Logits are All We Need to Adapt Closed Models | Paper Citation Record

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This paper cites write newline.

Logits are All We Need to Adapt Closed Models write newline

Reference 1

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This paper cites Kaggle, 2023.

Logits are All We Need to Adapt Closed Models Kaggle, 2023

Reference 2

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This paper cites GPT-4 Technical Report.

Logits are All We Need to Adapt Closed Models GPT-4 Technical Report

Reference 3

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

Logits are All We Need to Adapt Closed Models Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 4

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This paper cites Constitutional AI: Harmlessness from AI Feedback.

Logits are All We Need to Adapt Closed Models Constitutional AI: Harmlessness from AI Feedback

Reference 5

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Logits are All We Need to Adapt Closed Models and Lavie, A

Reference 6

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This paper cites M., Netrapalli, P., and Sanghavi, S.

Logits are All We Need to Adapt Closed Models M., Netrapalli, P., and Sanghavi, S

Reference 7

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This paper cites F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D.

Logits are All We Need to Adapt Closed Models F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D

Reference 8

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This paper cites Plug and play language models: A simple approach to controlled text generation.

Logits are All We Need to Adapt Closed Models Plug and play language models: A simple approach to controlled text generation

Reference 9

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This paper cites Qlora: Efficient finetuning of quantized llms.

Logits are All We Need to Adapt Closed Models Qlora: Efficient finetuning of quantized llms

Reference 10

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This paper cites Black-box Prompt Learning for Pre-trained Language Models.

Logits are All We Need to Adapt Closed Models Black-box Prompt Learning for Pre-trained Language Models

Reference 11

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This paper cites Automatic evaluation of machine translation quality using n-gram co-occurrence statistics.

Logits are All We Need to Adapt Closed Models Automatic evaluation of machine translation quality using n-gram co-occurrence statistics

Reference 12

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This paper cites A survey on in-context learning.

Logits are All We Need to Adapt Closed Models A survey on in-context learning

Reference 13

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This paper cites The Llama 3 Herd of Models.

Logits are All We Need to Adapt Closed Models The Llama 3 Herd of Models

Reference 14

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This paper cites Evaluating the state-of-the-art of end-to-end natural language generation: The e2e nlg challenge.

Logits are All We Need to Adapt Closed Models Evaluating the state-of-the-art of end-to-end natural language generation: The e2e nlg challenge

Reference 15

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This paper cites M., and Sidford, A.

Logits are All We Need to Adapt Closed Models M., and Sidford, A

Reference 16

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This paper cites Aligning LLM Agents by Learning Latent Preference from User Edits.

Logits are All We Need to Adapt Closed Models Aligning LLM Agents by Learning Latent Preference from User Edits

Reference 17

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This paper cites Creating training corpora for nlg micro-planning.

Logits are All We Need to Adapt Closed Models Creating training corpora for nlg micro-planning

Reference 18

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This paper cites M., and Koyejo, S.

Logits are All We Need to Adapt Closed Models M., and Koyejo, S

Reference 19

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This paper cites A tail inequality for quadratic forms of subgaussian random vectors.

Logits are All We Need to Adapt Closed Models A tail inequality for quadratic forms of subgaussian random vectors

Reference 20

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Logits are All We Need to Adapt Closed Models LoRA: Low-Rank Adaptation of Large Language Models

Reference 21

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This paper cites LLM -adapters: An adapter family for parameter-efficient fine-tuning of large language models.

Logits are All We Need to Adapt Closed Models LLM -adapters: An adapter family for parameter-efficient fine-tuning of large language models

Reference 22

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This paper cites Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models.

Logits are All We Need to Adapt Closed Models Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models

Reference 23

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This paper cites Calibrating Long-form Generations from Large Language Models.

Logits are All We Need to Adapt Closed Models Calibrating Long-form Generations from Large Language Models

Reference 24

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Logits are All We Need to Adapt Closed Models GPT-4o System Card

Reference 25

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This paper cites Calibration-tuning: Teaching large language models to know what they don’t know.

Logits are All We Need to Adapt Closed Models Calibration-tuning: Teaching large language models to know what they don’t know

Reference 26

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This paper cites S., Reid, M., Matsuo, Y., and Iwasawa, Y.

Logits are All We Need to Adapt Closed Models S., Reid, M., Matsuo, Y., and Iwasawa, Y

Reference 27

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This paper cites O., Natarajan, N., Ravikumar, P.

Logits are All We Need to Adapt Closed Models O., Natarajan, N., Ravikumar, P

Reference 28

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This paper cites D., McCann, B., Keskar, N.

Logits are All We Need to Adapt Closed Models D., McCann, B., Keskar, N

Reference 29

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This paper cites and Szepesv \'a ri, C.

Logits are All We Need to Adapt Closed Models and Szepesv \'a ri, C

Reference 30

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This paper cites The power of scale for parameter-efficient prompt tuning.

Logits are All We Need to Adapt Closed Models The power of scale for parameter-efficient prompt tuning

Reference 31

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Logits are All We Need to Adapt Closed Models S., and Hashimoto, T

Reference 32

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Logits are All We Need to Adapt Closed Models Unresolved cited work

Reference 33

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Logits are All We Need to Adapt Closed Models Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., and Ren, X

Reference 34

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Logits are All We Need to Adapt Closed Models Rouge: A package for automatic evaluation of summaries

Reference 35

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Logits are All We Need to Adapt Closed Models and Och, F

Reference 36

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This paper cites Detecting and correcting for label shift with black box predictors.

Logits are All We Need to Adapt Closed Models Detecting and correcting for label shift with black box predictors

Reference 37

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Logits are All We Need to Adapt Closed Models A., and Choi, Y

Reference 38

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Logits are All We Need to Adapt Closed Models Tuning Language Models by Proxy

Reference 39

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This paper cites Adapt in contexts: Retrieval-augmented domain adaptation via in-context learning.

Logits are All We Need to Adapt Closed Models Adapt in contexts: Retrieval-augmented domain adaptation via in-context learning

Reference 40

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This paper cites Mix and match: Learning-free controllable text generationusing energy language models.

Logits are All We Need to Adapt Closed Models Mix and match: Learning-free controllable text generationusing energy language models

Reference 41

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Logits are All We Need to Adapt Closed Models S., and Nowak, R

Reference 42

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Logits are All We Need to Adapt Closed Models and Javidi, T

Reference 43

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Logits are All We Need to Adapt Closed Models Consistent multiclass algorithms for complex performance measures

Reference 44

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This paper cites Training language models to follow instructions with human feedback.

Logits are All We Need to Adapt Closed Models Training language models to follow instructions with human feedback

Reference 45

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Logits are All We Need to Adapt Closed Models Bleu: a method for automatic evaluation of machine translation

Reference 46

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Logits are All We Need to Adapt Closed Models Making deep neural networks robust to label noise: A loss correction approach

Reference 47

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This paper cites To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO.

Logits are All We Need to Adapt Closed Models To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO

Reference 48

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Logits are All We Need to Adapt Closed Models Language models are unsupervised multitask learners

Reference 49

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Logits are All We Need to Adapt Closed Models D., Ermon, S., and Finn, C

Reference 50

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This paper cites Multitask prompted training enables zero-shot task generalization.

Logits are All We Need to Adapt Closed Models Multitask prompted training enables zero-shot task generalization

Reference 51

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Logits are All We Need to Adapt Closed Models W., and Ghosh, S

Reference 52

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This paper cites The Hessian perspective into the Nature of Convolutional Neural Networks.

Logits are All We Need to Adapt Closed Models The Hessian perspective into the Nature of Convolutional Neural Networks

Reference 53

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This paper cites H., Wu, J., Washington, C., Sadler, B.

Logits are All We Need to Adapt Closed Models H., Wu, J., Washington, C., Sadler, B

Reference 54

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This paper cites Training convolutional networks with noisy labels.

Logits are All We Need to Adapt Closed Models Training convolutional networks with noisy labels

Reference 55

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This paper cites On the Duality between Gradient Transformations and Adapters.

Logits are All We Need to Adapt Closed Models On the Duality between Gradient Transformations and Adapters

Reference 56

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Observation f6c84453-3adc-4b8f-b4a8-74a5456591b0 · outbound

This paper cites Calibrating Large Language Models Using Their Generations Only.

Logits are All We Need to Adapt Closed Models Calibrating Large Language Models Using Their Generations Only

Reference 57

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This paper cites Cider: Consensus-based image description evaluation.

Logits are All We Need to Adapt Closed Models Cider: Consensus-based image description evaluation

Reference 58

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This paper cites W., Lester, B., Du, N., Dai, A.

Logits are All We Need to Adapt Closed Models W., Lester, B., Du, N., Dai, A

Reference 59

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This paper cites HuggingFace's Transformers: State-of-the-art Natural Language Processing.

Logits are All We Need to Adapt Closed Models HuggingFace's Transformers: State-of-the-art Natural Language Processing

Reference 60

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This paper cites and Klein, D.

Logits are All We Need to Adapt Closed Models and Klein, D

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:06.117717Z

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This paper cites A Study on the Calibration of In-context Learning.

Logits are All We Need to Adapt Closed Models A Study on the Calibration of In-context Learning

Reference 62

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This paper cites Why Transformers Need Adam: A Hessian Perspective.

Logits are All We Need to Adapt Closed Models Why Transformers Need Adam: A Hessian Perspective

Reference 63

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This paper cites On the calibration of large language models and alignment.

Logits are All We Need to Adapt Closed Models On the calibration of large language models and alignment

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:18:06.101812Z

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