Built on

Papineni, K., Roukos, S., Ward, T., Zhu, W. J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
2002
Earlier work this paper cites.
Lin, C. Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out, pp. 74-81 (2004)
2004
Earlier work this paper cites.
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., et al.: ELI5: Long form question answering. arXiv preprint arXiv:1907.09190 (2019)
Original
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, pp. 1877-1901 (2020)
2020
Earlier work this paper cites.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. In: Advances in Neural Information Processing Systems, pp. 9459-9474 (2020)
2020
Earlier work this paper cites.
Shuster, K., Poff, S., Chen, M., Kiela, D., Weston, J.: Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567 (2021)
Original
2021
Earlier work this paper cites.
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., et al.: Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332 (2021)
Original
2021
Earlier work this paper cites.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., et al.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)
Original
2021
Earlier work this paper cites.
Stelmakh, I., Luan, Y., Dhingra, B., Chang, M. W.: ASQA: Factoid questions meet long-form answers. arXiv preprint arXiv:2204.06092 (2022)
Original
2022
Earlier work this paper cites.
Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., et al.: Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147 (2022)
Original
2022
Earlier work this paper cites.
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., et al.: Rarr: Researching and revising what language models say, using language models. arXiv preprint arXiv:2210.08726 (2022)
Original
2022
Earlier work this paper cites.
Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., et al.: Attributed question answering: Evaluation and modeling for attributed large language models. arXiv preprint arXiv:2212.08037 (2022)
Original
2022
Earlier work this paper cites.

Similar

Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., et al.: TRUE: Re-evaluating factual consistency evaluation. arXiv preprint arXiv:2204.04991 (2022)
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2022
Cited alongside, same era.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., et al.: Survey of hallucination in natural language generation. ACM Computing Surveys 55
2023
Cited alongside, same era.
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., et al.: Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023)
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2023
Cited alongside, same era.
Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., et al.: Active retrieval augmented generation. arXiv preprint arXiv:2305.06983 (2023)
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Cited alongside, same era.
Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., et al.: Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy. arXiv preprint arXiv:2305.15294 (2023)

Then

Liu, N. F., Zhang, T., Liang, P.: Evaluating verifiability in generative search engines. arXiv preprint arXiv:2304.09848 (2023)
Original
2023
Later among the works it cites.
Yue, X., Wang, B., Chen, Z., Zhang, K., Su, Y., et al.: Automatic evaluation of attribution by large language models. arXiv preprint arXiv:2305.06311 (2023)
Original
2023
Later among the works it cites.
Kamalloo, E., Jafari, A., Zhang, X., Thakur, N., Lin, J.: Hagrid: A human-llm collaborative dataset for generative information-seeking with attribution. arXiv preprint arXiv:2307.16883 (2023)
Original
2023
Later among the works it cites.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., et al.: Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)
Original
2023
Later among the works it cites.

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2024

On the Capacity of Citation Generation by Large Language Models

Qian, Haosheng, Fan, Yixing, Zhang, Ruqing et al.

Understand

Retrieval-augmented generation (RAG) appears as a promising method to alleviate the "hallucination" problem in large language models (LLMs), since it can incorporate external traceable resources for response generation.

The essence of RAG in combating the hallucination issue lies in accurately attributing claims in responses to the corresponding retrieved documents.
However, most of existing works focus on improving the quality of generated responses from the LLM, while largely overlooked its ability to attribute sources accurately.
In this study, we conduct a systematic analysis about the capabilities of LLMs in generating citations within response generation, and further introduce a novel method to enhance their citation generation abilities.

Built on

Papineni, K., Roukos, S., Ward, T., Zhu, W. J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
2002
Earlier work this paper cites.
Lin, C. Y.: Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out, pp. 74-81 (2004)
2004
Earlier work this paper cites.
Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., et al.: ELI5: Long form question answering. arXiv preprint arXiv:1907.09190 (2019)
Original
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, pp. 1877-1901 (2020)
2020
Earlier work this paper cites.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. In: Advances in Neural Information Processing Systems, pp. 9459-9474 (2020)
2020
Earlier work this paper cites.
Shuster, K., Poff, S., Chen, M., Kiela, D., Weston, J.: Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567 (2021)
Original
2021
Earlier work this paper cites.
Nakano, R., Hilton, J., Balaji, S., Wu, J., Ouyang, L., et al.: Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332 (2021)
Original
2021
Earlier work this paper cites.
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., et al.: Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685 (2021)
Original
2021
Earlier work this paper cites.
Stelmakh, I., Luan, Y., Dhingra, B., Chang, M. W.: ASQA: Factoid questions meet long-form answers. arXiv preprint arXiv:2204.06092 (2022)
Original
2022
Earlier work this paper cites.
Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., et al.: Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147 (2022)
Original
2022
Earlier work this paper cites.
Gao, L., Dai, Z., Pasupat, P., Chen, A., Chaganty, A. T., et al.: Rarr: Researching and revising what language models say, using language models. arXiv preprint arXiv:2210.08726 (2022)
Original
2022
Earlier work this paper cites.
Bohnet, B., Tran, V. Q., Verga, P., Aharoni, R., Andor, D., et al.: Attributed question answering: Evaluation and modeling for attributed large language models. arXiv preprint arXiv:2212.08037 (2022)
Original
2022
Earlier work this paper cites.

Similar

Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., et al.: TRUE: Re-evaluating factual consistency evaluation. arXiv preprint arXiv:2204.04991 (2022)
Original
2022
Cited alongside, same era.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., et al.: Survey of hallucination in natural language generation. ACM Computing Surveys 55
2023
Cited alongside, same era.
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., et al.: Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023)
Original
2023
Cited alongside, same era.
Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., et al.: Active retrieval augmented generation. arXiv preprint arXiv:2305.06983 (2023)
Original
2023
Cited alongside, same era.
Shao, Z., Gong, Y., Shen, Y., Huang, M., Duan, N., et al.: Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy. arXiv preprint arXiv:2305.15294 (2023)

Then

Liu, N. F., Zhang, T., Liang, P.: Evaluating verifiability in generative search engines. arXiv preprint arXiv:2304.09848 (2023)
Original
2023
Later among the works it cites.
Yue, X., Wang, B., Chen, Z., Zhang, K., Su, Y., et al.: Automatic evaluation of attribution by large language models. arXiv preprint arXiv:2305.06311 (2023)
Original
2023
Later among the works it cites.
Kamalloo, E., Jafari, A., Zhang, X., Thakur, N., Lin, J.: Hagrid: A human-llm collaborative dataset for generative information-seeking with attribution. arXiv preprint arXiv:2307.16883 (2023)
Original
2023
Later among the works it cites.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., et al.: Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Original

2023

Cited alongside, same era.

Gao, T., Yen, H., Yu, J., Chen, D.: Enabling large language models to generate text with citations. arXiv preprint arXiv:2305.14627 (2023)

Original

2023

Cited alongside, same era.

Huang, J., Chang, K. C. C.: Citation: A Key to Building Responsible and Accountable Large Language Models. arXiv preprint arXiv:2307.02185 (2023)

Original

2023

Cited alongside, same era.

Liu, X., Lai, H., Yu, H., Xu, Y., Zeng, A., et al.: WebGLM: Towards an efficient web-enhanced question answering system with human preferences. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4549-4560 (2023)

2023

Cited alongside, same era.

Qian, H., Zhu, Y., Dou, Z., Gu, H., Zhang, X., et al.: Webbrain: Learning to generate factually correct articles for queries by grounding on large web corpus. arXiv preprint arXiv:2304.04358 (2023)

Original

2023

Cited alongside, same era.

Qin, Y., Cai, Z., Jin, D., Yan, L., Liang, S., et al.: Webcpm: Interactive web search for chinese long-form question answering. arXiv preprint arXiv:2305.06849 (2023)

Original

2023

Cited alongside, same era.

Sun, H., Cai, H., Wang, B., Hou, Y., Wei, X., et al.: Towards verifiable text generation with evolving memory and self-reflection. arXiv preprint arXiv:2312.09075 (2023)

Original

2023

Cited alongside, same era.

Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., et al.: Measuring attribution in natural language generation models. Computational Linguistics 49

2023

Cited alongside, same era.

Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., et al.: Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)

Original

2023

Later among the works it cites.

Huang, C., Wu, Z., Hu, Y., Wang, W.: Training language models to generate text with citations via fine-grained rewards. arXiv preprint arXiv:2402.04315 (2024)

Original

2024

Closest in time.

Hu, N., Chen, J., Wu, Y., Qi, G., Bi, S., et al.: Benchmarking large language models in complex question answering attribution using knowledge graphs. arXiv preprint arXiv:2401.14640 (2024)

Original