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This paper introduces a causal attribution model to enhance the interpretability of large language models (LLMs) and improve their causal reasoning abilities via precise fine-tuning.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. et al. (2020), ‘Language models are few-shot learners’, Advances in neural information processing systems
1901
Earlier work this paper cites.
1904
Earlier work this paper cites.
1910
Earlier work this paper cites.
Roth, A. E. (1988), The Shapley Value: Essays in Honor of Lloyd S. Shapley
1988
Earlier work this paper cites.
Comon, P. (1994), ‘Independent component analysis, a new concept?’, Signal processing
1994
Earlier work this paper cites.
Spirtes, P., Glymour, C. N., Scheines, R. & Heckerman, D. (2000), Causation, prediction, and search
2000
Earlier work this paper cites.
Spirtes, P., Glymour, C., Scheines, R., Kauffman, S., Aimale, V. & Wimberly, F. (2000), ‘Constructing bayesian network models of gene expression networks from microarray data’
2000
Earlier work this paper cites.
Chickering, D. M. (2002), ‘Optimal structure identification with greedy search’, Journal of machine learning research
2002
Earlier work this paper cites.
2002
Earlier work this paper cites.
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A. & Nolan, G. P. (2005), ‘Causal protein-signaling networks derived from multiparameter single-cell data’, Science
2005
Earlier work this paper cites.
Kalisch, M. & Bühlmann, P. (2007), ‘Estimating high-dimensional directed acyclic graphs with the pc-algorithm’, Journal of Machine Learning Research
2007
Earlier work this paper cites.
Hoyer, P. O., Janzing, D., Mooij, J., Peters, J. & Schölkopf, B. (2008), Nonlinear causal discovery with additive noise models, in
2008
Earlier work this paper cites.
Hyvärinen, A., Hurri, J., Hoyer, P. O., Hyvärinen, A., Hurri, J. & Hoyer, P. O. (2009), Independent component analysis
2009
Earlier work this paper cites.
Pearl, J. (2009 a
2009
Earlier work this paper cites.
Pearl, J. (2009 b
2009
Earlier work this paper cites.
Strumbelj, E. & Kononenko, I. (2010), ‘An efficient explanation of individual classifications using game theory’, Journal of Machine Learning Research
2010
Earlier work this paper cites.
Hagmayer, Y. & Sloman, S. A. (2013), ‘Causal reasoning’, The Oxford Handbook of Cognitive Psychology
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Bühlmann, P., Peters, J., Ernest, J. et al. (2014), ‘Cam: Causal additive models, high-dimensional order search and penalized regression’, The Annals of Statistics
2014
Earlier work this paper cites.
Loh, P.-L. & Bühlmann, P. (2014), ‘High-dimensional learning of linear causal networks via inverse covariance estimation’, The Journal of Machine Learning Research
2014
Earlier work this paper cites.
Peters, J. & Bühlmann, P. (2014), ‘Identifiability of gaussian structural equation models with equal error variances’, Biometrika
2014
Earlier work this paper cites.
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R. & Samek, W. (2015), ‘On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation’, PLoS ONE
2015
Earlier work this paper cites.
Datta, A., Sen, S. & Zick, Y. (2016), Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems, in
2016
Earlier work this paper cites.
Mooij, J. M., Peters, J., Janzing, D., Zscheischler, J. & Schölkopf, B. (2016), ‘Distinguishing cause from effect using observational data: methods and benchmarks’, Journal of Machine Learning Research
2016
Earlier work this paper cites.
Ribeiro, M. T., Singh, S. & Guestrin, C. (2016), " why should i trust you?" explaining the predictions of any classifier, in
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Kim, J., Rohrbach, A., Darrell, T., Canny, J. & Akata, Z. (2017), ‘Interpretable learning for self-driving cars by visualizing causal attention’, Proceedings of the IEEE International Conference on Computer Vision
2017
Earlier work this paper cites.
Koh, P. W. & Liang, P. (2017), Understanding black-box predictions via influence functions, in
2017
Earlier work this paper cites.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. (2017), ‘Building machines that learn and think like people’, Behavioral and Brain Sciences
2017
Earlier work this paper cites.
Lundberg, S. M. & Lee, S.-I. (2017), ‘A unified approach to interpreting model predictions’, Advances in Neural Information Processing Systems
2017
Cited alongside, same era.
Ramsey, J., Glymour, M., Sanchez-Romero, R. & Glymour, C. (2017), ‘A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images’, International journal of data science and analytics
2017
Cited alongside, same era.
Sundararajan, M., Taly, A. & Yan, Q. (2017), Axiomatic attribution for deep networks, in
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u. & Polosukhin, I. (2017), Attention is all you need, in
2017
Cited alongside, same era.
Vowels, M. J., Camgoz, N. C. & Bowden, R. (2021), ‘D’ya like dags? a survey on structure learning and causal discovery’, ACM Computing Surveys (CSUR)
2021
Later among the works it cites.
2021
Later among the works it cites.
Hu, E. J., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W. et al. (2022), Lora: Low-rank adaptation of large language models, in
2022
Later among the works it cites.
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y. & Iwasawa, Y. (2022), ‘Large language models are zero-shot reasoners’, Advances in neural information processing systems
2022
Later among the works it cites.
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alphaXiv is searching for related work…
2017
Cited alongside, same era.
Zheng, X., Aragam, B., Ravikumar, P. K. & Xing, E. P. (2018), Dags with no tears: Continuous optimization for structure learning, in
2018
Cited alongside, same era.
Chattopadhyay, A., Manupriya, P., Sarkar, A. & Balasubramanian, V. N. (2019), Neural network attributions: A causal perspective, in
2019
Cited alongside, same era.
Glymour, C. & Zhang, K. (2019), ‘Review of causal discovery methods based on graphical models’, Frontiers in Genetics
2019
Cited alongside, same era.
Goyal, A., Shroff, G., Gummadi, K. P. & Choudhury, M. (2019), Explaining machine learning classifiers through diverse counterfactual explanations, in
2019
Cited alongside, same era.
Griffiths, T. L. et al. (2019), ‘Advancing psychological science through the study of causal cognition’, American Psychologist
2019
Cited alongside, same era.
Jain, S. & Wallace, B. C. (2019), Attention is not explanation, in
2019
Cited alongside, same era.
Kenton, J. D. M.-W. C. & Toutanova, L. K. (2019), Bert: Pre-training of deep bidirectional transformers for language understanding, in
2019
Cited alongside, same era.
Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J. M., Tworek, J., Yuan, Q., Tezak, N., Kim, J. W., Hallacy, C., Heidecke, J., Shyam, P., Power, B., Nekoul, T. E., Sastry, G., Krueger, G., Schnurr, D., Such, F. P., Hsu, K., Thompson, M., Khan, T., Sherbakov, T., Jang, J., Welinder, P. & Weng, L. (2022), ‘Text and code embeddings by contrastive pre-training’
2022
Later among the works it cites.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D. et al. (2022), ‘Chain-of-thought prompting elicits reasoning in large language models’, Advances in Neural Information Processing Systems
2022
Later among the works it cites.
Anthropic (2023), ‘Model card and evaluations for claude models’, https://www-files.anthropic.com/production/images/Model-Card-Claude-2.pdf
2023
Closest in time.
2023
Closest in time.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Scao, T. L., Lavril, T., Wang, T., Lacroix, T. & Sayed, W. E. (2023), ‘Mistral 7b’
2023
Closest in time.
Jin, Z., Chen, Y., Leeb, F., Gresele, L., Kamal, O., Zhiheng, L., Blin, K., Adauto, F. G., Kleiman-Weiner, M., Sachan, M. et al. (2023), Cladder: Assessing causal reasoning in language models, in
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
OpenAI (2023), ‘Gpt-4 technical report’
2023
Closest in time.
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S. & Scialom, T. (2023), ‘Llama 2: Open foundation and fine-tuned chat models’
2023
Closest in time.
Zecevic, M., Willig, M., Dhami, D. S. & Kersting, K. (2023), ‘Causal parrots: Large language models may talk causality but are not causal.’, Trans. Mach. Learn. Res
2023
Closest in time.
2024
Closest in time.
Jin, Z., Liu, J., Zhiheng, L., Poff, S., Sachan, M., Mihalcea, R., Diab, M. T. & Schölkopf, B. (2024), Can large language models infer causation from correlation?, in
2024
Closest in time.
Jiralerspong, T., Chen, X., More, Y., Shah, V. & Bengio, Y. (2024), Efficient causal graph discovery using large language models, in
2024
Closest in time.
2024
Closest in time.
Takayama, M. et al. (2024), Integrating large language models into causal discovery pipelines, in
2024
Closest in time.
Wang, L. et al. (2024), ‘Evaluating causal reasoning capabilities of large language models’, Electronics
2024
Closest in time.
Zheng, Y., Huang, B., Chen, W., Ramsey, J., Gong, M., Cai, R., Shimizu, S., Spirtes, P. & Zhang, K. (2024), ‘Causal-learn: Causal discovery in python’, Journal of Machine Learning Research
2024
Closest in time.
Overview of the Qwen3 model family with large context windows and multi-modal input support, including text, audio, and video
Alibaba (2025), ‘Qwen3 foundation language models’ · 2025
Closest in time.
Lee, D. & Others (2025), ‘Benchmarking llm causal reasoning with scientific data’, arXiv preprint arXiv:2510.07231 · 2025
Closest in time.
Ma, J. (2025), Causal inference with large language models: A survey, in
2025
Closest in time.
Large-scale benchmark assessing explicit causal reasoning capabilities of LLMs
Miliani, M. & Others (2025), Explica: Evaluating explicit causal reasoning in large language models, in · 2025
Closest in time.
Next-generation GPT-4 family model with enhanced reasoning and multi-size variants
OpenAI (2025), ‘Gpt-4.1 large language model’ · 2025
Closest in time.
Shimizu, S., Hoyer, P. O., Hyvärinen, A. & Kerminen, A. (2006), ‘A linear non-gaussian acyclic model for causal discovery’, Journal of Machine Learning Research
2030
Closest in time.