TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs | Paper Citation Record

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs write newline

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs and Bengio, Y

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Detecting Language Model Attacks with Perplexity

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Many-shot jailbreaking

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs A General Language Assistant as a Laboratory for Alignment

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs and Mitchell, T

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs L., Kiros, J

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This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Constitutional AI: Harmlessness from AI Feedback

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Probing classifiers: Promises, shortcomings, and advances

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Findings of the 2014 workshop on statistical machine translation

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Discovering latent knowledge in language models without supervision

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs T., Rubanova, Y., Bettencourt, J., and Duvenaud, D

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Some Gronwall Type Inequalities and Applications

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This paper cites A mathematical framework for transformer circuits.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs A mathematical framework for transformer circuits

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs and Cohen, V

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Studying Large Language Model Generalization with Influence Functions

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs and Lowd, D

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Measuring massive multitask language understanding

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs T., Wortsman, M., Schmidt, L., Hajishirzi, H., and Farhadi, A

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Baseline Defenses for Adversarial Attacks Against Aligned Language Models

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs S., Dick, R

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs J., Hassani, H., Zhang, Y., Wong, E., and Chang, S

Reference 31

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Mistral 7B

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs M., and Raghunathan, A

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs J., Feizi, S., and Lakkaraju, H

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Inference-time intervention: Eliciting truthful answers from a language model

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Mitigating the alignment tax of rlhf

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Autodan: Generating stealthy jailbreak prompts on aligned large language models

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs S., Love, J., Tafti, P., et al

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Introducing meta llama 3: The most capable openly available llm to date

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Zoom in: An introduction to circuits

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs In-context learning and induction heads

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Discovering language model behaviors with model-written evaluations

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs D., Peng, S., Szyller, S., Cornelius, C., and Chau, D

Reference 47

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs R., Haklay, T., Belinkov, Y., and Bau, D

Reference 48

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Estimating training data influence by tracing gradient descent

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Improving language understanding by generative pre-training

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Language models are unsupervised multitask learners

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs D., Ermon, S., and Finn, C

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Steering llama 2 via contrastive activation addition

Reference 53

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs The perceptron: a probabilistic model for information storage and organization in the brain

Reference 55

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Observation 9d759957-1809-45b5-9c31-20276b76c71e · outbound

This paper cites Principles of Mathematical Analysis.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Principles of Mathematical Analysis

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:19.098581Z

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Observation 4cd72699-35f4-451d-a3cd-b08bc067df87 · outbound

This paper cites E., Ablin, P., and Peyr \'e , G.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs E., Ablin, P., and Peyr \'e , G

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:18.955649Z

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Observation 7dc37f8b-3d6c-431f-a25a-58fe4fe2073a · outbound

This paper cites Scaling up influence functions.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Scaling up influence functions

Reference 58

Resolution: verified exact; doi, observed 2026-08-06T21:50:15.881705Z

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Observation 141717dd-485a-40bc-921b-0193b85c374c · outbound

This paper cites Proximal Policy Optimization Algorithms.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Proximal Policy Optimization Algorithms

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:13.190645Z

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs LLaMA: Open and Efficient Foundation Language Models

Reference 60

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:13.383086Z

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This paper cites and Schmid, P.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs and Schmid, P

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:18.803124Z

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Observation ef2e07b4-647e-444f-a69b-391f780e4087 · outbound

This paper cites M., and Wolf, T.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs M., and Wolf, T

Reference 63

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This paper cites N., Kaiser, ., and Polosukhin, I.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs N., Kaiser, ., and Polosukhin, I

Reference 64

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Observation e4d4d359-498c-4aa8-87c0-e30e26dfeba9 · outbound

This paper cites Ordinary differential equations, volume 182.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Ordinary differential equations, volume 182

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:18.625098Z

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Observation 49835ec9-7ca4-4ec7-8796-4cd22ba9849e · outbound

This paper cites R., Variengien, A., Conmy, A., Shlegeris, B., and Steinhardt, J.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs R., Variengien, A., Conmy, A., Shlegeris, B., and Steinhardt, J

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:18.478557Z

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This paper cites Defending llms against jailbreaking attacks via backtranslation.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Defending llms against jailbreaking attacks via backtranslation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:18.297796Z

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Observation a3bb0431-892f-4ba6-93ac-98e594c730f1 · outbound

This paper cites Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems, 36, 2024

Reference 68

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Observation 91a5de1e-7136-471f-8b1e-d8f5cafc65b2 · outbound

This paper cites Larger language models do in-context learning differently, 2023.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Larger language models do in-context learning differently, 2023

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:18.168581Z

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Observation 52dc678e-658e-4d5b-927d-471c32b269a3 · outbound

This paper cites Principal component analysis.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Principal component analysis

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:17.918649Z

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Observation 4ff83114-967b-4853-8112-e30ffad8073e · outbound

This paper cites Usage statistics of content languages for websites.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Usage statistics of content languages for websites

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:17.599966Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b6808509-f61e-451e-ae9f-06a3e3db47c0 · outbound

This paper cites W., Li, M., Kornblith, S., Roelofs, R., Lopes, R.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs W., Li, M., Kornblith, S., Roelofs, R., Lopes, R

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:17.403142Z

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Observation b3c69623-c5e6-4268-ad71-a0e9e07bd2de · outbound

This paper cites Defending chatgpt against jailbreak attack via self-reminders.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Defending chatgpt against jailbreak attack via self-reminders

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:14.587991Z

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Observation 1dd52d6f-44c9-4c3b-962c-40c2abc494f2 · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.\ 4791--4800, 2019.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp.\ 4791--4800, 2019

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:14.750123Z

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This paper cites and Sennrich, R.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs and Sennrich, R

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:14.950317Z

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Observation 932661df-5f6a-47d3-986e-594921747b4b · outbound

This paper cites Intention analysis makes llms a good jailbreak defender.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Intention analysis makes llms a good jailbreak defender

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:15.176064Z

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Observation 00ba2fe8-d947-4029-afeb-ccb85aa337b7 · outbound

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TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:50:17.223607Z

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Observation ce3f861b-a1a6-4190-b104-8328c3c0f3fb · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:17.016547Z

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Observation 551163d5-80a9-440b-b1eb-e5d18b1b001a · outbound

This paper cites Defending jailbreak prompts via in-context adversarial game.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Defending jailbreak prompts via in-context adversarial game

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:16.753576Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a1ecdf43-6ed1-40fb-87c5-92bde0b8a237 · outbound

This paper cites Autodan: Interpretable gradient-based adversarial attacks on large language models.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Autodan: Interpretable gradient-based adversarial attacks on large language models

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:16.526922Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5b88c3f7-2409-4b3a-9905-3be64ecef70b · outbound

This paper cites Representation engineering: A top-down approach to ai transparency.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Representation engineering: A top-down approach to ai transparency

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:50:16.220295Z

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Observation 0f825aa7-64ac-428f-91a8-1f6f32b76ea8 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

TuCo: Measuring the Contribution of Fine-Tuning to Individual Responses of LLMs Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 82

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