' kappa or average pairwise agreement) for the key judgments. revision: yes

Circularity Check

0 steps flagged

No circularity: dual-reward RL defined independently from answer-set supervision

full rationale

The paper defines its dual-reward RL objective using standard recall (on ambiguous inputs to cover valid answers) and precision (on unambiguous inputs to suppress extras), with supervision consisting solely of multiple valid answers per input. No equations or training steps reduce the claimed coverage improvement to a fitted parameter, self-referential quantity, or prior self-citation chain. The method is evaluated empirically on held-out conversational QA and semantic parsing benchmarks, and the structured output format is presented as a direct consequence of the RL objective rather than a renaming or ansatz smuggled from prior work. The derivation remains self-contained against external answer-set benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The method rests on standard assumptions of reinforcement learning from human feedback and the availability of multiple valid answers as supervision; no new entities or ad-hoc axioms are introduced in the abstract.

pith-pipeline@v0.9.0 · 5435 in / 1064 out tokens · 34706 ms · 2026-05-17T22:08:44.133436+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We train our model using DAPO ... For ambiguous questions (|A|>1), our reward function is recall: Rrecall = 1/|A| Σ sim(pi, âj) ... For unambiguous questions ... precision: Rprecision = 1/|P| Σ sim(pi, âj)
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

A question is ambiguous if it admits multiple distinct interpretations that lead to different answers.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 1 internal anchor

[1]

InProceedings of the 61st Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 7997–8012, Toronto, Canada

Query refinement prompts for closed- book long-form QA. InProceedings of the 61st Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 7997–8012, Toronto, Canada. Associa- tion for Computational Linguistics. Adithya Bhaskar, Tushar Tomar, Ashutosh Sathe, and Sunita Sarawagi. 2023. Benchmarking and improving text...

work page 2023
[2]

Language models identify ambigui- ties and exploit loopholes.arXiv preprint arXiv:2508.19546. Tri Dao. 2024. FlashAttention-2: Faster attention with better parallelism and work partitioning. InInternational Conference on Learning Rep- resentations (ICLR). Zhongjun Ding, Yin Lin, and Tianjing Zeng

work page arXiv 2024
[3]

Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu, Anuj Chauhan, Chung-Wei Hang, Shuaichen Chang, Lin Pan, Wuwei Lan, Henghui Zhu, Jiarong Jiang, Patrick Ng, and Zhiguo Wang

Ambisql: Interactive ambiguity detection and resolution for text-to-sql.arXiv preprint arXiv:2508.15276. Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu, Anuj Chauhan, Chung-Wei Hang, Shuaichen Chang, Lin Pan, Wuwei Lan, Henghui Zhu, Jiarong Jiang, Patrick Ng, and Zhiguo Wang. 2025. PRACTIQ: A practi- cal conversational text-to-SQL dataset with ambiguous and ...

work page arXiv 2025
[4]

Nan Huo, Xiaohan Xu, Jinyang Li, Per Jacobsson, Shipei Lin, Bowen Qin, Binyuan Hui, Xiaolong Li, Ge Qu, Shuzheng Si, et al

Qwen2.5-coder technical report. Nan Huo, Xiaohan Xu, Jinyang Li, Per Jacobsson, Shipei Lin, Bowen Qin, Binyuan Hui, Xiaolong Li, Ge Qu, Shuzheng Si, et al. 2025. Bird- interact: Re-imagining text-to-sql evaluation for large language models via lens of dynamic in- teractions.arXiv preprint arXiv:2510.05318. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redf...

work page arXiv 2025
[5]

InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6997–7008, On- line and Punta Cana, Dominican Republic

Joint passage ranking for diverse multi- answer retrieval. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6997–7008, On- line and Punta Cana, Dominican Republic. As- sociation for Computational Linguistics. Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020. AmbigQA: Answering ambigu...

work page 2021
[6]

InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 327–337, Abu Dhabi, UAE

POTATO: The portable text annotation tool. InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 327–337, Abu Dhabi, UAE. Association for Computational Linguistics. Steven T Piantadosi, Harry Tily, and Edward Gib- son. 2012. The communicative function of am- biguity in language.Cognition, 12...

work page 2022
[7]

InProceedings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing, pages 1601–1611, Singapore

Evaluating cross-domain text-to-SQL models and benchmarks. InProceedings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing, pages 1601–1611, Singapore. Association for Computational Lin- guistics. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020. Deep- speed: System optimizations enable training deep learni...

work page 2023
[8]

Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang

Spectrum tuning: Post-training for dis- tributional coverage and in-context steerability. Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2022. ASQA: Factoid ques- tions meet long-form answers. InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8273– 8288, Abu Dhabi, United Arab Emirates. Asso- ciat...

work page arXiv 2022
[9]

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Dapo: An open-source llm reinforce- ment learning system at scale.arXiv preprint arXiv:2503.14476. Jiayi Zhang, Simon Yu, Derek Chong, An- thony Sicilia, Michael R Tomz, Christopher D Manning, and Weiyan Shi. 2025a. Verbal- ized sampling: How to mitigate mode col- lapse and unlock llm diversity.arXiv preprint arXiv:2510.01171. Yiming Zhang, Harshita Didde...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[10]

All experiments were conducted on 5 NVIDIA H100 80GB GPUs: 1 GPU dedicated to the vLLM server for generation and 4 GPUs for training

for efficient generation during training. All experiments were conducted on 5 NVIDIA H100 80GB GPUs: 1 GPU dedicated to the vLLM server for generation and 4 GPUs for training. For evaluation, we use a single GPU, and choose the last checkpoint. We use default sam- pling parameters (recommended by the develop- ers) for Qwen 3 4B Instruct: temperature = 0.7...

work page 2022
[11]

open positions

Identify the relevant data tables:The question refers to “open positions”→likely refers to job roles listed in theJobstable. “Experience” in the context of jobs is mentioned in theMinimum_Yearsand Preferred_Yearscolumns in theJobstable.Minimum_Yearsindicates the minimum years of experience re- quired.Preferred_Yearsindicates the ideal or preferred years o...

work page
[12]

experience

Interpretation of “experience”:The term “experience” could mean either: theminimum years of experience required(i.e.,Minimum_Years), thepreferred years of experience(i.e.,Preferred_Years), or a combination of both (e.g., both minimum and preferred), or it could refer to thetypeof experience (e.g., in a field like software or data), but such information is...

work page
[13]

the open position with the greatest salary

Interpretation of “the open position with the greatest salary”:This could mean: the job with thehighest salary among all jobs, the job that has themaximum salary value. “Open position” simply means the job listed in the database—there’s no indication of a hiring status or closure, so we assume all jobs are open

work page
[14]

Scope of the question:It could be asking for: just theminimumexperience required (minimum years) for the job with the highest salary, just thepreferredexperience for that job, both minimum and preferred, or what kind of experience (e.g., in years) is needed—again, only years are specified

work page
[15]

experience

Potential ambiguity in “experience” vs. “years of experience”:“Experience” could be taken as a general term, but in the database schema, only years are provided (inMinimum_YearsandPreferred_Years). So interpretations must map “experience” to one or both of these years

work page
[16]

what experience do I need

Ambiguity in which experience (minimum or preferred):The question says “what experience do I need”, which may imply therequiredexperience (minimum), not just preferred. But in common usage, people may interpret “what experience do I need” as the minimum required. Alternatively, it could mean the preferred, or both

work page
[17]

the greatest salary

Ambiguity in what “the greatest salary” refers to:It could refer to: the single job with the highest salary value, or the job where salary is max, and then extract its experience. Conclusion:The question is ambiguous in: whether “experience” refers to minimum, preferred, or both years; whether it refers to theminimumorpreferredexperience required; whether...

work page 2010
[18]

her husband

Mike Comrie— described as Hilary Duff’s husband, a former pro hockey player. He is referred to as “her husband” and is mentioned as the one she is separated from. He is the only male mentioned in a marital relationship with Hilary Duff. His name is explicitly stated as “Mike Comrie.”

work page
[19]

His” could refer to Luca, but “his name

Luca— described as their son, a male child. “His” could refer to Luca, but “his name” would then be “Luca.” The question “What is his name?” lacks clarity on which male entity is being referred to. Possible interpretations: •“His” could refer to Mike Comrie (the husband), whose name is Mike Comrie. •“His” could refer to the person who had a child — Hilary...

work page 2012

[1] [1]

InProceedings of the 61st Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 7997–8012, Toronto, Canada

Query refinement prompts for closed- book long-form QA. InProceedings of the 61st Annual Meeting of the Association for Compu- tational Linguistics (Volume 1: Long Papers), pages 7997–8012, Toronto, Canada. Associa- tion for Computational Linguistics. Adithya Bhaskar, Tushar Tomar, Ashutosh Sathe, and Sunita Sarawagi. 2023. Benchmarking and improving text...

work page 2023

[2] [2]

Language models identify ambigui- ties and exploit loopholes.arXiv preprint arXiv:2508.19546. Tri Dao. 2024. FlashAttention-2: Faster attention with better parallelism and work partitioning. InInternational Conference on Learning Rep- resentations (ICLR). Zhongjun Ding, Yin Lin, and Tianjing Zeng

work page arXiv 2024

[3] [3]

Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu, Anuj Chauhan, Chung-Wei Hang, Shuaichen Chang, Lin Pan, Wuwei Lan, Henghui Zhu, Jiarong Jiang, Patrick Ng, and Zhiguo Wang

Ambisql: Interactive ambiguity detection and resolution for text-to-sql.arXiv preprint arXiv:2508.15276. Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu, Anuj Chauhan, Chung-Wei Hang, Shuaichen Chang, Lin Pan, Wuwei Lan, Henghui Zhu, Jiarong Jiang, Patrick Ng, and Zhiguo Wang. 2025. PRACTIQ: A practi- cal conversational text-to-SQL dataset with ambiguous and ...

work page arXiv 2025

[4] [4]

Nan Huo, Xiaohan Xu, Jinyang Li, Per Jacobsson, Shipei Lin, Bowen Qin, Binyuan Hui, Xiaolong Li, Ge Qu, Shuzheng Si, et al

Qwen2.5-coder technical report. Nan Huo, Xiaohan Xu, Jinyang Li, Per Jacobsson, Shipei Lin, Bowen Qin, Binyuan Hui, Xiaolong Li, Ge Qu, Shuzheng Si, et al. 2025. Bird- interact: Re-imagining text-to-sql evaluation for large language models via lens of dynamic in- teractions.arXiv preprint arXiv:2510.05318. Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redf...

work page arXiv 2025

[5] [5]

InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6997–7008, On- line and Punta Cana, Dominican Republic

Joint passage ranking for diverse multi- answer retrieval. InProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6997–7008, On- line and Punta Cana, Dominican Republic. As- sociation for Computational Linguistics. Sewon Min, Julian Michael, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2020. AmbigQA: Answering ambigu...

work page 2021

[6] [6]

InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 327–337, Abu Dhabi, UAE

POTATO: The portable text annotation tool. InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 327–337, Abu Dhabi, UAE. Association for Computational Linguistics. Steven T Piantadosi, Harry Tily, and Edward Gib- son. 2012. The communicative function of am- biguity in language.Cognition, 12...

work page 2022

[7] [7]

InProceedings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing, pages 1601–1611, Singapore

Evaluating cross-domain text-to-SQL models and benchmarks. InProceedings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing, pages 1601–1611, Singapore. Association for Computational Lin- guistics. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020. Deep- speed: System optimizations enable training deep learni...

work page 2023

[8] [8]

Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang

Spectrum tuning: Post-training for dis- tributional coverage and in-context steerability. Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, and Ming-Wei Chang. 2022. ASQA: Factoid ques- tions meet long-form answers. InProceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8273– 8288, Abu Dhabi, United Arab Emirates. Asso- ciat...

work page arXiv 2022

[9] [9]

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Dapo: An open-source llm reinforce- ment learning system at scale.arXiv preprint arXiv:2503.14476. Jiayi Zhang, Simon Yu, Derek Chong, An- thony Sicilia, Michael R Tomz, Christopher D Manning, and Weiyan Shi. 2025a. Verbal- ized sampling: How to mitigate mode col- lapse and unlock llm diversity.arXiv preprint arXiv:2510.01171. Yiming Zhang, Harshita Didde...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[10] [10]

All experiments were conducted on 5 NVIDIA H100 80GB GPUs: 1 GPU dedicated to the vLLM server for generation and 4 GPUs for training

for efficient generation during training. All experiments were conducted on 5 NVIDIA H100 80GB GPUs: 1 GPU dedicated to the vLLM server for generation and 4 GPUs for training. For evaluation, we use a single GPU, and choose the last checkpoint. We use default sam- pling parameters (recommended by the develop- ers) for Qwen 3 4B Instruct: temperature = 0.7...

work page 2022

[11] [11]

open positions

Identify the relevant data tables:The question refers to “open positions”→likely refers to job roles listed in theJobstable. “Experience” in the context of jobs is mentioned in theMinimum_Yearsand Preferred_Yearscolumns in theJobstable.Minimum_Yearsindicates the minimum years of experience re- quired.Preferred_Yearsindicates the ideal or preferred years o...

work page

[12] [12]

experience

Interpretation of “experience”:The term “experience” could mean either: theminimum years of experience required(i.e.,Minimum_Years), thepreferred years of experience(i.e.,Preferred_Years), or a combination of both (e.g., both minimum and preferred), or it could refer to thetypeof experience (e.g., in a field like software or data), but such information is...

work page

[13] [13]

the open position with the greatest salary

Interpretation of “the open position with the greatest salary”:This could mean: the job with thehighest salary among all jobs, the job that has themaximum salary value. “Open position” simply means the job listed in the database—there’s no indication of a hiring status or closure, so we assume all jobs are open

work page

[14] [14]

Scope of the question:It could be asking for: just theminimumexperience required (minimum years) for the job with the highest salary, just thepreferredexperience for that job, both minimum and preferred, or what kind of experience (e.g., in years) is needed—again, only years are specified

work page

[15] [15]

experience

Potential ambiguity in “experience” vs. “years of experience”:“Experience” could be taken as a general term, but in the database schema, only years are provided (inMinimum_YearsandPreferred_Years). So interpretations must map “experience” to one or both of these years

work page

[16] [16]

what experience do I need

Ambiguity in which experience (minimum or preferred):The question says “what experience do I need”, which may imply therequiredexperience (minimum), not just preferred. But in common usage, people may interpret “what experience do I need” as the minimum required. Alternatively, it could mean the preferred, or both

work page

[17] [17]

the greatest salary

Ambiguity in what “the greatest salary” refers to:It could refer to: the single job with the highest salary value, or the job where salary is max, and then extract its experience. Conclusion:The question is ambiguous in: whether “experience” refers to minimum, preferred, or both years; whether it refers to theminimumorpreferredexperience required; whether...

work page 2010

[18] [18]

her husband

Mike Comrie— described as Hilary Duff’s husband, a former pro hockey player. He is referred to as “her husband” and is mentioned as the one she is separated from. He is the only male mentioned in a marital relationship with Hilary Duff. His name is explicitly stated as “Mike Comrie.”

work page

[19] [19]

His” could refer to Luca, but “his name

Luca— described as their son, a male child. “His” could refer to Luca, but “his name” would then be “Luca.” The question “What is his name?” lacks clarity on which male entity is being referred to. Possible interpretations: •“His” could refer to Mike Comrie (the husband), whose name is Mike Comrie. •“His” could refer to the person who had a child — Hilary...

work page 2012