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Large Language Models (LLMs) are deployed as powerful tools for several natural language processing (NLP) applications.
Visualizing and understanding neural models in nlp
Li, J., Chen, X., Hovy, E. H., and Jurafsky, D · 2015
Earlier work this paper cites.
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
Amann, J., Blasimme, A., Vayena, E., Frey, D., and Madai, V · 2020
Earlier work this paper cites.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
Earlier work this paper cites.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Jacovi, A. and Goldberg, Y · 2020
Earlier work this paper cites.
Zoom in: An introduction to circuits
Olah, C., Cammarata, N., Schubert, L., Goh, G., Petrov, M., and Carter, S · 2020
Earlier work this paper cites.
Perturbed masking: Parameter-free probing for analyzing and interpreting bert
Wu, Z., Chen, Y., Kao, B., and Liu, Q · 2020
Earlier work this paper cites.
Teach me to explain: A review of datasets for explainable natural language processing
Wiegreffe, S. and Marasović, A · 2021
Earlier work this paper cites.
Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Valmeekam, K., Olmo, A., Sreedharan, S., and Kambhampati, S · 2022
Earlier work this paper cites.
Emergent abilities of large language models
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al · 2022
Earlier work this paper cites.
The unreliability of explanations in few-shot prompting for textual reasoning
Ye, X. and Durrett, G · 2022
Earlier work this paper cites.
Openxai: Towards a transparent evaluation of model explanations, 2023
Agarwal, C., Krishna, S., Saxena, E., Pawelczyk, M., Johnson, N., Puri, I., Zitnik, M., and Lakkaraju, H · 2023
Cited alongside, same era.
Opt-r: Exploring the role of explanations in finetuning and prompting for reasoning skills of large language models
AlKhamissi, B., Verma, S., Yu, P., Jin, Z., Celikyilmaz, A., and Diab, M · 2023
Cited alongside, same era.
Large language models in education: Vision and opportunities, 2023
Gan, W., Qi, Z., Wu, J., and Lin, J. C.-W · 2023
Cited alongside, same era.
Challenges and applications of large language models
Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., and McHardy, R · 2023
Cited alongside, same era.
Llms as factual reasoners: Insights from existing benchmarks and beyond
Laban, P., Kryściński, W., Agarwal, D., Fabbri, A. R., Xiong, C., Joty, S., and Wu, C.-S · 2023
Cited alongside, same era.
Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting, 2023
Turpin, M., Michael, J., Perez, E., and Bowman, S. R · 2023
Later among the works it cites.
Why can large language models generate correct chain-of-thoughts?
Tutunov, R., Grosnit, A., Ziomek, J., Wang, J., and Bou-Ammar, H · 2023
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models, 2023
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2023
Later among the works it cites.
Agcvt-prompt for sentiment classification: Automatically generating chain of thought and verbalizer in prompt learning
Gu, X., Chen, X., Lu, P., Li, Z., Du, Y., and Li, X · 2024
Closest in time.
Towards faithful model explanation in nlp: A survey, 2024
Lyu, Q., Apidianaki, M., and Callison-Burch, C · 2024
Closest in time.
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Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., Lukošiūtė, K., Nguyen, K., Cheng, N., Joseph, N., Schiefer, N., Rausch, O., Larson, R., McCandlish, S., Kundu, S., Kadavath, S., Yang, S., Henighan, T., Maxwell, T., Telleen-Lawton, T., Hume, T., Hatfield-Dodds, Z., Kaplan, J., Brauner, J., Bowman, S. R., and Perez, E · 2023
Cited alongside, same era.
Ai transparency in the age of llms: A human-centered research roadmap, 2023
Liao, Q. V. and Vaughan, J. W · 2023
Cited alongside, same era.
Faithful chain-of-thought reasoning, 2023
Lyu, Q., Havaldar, S., Stein, A., Zhang, L., Rao, D., Wong, E., Apidianaki, M., and Callison-Burch, C · 2023
Cited alongside, same era.
The art of SOCRATIC QUESTIONING: Recursive thinking with large language models
Qi, J., Xu, Z., Shen, Y., Liu, M., Jin, D., Wang, Q., and Huang, L · 2023
Cited alongside, same era.
Large language models help humans verify truthfulness–except when they are convincingly wrong
Si, C., Goyal, N., Wu, S. T., Zhao, C., Feng, S., Daumé III, H., and Boyd-Graber, J · 2023
Cited alongside, same era.
Do models explain themselves? counterfactual simulatability of natural language explanations, 2023a
Chen, Y., Zhong, R., Ri, N., Zhao, C., He, H., Steinhardt, J., Yu, Z., and McKeown, K
Cited in the paper.
Xplainllm: A qa explanation dataset for understanding llm decision-making
Chen, Z., Chen, J., Gaidhani, M., Singh, A., and Sra, M
Cited in the paper.
Meng, Z., Zhang, Y., Feng, Z., Feng, Y., Wang, G., Zhou, J. T., Wu, J., and Liu, Z · 2024
Closest in time.
Chain of thought utilization in large language models and application in nephrology
Miao, J., Thongprayoon, C., Suppadungsuk, S., Krisanapan, P., Radhakrishnan, Y., and Cheungpasitporn, W · 2024
Closest in time.
Llm harmony: Multi-agent communication for problem solving
Rasal, S · 2024
Closest in time.
The benefits of a concise chain of thought on problem-solving in large language models
Renze, M. and Guven, E · 2024
Closest in time.