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What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance

Paper · 2026 · William Watson, Nicole Cho, Sumitra Ganesh et al.

Summary

The paper asks whether the phrasing of a query, and not only the model or its decoding, affects how often LLMs hallucinate. Drawing on linguistics, the authors define 17 binary query properties that are known to make text harder for humans to understand. These include lack of specificity, clause complexity, anaphora, negation, polysemy, answerability and intention grounding. An LLM labels these properties with few-shot prompts across 369,837 real-world queries from 13 QA datasets.

Benchmark items may be memorized, so each query is replaced by six filtered paraphrases. Each paraphrased answer is scored with a combined LLM-judge and string-similarity proxy, and the number of failures places the query in one of three classes: Safe, Borderline or Risky. A proportional-odds model with dataset and scenario fixed effects, together with ECDF comparisons, links features to risk.

Vague and syntactically intricate queries are associated with more hallucination. Answerable queries with explicit intent are associated with less. Several features that typically trouble human readers, such as rare words and superlatives, have little association with hallucination. The findings support triaging and rewriting queries before generation, though the evidence is correlational rather than causal.

Key points

  • Data and labels:
    • The corpus has 369,837 queries from 13 QA datasets, covering 3 scenarios and 16 configurations.
    • Paraphrases are kept only if their hybrid similarity is at least 0.85. This similarity combines text-embedding-3-large and the MS-MARCO-MiniLM-L6-v2 cross-encoder.
    • The hallucination proxy weights its components 0.6 (LLM judge), 0.3 (fuzzy match) and 0.1 (BLEU-1).
    • Labels depend on how many of the six paraphrases fail: Safe means 0/6, Borderline 1–3/6, and Risky 4–6/6.
  • Strongest risk factors:
    • Lack of Specificity has a coefficient of 0.868 (OR 2.382).
    • Clause Complexity has a coefficient of 0.568 (OR 1.764).
    • Negation (0.311) and Anaphora (0.214) show weaker positive effects.
  • Protective factors:
    • Answerability has the largest negative coefficient, −1.106 (OR 0.331).
    • Intention Grounding has a coefficient of −0.168.
    • Numeric measures (Query Token Length, Number of Clauses, Dependency Depth) are also inversely related to risk.
  • Mixed or null effects:
    • Domain Specificity is near zero (0.003) and varies by dataset and model.
    • Named Entities Present is not significant. The paper reports both p = 0.205 and an adjusted p = 0.115.
    • Related Work mentions 22 features, while the method describes 17 binary features plus numeric syntactic measures.
  • Practical guidance and limitations:
    • The authors suggest routing high-risk queries to a clarification step or to retrieval.
    • They also suggest low-effort rewrites: add constraints such as time, place or entity; state intent explicitly; and disambiguate polysemous words. These rewrites matter most for short, open-ended prompts.
    • The study covers English only and is observational.
    • It relies on an LLM judge and does not model interactions between features.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksInformation Systems · 1,730 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,894 works

Cite

Cite as William Watson, Nicole Cho, Sumitra Ganesh and Manuela Veloso. 2026. What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.251

@inproceedings{watson2026what,
  title = {{What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance}},
  author = {William Watson, Nicole Cho, Sumitra Ganesh and Manuela Veloso},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.251},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.251}
}

References

80 references, 48 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, and Siva Reddy. 2024. Evaluating correctness and faithfulness of instruction-following models for question answering. Preprint, arXiv:2307.16877.
  2. Michael Aerni, Javier Rando, Edoardo Debenedetti, Nicholas Carlini, Daphne Ippolito, and Florian Tramèr. 2024. Measuring non-adversarial reproduction of training data in large language models. Preprint, arXiv:2411.10242. — [arXiv:2411.10242](https://arxiv.org/abs/2411.10242) find
  3. Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019. MathQA: Towards interpretable math word problem solving with operation-based formalisms. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computation…
  4. Chenxin An, Jun Zhang, Ming Zhong, Lei Li, Shansan Gong, Yao Luo, Jingjing Xu, and Lingpeng Kong. 2024. Why does the effective context length of llms fall short? Preprint, arXiv:2410.18745. — [arXiv:2410.18745](https://arxiv.org/abs/2410.18745) find
  5. Max Bachmann. 2024. rapidfuzz/rapidfuzz: Release 3.8.1. find
  6. Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020. Piqa: Reasoning about physical commonsense in natural language. In Thirty-Fourth AAAI Conference on Artificial Intelligence.
  7. Terra Blevins, Hila Gonen, and Luke Zettlemoyer. 2023. Prompting language models for linguistic structure. Preprint, arXiv:2211.07830. find
  8. Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter,… — Language Models are Few-Shot Learners
  9. Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006. Re-evaluating the role of Bleu in machine translation research. In 11th Conference of the European Chapter of the Association for Computational Linguistics, pages 249–256, Trento, Italy. Association for Computational Linguistics.
  10. Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel. 2021. Extracting training data from large language models. Preprint, arXiv:2012.07805.
  11. Nicole Cho and William Watson. 2025. Multiq&a: An analysis in measuring robustness via automated crowdsourcing of question perturbations and answers. Preprint, arXiv:2502.03711. — [arXiv:2502.03711](https://arxiv.org/abs/2502.03711) find
  12. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. Boolq: Exploring the surprising difficulty of natural yes/no questions. In NAACL.
  13. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv:1803.05457v1.
  14. Charles L. A. Clarke, Nick Craswell, and Ian Soboroff. 2009. Overview of the TREC 2009 web track. In Proceedings of The Eighteenth Text REtrieval Conference, TREC 2009, Gaithersburg, Maryland, USA, November 17-20, 2009, volume 500-278 of NIST Special Publication. National Institute of Standards and… find
  15. Scott A. Crossley and Stephen Skalicky. 2017. Making sense of polysemy relations in first and second language speakers of english. International Journal of Bilingualism, 23(2):400–416. (Original work published 2019). find
  16. Matthew Dahl, Varun Magesh, Mirac Suzgun, and Daniel E. Ho. 2024. Large legal fictions: Profiling legal hallucinations in large language models. Preprint, arXiv:2401.01301.
  17. Shizhe Diao, Pengcheng Wang, Yong Lin, Rui Pan, Xiang Liu, and Tong Zhang. 2024. Active prompting with chain-of-thought for large language models. Preprint, arXiv:2302.12246. find
  18. Janosch Haber and Massimo Poesio. 2024. Polysemy— Evidence from linguistics, behavioral science, and contextualized language models. Computational Linguistics, 50(1):351–417.
  19. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring massive multitask language understanding. Proceedings of the International Conference on Learning Representations (ICLR).
  20. Matthew Honnibal, Ines Montani, Sofie Van Lan-deghem, and Adriane Boyd. 2020. spacy: Industrial-strength natural language processing in python. https://spacy.io/. Accessed 2025-10-05. ↗
  21. Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems,…
  22. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023a. Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12):1–38.
  23. Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung. 2023b. Towards mitigating LLM hallucination via self reflection. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1827–1843, Singapore. Association for Computational Linguistics.
  24. Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Xing Wang, Shuming Shi, and Zhaopeng Tu. 2023. Is chatgpt a good translator? yes with gpt-4 as the engine. Preprint, arXiv:2301.08745.
  25. Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. arXiv e-prints, arXiv:1705.03551.
  26. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Onli…
  27. Nora Kassner and Hinrich Schütze. 2020. Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7811–7818, Online. Association for Computational Linguistics.
  28. Muhammad Ali Khalidi. 2023. Domain specificity. In Cognitive Ontology: Taxonomic Practices in the Mind-Brain Sciences, pages 100–122. Cambridge University Press. find
  29. Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, and Taeuk Kim. 2024. Aligning language models to explicitly handle ambiguity. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1989–2007, Miami, Florid… find
  30. Diederik P. Kingma and Jimmy Ba. 2017. Adam: A method for stochastic optimization. Preprint, arXiv:1412.6980.
  31. Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023. Clam: Selective clarification for ambiguous questions with generative language models. Preprint, arXiv:2212.07769. find
  32. Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale Fung, Mohammad Shoeybi, and Bryan Catanzaro. 2023. Factuality enhanced language models for open-ended text generation. Preprint, arXiv:2206.04624.
  33. Stephen C. Levinson. 1983. Pragmatics. Cambridge Textbooks in Linguistics. Cambridge University Press. find
  34. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. Retrieval-augmented generation for knowledge-intensive nlp tasks. Preprint, arXiv:2005.11401.
  35. Richard L. Lewis, Shravan Vasishth, and Julie A. Van Dyke. 2006. Computational principles of working memory in sentence comprehension. Trends in Cognitive Sciences, 10(10):447–454. find
  36. Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024. The dawn after the dark: An empirical study on factuality hallucination in large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long…
  37. Chin-Yew Lin and Franz Josef Och. 2004. ORANGE: a method for evaluating automatic evaluation metrics for machine translation. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics, pages 501–507, Geneva, Switzerland. COLING.
  38. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics…
  39. Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, and Yejin Choi. 2023a. We‘re afraid language models aren‘t modeling ambiguity. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 790–807, S…
  40. Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023b. G-eval: Nlg evaluation using gpt-4 with better human alignment. Preprint, arXiv:2303.16634.
  41. Brian MacWhinney, Elizabeth Bates, and Reinhold Kliegl. 1984. Cue validity and sentence interpretation in english, german, and italian. Journal of Verbal Learning and Verbal Behavior, 23(2):127–150. find
  42. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023. Self-refine: Iterative refinement with… find
  43. Potsawee Manakul, Adian Liusie, and Mark Gales. 2023. SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9004–9017, Singapore. Association for Computation… find
  44. William Mann and Sandra Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text, 8(3):243–281.
  45. Klara Marton, Richard G. Schwartz, Lajos Farkas, and Victoria Katsnelson. 2006. Effect of sentence length and complexity on working memory performance in hungarian children with specific language impairment (sli): A cross-linguistic comparison. International Journal of Language & Communication Disor… find
  46. Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018. Can a suit of armor conduct electricity? a new dataset for open book question answering. In Conference on Empirical Methods in Natural Language Processing. find
  47. Dan Milmo. 2023. Two US Lawyers Fined for Submitting Fake Court Citations from ChatGPT. The Guardian. Accessed: 2025-03-21. find
  48. Milad Nasr, Nicholas Carlini, Jonathan Hayase, Matthew Jagielski, A. Feder Cooper, Daphne Ippolito, Christopher A. Choquette-Choo, Eric Wallace, Florian Tramèr, and Katherine Lee. 2023. Scalable extraction of training data from (production) language models. Preprint, arXiv:2311.17035.
  49. Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2024. A comprehensive overview of large language models. Preprint, arXiv:2307.06435.
  50. Shereen Oraby, Vrindavan Harrison, Amita Misra, Ellen Riloff, and Marilyn Walker. 2017. Are you serious?: Rhetorical questions and sarcasm in social media dialog. In The 17th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL), Saarbrucken, Germany.
  51. Yasuhiro Ozuru, Stephen Briner, Christopher A. Kurby, and Danielle S. McNamara. 2013. Comparing comprehension measured by multiple-choice and open-ended questions. Canadian Journal of Experimental Psychology, 67(3):215–227. find
  52. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational…
  53. Friedemann Pulvermüller and Yury Shtyrov. 2006. Language outside the focus of attention: The mismatch negativity as a tool for studying higher cognitive processes. Progress in Neurobiology, 79(1):49–71. find
  54. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2023. Toolllm: Facilitating large language models to master 16000+ real…
  55. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don’t know: Unanswerable questions for squad. Preprint, arXiv:1806.03822.
  56. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Squad: 100,000+ questions for machine comprehension of text. Preprint, arXiv:1606.05250.
  57. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
  58. Jerrold M. Sadock and Arnold M. Zwicky. 1985. Speech acts distinctions in syntax. In Timothy Shopen, editor, Language Typology and Syntactic Description, pages 155–196. Cambridge University Press, Cambridge. find
  59. Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024. A systematic survey of prompt engineering in large language models: Techniques and applications. Preprint, arXiv:2402.07927.
  60. Silke Scheible. 2008. Annotating superlatives. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC‘08), Marrakech, Morocco. European Language Resources Association (ELRA). find
  61. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. Preprint, arXiv:2302.04761.
  62. Timo Schick and Hinrich Schütze. 2019. Rare words: A major problem for contextualized embeddings and how to fix it by attentive mimicking. Preprint, arXiv:1904.06707. — [arXiv:1904.06707](https://arxiv.org/abs/1904.06707) find
  63. Ethel Schuster. 1988. Anaphoric reference to events and actions: A representation and its advantages. In Coling Budapest 1988 Volume 2: International Conference on Computational Linguistics. find
  64. Skipper Seabold and Josef Perktold. 2010. statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference.
  65. Settaluri Sravanthi, Meet Doshi, Pavan Tankala, Rudra Murthy, Raj Dabre, and Pushpak Bhattacharyya. 2024. PUB: A pragmatics understanding benchmark for assessing LLMs’ pragmatics capabilities. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12075–12097, Bangkok, Thailan…
  66. Rhea Sukthanker, Soujanya Poria, Erik Cambria, and Ramkumar Thirunavukarasu. 2018. Anaphora and coreference resolution: A review. Preprint, arXiv:1805.11824. find
  67. Thinh Hung Truong, Timothy Baldwin, Karin Verspoor, and Trevor Cohn. 2023. Language models are not naysayers: An analysis of language models on negation benchmarks. Preprint, arXiv:2306.08189.
  68. Rob A. Van der Sandt. 1992. Presupposition projection as anaphora resolution. Journal of Semantics, 9(4):333–377. find
  69. Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023. A stitch in time saves nine: Detecting and mitigating hallucinations of llms by validating low-confidence generation. Preprint, arXiv:2307.03987.
  70. Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2019. SuperGLUE: A stickier benchmark for general-purpose language understanding systems. arXiv preprint 1905.00537.
  71. Jiaan Wang, Yunlong Liang, Fandong Meng, Zengkui Sun, Haoxiang Shi, Zhixu Li, Jinan Xu, Jianfeng Qu, and Jie Zhou. 2023a. Is ChatGPT a good NLG evaluator? a preliminary study. In Proceedings of the 4th New Frontiers in Summarization Workshop, pages 1–11, Singapore. Association for Computational Ling…
  72. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023b. Self-consistency improves chain of thought reasoning in language models. Preprint, arXiv:2203.11171.
  73. William Watson, Nicole Cho, and Nishan Srishankar. 2025. Is there no such thing as a bad question? h4r: Hallucibot for ratiocination, rewriting, ranking, and routing. Proceedings of the AAAI Conference on Artificial Intelligence, 39(24):25470–25478. find
  74. William Watson, Nicole Cho, Nishan Srishankar, Zhen Zeng, Lucas Cecchi, Daniel Scott, Suchetha Siddagangappa, Rachneet Kaur, Tucker Balch, and Manuela Veloso. 2024. Law: Legal agentic workflows for custody and fund services contracts. Preprint, arXiv:2412.11063. find
  75. Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017. Crowdsourcing multiple choice science questions. Preprint, arXiv:1707.06209.
  76. Yi Yang, Wen-tau Yih, and Christopher Meek. 2015. WikiQA: A challenge dataset for open-domain question answering. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2013–2018, Lisbon, Portugal. Association for Computational Linguistics.
  77. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2369…
  78. Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi, Shayleen Reynolds, Tucker Balch, and Manuela Veloso. 2024. Flowmind: Automatic workflow generation with llms. Preprint, arXiv:2404.13050. — [arXiv:2404.13050](https://arxiv.org/abs/2404.13050) find
  79. Biao Zhang, Ivan Titov, and Rico Sennrich. 2021. Sparse attention with linear units. Preprint, arXiv:2104.07012. find
  80. Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, and Tat-Seng Chua. 2024. Clamber: A benchmark of identifying and clarifying ambiguous information needs in large language models. Preprint, arXiv:2405.12063.

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