---
title: "Alignment for Efficient Tool Calling of Large Language Models"
description: "LLMs equipped with tools often misjudge when a tool is worth calling. 45 references, cited by 7 papers here."
authors: ["Hongshen Xu", "Zihan Wang", "Zichen Zhu", "Lei Pan", "Xingyu Chen", "Shuai Fan", "Lu Chen", "Kai Yu"]
year: 2025
venue: "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2025.emnlp-main.898/"
doi: "10.18653/v1/2025.emnlp-main.898"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2025-alignment-efficient-tool-calling-large/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/xu-2025-alignment-efficient-tool-calling-large/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2025](https://md.research.elamur.ai/free/en/papers/2025/page/6/) / Alignment for Efficient Tool Calling of Large Language Models

# Alignment for Efficient Tool Calling of Large Language Models

**Authors:** Hongshen Xu, [Zihan Wang](https://md.research.elamur.ai/free/en/authors/zihan-wang/), Zichen Zhu, Lei Pan, [Xingyu Chen](https://md.research.elamur.ai/free/en/authors/xingyu-chen/), Shuai Fan, [Lu Chen](https://md.research.elamur.ai/free/en/authors/lu-chen/) and [Kai Yu](https://md.research.elamur.ai/free/en/authors/kai-yu/) · **Year:** 2025 · **Venue:** [Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.emnlp-main.898/) · [PDF](https://aclanthology.org/2025.emnlp-main.898.pdf) · [DOI 10.18653/v1/2025.emnlp-main.898](https://doi.org/10.18653/v1/2025.emnlp-main.898) · [OpenAlex](https://openalex.org/W4416036183)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · [7](https://md.research.elamur.ai/free/en/papers/xu-2025-alignment-efficient-tool-calling-large/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLMs equipped with tools often misjudge when a tool is worth calling. They may call a calculator, search engine or stronger model for questions they could answer alone (over-tool-reliance), or they may skip a tool when they actually need it (overconfidence). Both errors waste time and money or lower accuracy. The authors treat this as a multi-objective alignment problem. Their preference ordering ranks a correct answer without tools highest, then a correct answer with tools, then the two incorrect variants. They score models with a utility that subtracts a tool-specific cost from accuracy.

The model's knowledge boundary is treated as graded rather than binary. They estimate it in two ways: by consistency across sampled answers, or by average accuracy against ground truth. These estimates become training data in two forms. In implicit modeling, the model learns threshold-based call/no-call behaviour. In explicit modeling, it outputs a confidence score alongside its answer.

Across arithmetic, open-domain QA and math reasoning settings, the method cuts unnecessary tool calls and improves utility. This makes tool-using agents cheaper and more sensible.

## Key points
- Evaluation metric: Utility = Acc − α·TR, where TR is the tool usage ratio. The cost α is set to 0.2 for a calculator, 0.4 for a search engine/RAG and 0.6 for an external LLM reasoner.
- There are three test scenarios: Arithmetic + Calculator, TriviaQA + RAG and Math + Reasoner.
- At every tool-invocation ratio, explicit confidence modeling (EXPLICIT-ABSOLUTE) beats uncertainty-based baselines, including agreement/consistency, P(True), raw logits and verbalized confidence.
- The auto_tool baseline calls tools at an almost uniform rate regardless of question difficulty. IMPLICIT-ABSOLUTE uses tools less as the model's accuracy on a question rises, and it reduces over-tool-reliance.
- Limitations: the method was tested with only three tools and only two open-source models. Closed models were excluded because some baselines require access to token logits.

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

## Institutions

- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 149 works · 16,161 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 2,869 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen and Kai Yu. 2025. Alignment for Efficient Tool Calling of Large Language Models. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2025alignment,
  title = {{Alignment for Efficient Tool Calling of Large Language Models}},
  author = {Hongshen Xu, Zihan Wang, Zichen Zhu, Lei Pan, Xingyu Chen, Shuai Fan, Lu Chen and Kai Yu},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.898},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.898}
}
```

- [← previous AFRIDOC-MT: Document-level MT Corpus for African Languages](https://md.research.elamur.ai/free/en/papers/alabi-2025-afridoc-mt-document-level-mt/)
- [next → An Annotated Corpus of Arabic Tweets for Hate Speech Analysis](https://md.research.elamur.ai/free/en/papers/zaghouani-2025-annotated-corpus-arabic-tweets-hate/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/6/)

## References

45 references, 24 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. [On the dangers of stochastic parrots: Can language models DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingka…](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)
3. Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou. 2023. [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://md.research.elamur.ai/free/en/papers/ding-2023-enhancing-chat-language-models-scaling/). ArXiv preprint, abs/2305.14233.
4. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
5. Leo Gao, John Schulman, and Jacob Hilton. 2023a. [Scaling laws for reward model overoptimization](https://md.research.elamur.ai/free/en/papers/gao-2022-scaling-laws-reward-model-overoptimization/). In International Conference on Machine Learning, pages 10835–10866. PMLR.
6. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. 2023b. [Retrieval-augmented generation for large language models: A survey](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/). arXiv preprint arXiv:2312.10997.
7. Gemini Team. 2023. [Gemini: A Family of Highly Capable Multimodal Models](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/). Preprint, arXiv:2312.11805.
8. Amelia Glaese, Nat McAleese, Maja Trebacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, et al. 2022. [Improving alignment of dialogue agents via targeted human judgements](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/). arXiv preprint arXiv:2209.14375.
9. Anchun Gui, Jian Li, Yong Dai, Nan Du, and Han Xiao. 2024. Look before you leap: Towards decision-aware and generalizable tool-usage for large language models. arXiv preprint arXiv:2402.16696. — [arXiv:2402.16696](https://arxiv.org/abs/2402.16696) [find](https://scholar.google.com/scholar?q=Anchun+Gui%2C+Jian+Li%2C+Yong+Dai%2C+Nan+Du%2C+and+Han+Xiao%2E+2024%2E+Look+before+you+leap%3A+Towards+decision%2Daware+and+generalizable+tool%2Dusage+for+large+language+models%2E+arXiv+preprint+arXiv%3A2402%2E16696%2E+%E2%80%94+%5BarXi)
10. Tanmay Gupta and Aniruddha Kembhavi. 2023. Visual programming: Compositional visual reasoning without training. In In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), pages 14953–14962. [find](https://scholar.google.com/scholar?q=Tanmay+Gupta+and+Aniruddha+Kembhavi%2E+2023%2E+Visual+programming%3A+Compositional+visual+reasoning+without+training%2E+In+In+Proceedings+of+the+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition+)
11. Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. 2023. [ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://md.research.elamur.ai/free/en/papers/hao-2023-toolkengpt-augmenting-frozen-language-models/). ArXiv preprint, abs/2305.11554.
12. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. [Measuring mathematical problem solving with the math dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). NeurIPS.
13. Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister. 2023. Tool documentation enables zero-shot tool-usage with large language models. ArXiv preprint, abs/2308.00675. [find](https://scholar.google.com/scholar?q=Cheng%2DYu+Hsieh%2C+Si%2DAn+Chen%2C+Chun%2DLiang+Li%2C+Yasuhisa+Fujii%2C+Alexander+Ratner%2C+Chen%2DYu+Lee%2C+Ranjay+Krishna%2C+and+Tomas+Pfister%2E+2023%2E+Tool+documentation+enables+zero%2Dshot+tool%2Dusage+with+large+language+m)
14. Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, et al. 2023. Metatool benchmark for large language models: Deciding whether to use tools and which to use. arXiv preprint arXiv:2310.03128. [find](https://scholar.google.com/scholar?q=Yue+Huang%2C+Jiawen+Shi%2C+Yuan+Li%2C+Chenrui+Fan%2C+Siyuan+Wu%2C+Qihui+Zhang%2C+Yixin+Liu%2C+Pan+Zhou%2C+Yao+Wan%2C+Neil+Zhenqiang+Gong%2C+et+al%2E+2023%2E+Metatool+benchmark+for+large+language+models%3A+Deciding+whether+to+u)
15. Qiao Jin, Yifan Yang, Qingyu Chen, and Zhiyong Lu. 2024. [GeneGPT: augmenting large language models with domain tools for improved access to biomedical information](https://md.research.elamur.ai/free/en/papers/jin-2023-genegpt-augmenting-large-language-models/). Bioinformatics, 40(2):btae075.
16. Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017. [Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/). arXiv preprint arXiv:1705.03551.
17. Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221. [find](https://scholar.google.com/scholar?q=Saurav+Kadavath%2C+Tom+Conerly%2C+Amanda+Askell%2C+Tom+Henighan%2C+Dawn+Drain%2C+Ethan+Perez%2C+Nicholas+Schiefer%2C+Zac+Hatfield%2DDodds%2C+Nova+DasSarma%2C+Eli+Tran%2DJohnson%2C+et+al%2E+2022%2E+Language+models+%28mostly%29+know+w)
18. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023. [Efficient memory management for large language model serving with pagedattention](https://md.research.elamur.ai/free/en/papers/kwon-2023-efficient-memory-management-large-language/). In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles.
19. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations. In Proceedings of the 44th International ACM SIGIR Conference on Research and Develop… [find](https://scholar.google.com/scholar?q=Jimmy+Lin%2C+Xueguang+Ma%2C+Sheng%2DChieh+Lin%2C+Jheng%2DHong+Yang%2C+Ronak+Pradeep%2C+and+Rodrigo+Nogueira%2E+2021%2E+Pyserini%3A+A+python+toolkit+for+reproducible+information+retrieval+research+with+sparse+and+dense+re)
20. Tiedong Liu and Bryan Kian Hsiang Low. 2023. Goat: Fine-tuned llama outperforms gpt-4 on arithmetic tasks. arXiv preprint arXiv:2305.14201. [find](https://scholar.google.com/scholar?q=Tiedong+Liu+and+Bryan+Kian+Hsiang+Low%2E+2023%2E+Goat%3A+Fine%2Dtuned+llama+outperforms+gpt%2D4+on+arithmetic+tasks%2E+arXiv+preprint+arXiv%3A2305%2E14201%2E)
21. Qing Lyu, Kumar Shridhar, Chaitanya Malaviya, Li Zhang, Yanai Elazar, Niket Tandon, Marianna Apidianaki, Mrinmaya Sachan, and Chris Callison-Burch. 2024. Calibrating large language models with sample consistency. arXiv preprint arXiv:2402.13904. — [arXiv:2402.13904](https://arxiv.org/abs/2402.13904) [find](https://scholar.google.com/scholar?q=Qing+Lyu%2C+Kumar+Shridhar%2C+Chaitanya+Malaviya%2C+Li+Zhang%2C+Yanai+Elazar%2C+Niket+Tandon%2C+Marianna+Apidianaki%2C+Mrinmaya+Sachan%2C+and+Chris+Callison%2DBurch%2E+2024%2E+Calibrating+large+language+models+with+sample+)
22. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. [Training language models to follow instructions with human feedback](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/). Advances in Neural Information Processing Systems, 35:27730–27744.
23. Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. 2023. [Gorilla: Large Language Model Connected with Massive APIs](https://md.research.elamur.ai/free/en/papers/patil-2023-gorilla-large-language-model-connected/). ArXiv preprint, abs/2305.15334.
24. Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, 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-world APIs](https://md.research.elamur.ai/free/en/papers/qin-2023-toolllm-facilitating-large-language-models/).…
25. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024. [Direct preference optimization: Your language model is secretly a reward model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). Advances in Neural Information Processing Systems, 36.
26. Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021. [Multitask prompted training enables zero-shot task generalization](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/). arXiv preprint arXiv:2110.08207.
27. 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](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/). ArXiv preprint, abs/2302.04761.
28. Irene Solaiman and Christy Dennison. 2021. Process for adapting language models to society (palms) with values-targeted datasets. Advances in Neural Information Processing Systems, 34:5861–5873. [find](https://scholar.google.com/scholar?q=Irene+Solaiman+and+Christy+Dennison%2E+2021%2E+Process+for+adapting+language+models+to+society+%28palms%29+with+values%2Dtargeted+datasets%2E+Advances+in+Neural+Information+Processing+Systems%2C+34%3A5861%E2%80%935873%2E)
29. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008– 3021. [find](https://scholar.google.com/scholar?q=Nisan+Stiennon%2C+Long+Ouyang%2C+Jeffrey+Wu%2C+Daniel+Ziegler%2C+Ryan+Lowe%2C+Chelsea+Voss%2C+Alec+Radford%2C+Dario+Amodei%2C+and+Paul+F+Christiano%2E+2020%2E+Learning+to+summarize+with+human+feedback%2E+Advances+in+Neural)
30. Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, and Le Sun. 2023. [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://md.research.elamur.ai/free/en/papers/tang-2023-toolalpaca-generalized-tool-learning-language/). Preprint, arXiv:2306.05301.
31. Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023. Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback. arXiv preprint arXiv:2305.14975. [find](https://scholar.google.com/scholar?q=Katherine+Tian%2C+Eric+Mitchell%2C+Allan+Zhou%2C+Archit+Sharma%2C+Rafael+Rafailov%2C+Huaxiu+Yao%2C+Chelsea+Finn%2C+and+Christopher+D+Manning%2E+2023%2E+Just+ask+for+calibration%3A+Strategies+for+eliciting+calibrated+conf)
32. Xinpeng Wang, Shitong Duan, Xiaoyuan Yi, Jing Yao, Shanlin Zhou, Zhihua Wei, Peng Zhang, Dongkuan Xu, Maosong Sun, and Xing Xie. 2024. On the essence and prospect: An investigation of alignment approaches for big models. arXiv preprint arXiv:2403.04204. — [arXiv:2403.04204](https://arxiv.org/abs/2403.04204) [find](https://scholar.google.com/scholar?q=Xinpeng+Wang%2C+Shitong+Duan%2C+Xiaoyuan+Yi%2C+Jing+Yao%2C+Shanlin+Zhou%2C+Zhihua+Wei%2C+Peng+Zhang%2C+Dongkuan+Xu%2C+Maosong+Sun%2C+and+Xing+Xie%2E+2024%2E+On+the+essence+and+prospect%3A+An+investigation+of+alignment+approa)
33. Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, et al. 2025. Thoughts are all over the place: On the underthinking of o1-like llms. arXiv preprint arXiv:2501.18585. [find](https://scholar.google.com/scholar?q=Yue+Wang%2C+Qiuzhi+Liu%2C+Jiahao+Xu%2C+Tian+Liang%2C+Xingyu+Chen%2C+Zhiwei+He%2C+Linfeng+Song%2C+Dian+Yu%2C+Juntao+Li%2C+Zhuosheng+Zhang%2C+et+al%2E+2025%2E+Thoughts+are+all+over+the+place%3A+On+the+underthinking+of+o1%2Dlike+ll)
34. Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2022. [Finetuned Language Models are Zero-Shot Learners](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/). In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview…
35. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. [WizardLM: Empowering Large Language Models to Follow Complex Instructions](https://md.research.elamur.ai/free/en/papers/xu-2023-wizardlm-empowering-large-pre-trained/). Preprint, arXiv:2304.12244.
36. Hongshen Xu, Su Zhu, Zihan Wang, Hang Zheng, Da Ma, Ruisheng Cao, Shuai Fan, Lu Chen, and Kai Yu. 2024a. Reducing tool hallucination via reliability alignment. arXiv preprint arXiv:2412.04141. — [arXiv:2412.04141](https://arxiv.org/abs/2412.04141) [find](https://scholar.google.com/scholar?q=Hongshen+Xu%2C+Su+Zhu%2C+Zihan+Wang%2C+Hang+Zheng%2C+Da+Ma%2C+Ruisheng+Cao%2C+Shuai+Fan%2C+Lu+Chen%2C+and+Kai+Yu%2E+2024a%2E+Reducing+tool+hallucination+via+reliability+alignment%2E+arXiv+preprint+arXiv%3A2412%2E04141%2E+%E2%80%94+%5BarXi)
37. Hongshen Xu, Zichen Zhu, Da Ma, Situo Zhang, Shuai Fan, Lu Chen, and Kai Yu. 2024b. Rejection improves reliability: Training llms to refuse unknown questions using rl from knowledge feedback. arXiv preprint arXiv:2403.18349. — [arXiv:2403.18349](https://arxiv.org/abs/2403.18349) [find](https://scholar.google.com/scholar?q=Hongshen+Xu%2C+Zichen+Zhu%2C+Da+Ma%2C+Situo+Zhang%2C+Shuai+Fan%2C+Lu+Chen%2C+and+Kai+Yu%2E+2024b%2E+Rejection+improves+reliability%3A+Training+llms+to+refuse+unknown+questions+using+rl+from+knowledge+feedback%2E+arXiv+pr)
38. Linyao Yang, Hongyang Chen, Zhao Li, Xiao Ding, and Xindong Wu. 2023a. ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling. ArXiv preprint, abs/2306.11489. [find](https://scholar.google.com/scholar?q=Linyao+Yang%2C+Hongyang+Chen%2C+Zhao+Li%2C+Xiao+Ding%2C+and+Xindong+Wu%2E+2023a%2E+ChatGPT+is+not+Enough%3A+Enhancing+Large+Language+Models+with+Knowledge+Graphs+for+Fact%2Daware+Language+Modeling%2E+ArXiv+preprint%2C+ab)
39. Yuchen Yang, Houqiang Li, Yanfeng Wang, and Yu Wang. 2023b. Improving the reliability of large language models by leveraging uncertainty-aware in-context learning. arXiv preprint arXiv:2310.04782. — [arXiv:2310.04782](https://arxiv.org/abs/2310.04782) [find](https://scholar.google.com/scholar?q=Yuchen+Yang%2C+Houqiang+Li%2C+Yanfeng+Wang%2C+and+Yu+Wang%2E+2023b%2E+Improving+the+reliability+of+large+language+models+by+leveraging+uncertainty%2Daware+in%2Dcontext+learning%2E+arXiv+preprint+arXiv%3A2310%2E04782%2E+%E2%80%94+%5B)
40. Yuqing Yang, Ethan Chern, Xipeng Qiu, Graham Neubig, and Pengfei Liu. 2023c. Alignment for honesty. arXiv preprint arXiv:2312.07000. [find](https://scholar.google.com/scholar?q=Yuqing+Yang%2C+Ethan+Chern%2C+Xipeng+Qiu%2C+Graham+Neubig%2C+and+Pengfei+Liu%2E+2023c%2E+Alignment+for+honesty%2E+arXiv+preprint+arXiv%3A2312%2E07000%2E)
41. Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, et al. 2023. Deepspeed-chat: Easy, fast and affordable rlhf training of chatgpt-like models at all scales. arXiv preprint arXiv:2308.01320. — [arXiv:2308.01320](https://arxiv.org/abs/2308.01320) [find](https://scholar.google.com/scholar?q=Zhewei+Yao%2C+Reza+Yazdani+Aminabadi%2C+Olatunji+Ruwase%2C+Samyam+Rajbhandari%2C+Xiaoxia+Wu%2C+Ammar+Ahmad+Awan%2C+Jeff+Rasley%2C+Minjia+Zhang%2C+Conglong+Li%2C+Connor+Holmes%2C+et+al%2E+2023%2E+Deepspeed%2Dchat%3A+Easy%2C+fast+an)
42. Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, et al. 2022. [Socratic models: Composing zero-shot multimodal reasoning with language](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/). arXiv preprint arXiv:2204.00598.
43. Shengyu Zhang, Linfeng Dong, Xiaoya Li, Sen Zhang, Xiaofei Sun, Shuhe Wang, Jiwei Li, Runyi Hu, Tianwei Zhang, Fei Wu, et al. 2023. [Instruction tuning for large language models: A survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/). arXiv preprint arXiv:2308.10792.
44. Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, et al. 2024. Toolbehonest: A multi-level hallucination diagnostic benchmark for tool-augmented large language models. arXiv preprint arXiv:2406.20015. [find](https://scholar.google.com/scholar?q=Yuxiang+Zhang%2C+Jing+Chen%2C+Junjie+Wang%2C+Yaxin+Liu%2C+Cheng+Yang%2C+Chufan+Shi%2C+Xinyu+Zhu%2C+Zihao+Lin%2C+Hanwen+Wan%2C+Yujiu+Yang%2C+et+al%2E+2024%2E+Toolbehonest%3A+A+multi%2Dlevel+hallucination+diagnostic+benchmark+for+)
45. Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang, Fei Huang, and Yang Liu. 2024. Budget-constrained tool learning with planning. arXiv preprint arXiv:2402.15960. [find](https://scholar.google.com/scholar?q=Yuanhang+Zheng%2C+Peng+Li%2C+Ming+Yan%2C+Ji+Zhang%2C+Fei+Huang%2C+and+Yang+Liu%2E+2024%2E+Budget%2Dconstrained+tool+learning+with+planning%2E+arXiv+preprint+arXiv%3A2402%2E15960%2E)

*Updated 30 October 2026*