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home / papers / 2023 / Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources

Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources

Paper · 2023 · Xingxuan Li, Ruochen Zhao, Yew Ken Chia et al.

Summary

Large language models often produce fluent but factually wrong reasoning, especially on knowledge-heavy questions. Earlier retrieval-augmented methods such as Verify-and-Edit and ReAct have three weaknesses. They query one fixed source, they struggle to write structured queries, and they let mistakes from early reasoning steps carry into later ones. Chain-of-Knowledge (CoK) tackles these problems in three stages:

  1. It samples chain-of-thought rationales and picks the relevant knowledge domains.
  2. When self-consistency voting shows no clear majority, it fixes the rationales one at a time. Each step uses evidence retrieved from domain-specific sources, and the corrected earlier steps guide the generation of the next one.
  3. It derives the final answer from the repaired chain.

An adaptive query generator (AQG) writes SPARQL, SQL or natural-language queries for each source. The AQG can be a LoRA-tuned Llama-2 or ChatGPT. The sources span factual, medical, physics and biology domains and include both structured and unstructured data. Across these domains, CoK beats the CoT baseline by 4.3% on average. Its token cost is close to Verify-and-Edit and much lower than ReAct. This makes CoK a practical, modular way to ground LLM answers in verifiable knowledge.

Key points

  • Knowledge sources by domain:
    • Factual: Wikidata (SPARQL), Wikipedia, tables (SQL)
    • Medical: Flashcard, UpToDate
    • Physics: ScienceQA Physics, PhysicsClassroom
    • Biology: ScienceQA Biology, CK-12
  • Selective editing: Only low-consistency predictions are corrected. With the CoT-SC threshold, this covered 86 of 308 HotpotQA instances and 127 of 1,000 FEVER instances.
  • Cost: Input tokens per instance are 787 for HotpotQA and 329 for FEVER. This is about 40% of ReAct's cost (1638 and 848) and close to Verify-and-Edit (630 and 286).
  • Human study: Annotators compared CoK with CoT-SC reasoning chains on 100 samples from HotpotQA and FEVER. Among CoK's wrong predictions, 44% still had improved chains, and 73% of those were judged likely to support better answers. The remaining failures came from the LLM's own reasoning errors.
  • Availability: Code is released at github.com/DAMO-NLP-SG/chain-of-knowledge. The framework is modular, so both the AQG and the backbone LLM can be swapped.

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

🏢Alibaba GroupHangzhou · China201 works · 17,173 citations🎓Nanyang Technological UniversitySingapore · Singapore184 works · 27,484 citations🎓Singapore University of Technology and DesignSingapore · Singapore54 works · 11,534 citations🏢Alibaba DAMO AcademyHangzhou · China45 works · 6,874 citations

 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 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,894 worksReasoning · 1,685 works

Cite

Cite as Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria and Lidong Bing. 2023. Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources. arXiv:2305.13269.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.13269 · arXiv 2305.13269

@misc{li2023chain,
  title = {{Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources}},
  author = {Xingxuan Li, Ruochen Zhao, Yew Ken Chia, Bosheng Ding, Shafiq Joty, Soujanya Poria and Lidong Bing},
  year = {2023},
  doi = {10.48550/arxiv.2305.13269},
  eprint = {2305.13269},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.13269}
}

References

40 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. Autoregressive entity retrieval. In Proceedings of ICLR, 2021. find
  2. Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, and Hanwang Zhang. KQA pro: A dataset with explicit compositional programs for complex question answering over knowledge base. In Proceedings of ACL, 2022. find
  3. Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. Reading Wikipedia to answer open-domain questions. In Proceedings of ACL, 2017.
  4. Hailin Chen, Fangkai Jiao, Xingxuan Li, Chengwei Qin, Mathieu Ravaut, Ruochen Zhao, Caiming Xiong, and Shafiq Joty. Chatgpt’s one-year anniversary: Are open-source large language models catching up? arXiv preprint arXiv:2311.16989, 2024. — [arXiv:2311.16989](https://arxiv.org/abs/2311.16989) find
  5. Liying Cheng, Xingxuan Li, and Lidong Bing. Is gpt-4 a good data analyst? arXiv:2305.15038, 2023. find
  6. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
  7. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416, 2022. find
  8. Bosheng Ding, Chengwei Qin, Linlin Liu, Lidong Bing, Shafiq Joty, and Boyang Li. Is gpt-3 a good data annotator? In Proceedings of ACL, 2023.
  9. Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. Can machine translation systems be evaluated by the crowd alone. Natural Language Engineering, 2017. find
  10. Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. Retrieval augmented language model pre-training. In Proceedings of ICML, 2020. — REALM: Retrieval-Augmented Language Model Pre-Training
  11. Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexander Löser, Daniel Truhn, and Keno K. Bressem. Medalpaca – an open-source collection of medical conversational ai models and training data. arXiv preprint arXiv:2304.08247, 2023.
  12. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. In Proceedings of ICLR, 2021.
  13. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021.
  14. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation. ACM Computing Surveys, 2023.
  15. Tom Kocmi, Rachel Bawden, Ondřej Bojar, Anton Dvorkovich, Christian Federmann, Mark Fishel, Thamme Gowda, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Rebecca Knowles, Philipp Koehn, Christof Monz, Makoto Morishita, Masaaki Nagata, Toshiaki Nakazawa, Michal Novák, Martin Popel, and Maja Popovi… find
  16. 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. Retrieval-augmented generation for knowledge-intensive nlp tasks. In Proceedings of NIPS, 2020.
  17. Hao Liu, Carmelo Sferrazza, and Pieter Abbeel. Chain of hindsight aligns language models with feedback. arXiv preprint arXiv:2302.02676, 2023.
  18. Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. In Proceedings of NIPS, 2022. find
  19. Ziyang Luo, Can Xu, Pu Zhao, Xiubo Geng, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. Augmented large language models with parametric knowledge guiding. arXiv preprint arXiv:2305.04757, 2023.
  20. Grégoire Mialon, Roberto Dessı̀, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom. Augmented language models: a survey. arXiv preprint arXiv:2302.07842, 2023.
  21. Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, and Dragomir Radev. FeTaQA: Free-form… — FeTaQA: Free-form Table Question Answering
  22. OpenAI. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  23. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. Training la… — Training language models to follow instructions with human feedback
  24. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. Medmcqa : A large-scale multi-subject multi-choice dataset for medical domain question answering. arXiv preprint arXiv:2203.14371, 2022.
  25. Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, and Preslav Nakov. Fact-checking complex claims with program-guided reasoning. In Proceedings of ACL, 2023.
  26. Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. KILT: a benchmark for knowledge intensive language tasks. In Proceedings of NAACL,…
  27. Timo Schick, Jane Dwivedi-Yu, Roberto Dessı̀, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761, 2023.
  28. Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580, 2023.
  29. Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. Replug: Retrieval-augmented black-box language models, 2023.
  30. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. FEVER: a large-scale dataset for fact extraction and VERification. In Proceedings of NAACL, 2018.
  31. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,… — Llama 2: Open Foundation and Fine-Tuned Chat Models
  32. Priyansh Trivedi, Gaurav Maheshwari, Mohnish Dubey, and Jens Lehmann. Lc-quad: A corpus for complex question answering over knowledge graphs. In Proceedings of ISWC, 2017. find
  33. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. In Proceedings of ICLR, 2023.
  34. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. In Proceedings of NIPS, 2022.
  35. Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zett… — UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
  36. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. HotpotQA: A dataset for diverse, explainable multi-hop question answering. In Proceedings of EMNLP, 2018.
  37. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In Proceedings of ICLR, 2023.
  38. Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, et al. Retrieving multimodal information for augmented generation: A survey. arXiv preprint arXiv:2303.10868, 2023a. find
  39. Ruochen Zhao, Xingxuan Li, Yew Ken Chia, Bosheng Ding, and Lidong Bing. Can chatgpt-like generative models guarantee factual accuracy? on the mistakes of new generation search engines. arXiv preprint arXiv:2304.11076, 2023b. — [arXiv:2304.11076](https://arxiv.org/abs/2304.11076) find
  40. Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. Verify-and-edit: A knowledge-enhanced chain-of-thought framework. In Proceedings of ACL, 2023c.

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