Refine Knowledge of Large Language Models via Adaptive Contrastive Learning | Paper Citation Record

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This paper cites Actions, not apps: Toward using llms to reshape context aware interactions in mixed reality systems.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Actions, not apps: Toward using llms to reshape context aware interactions in mixed reality systems

Reference 1

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Observation a92324f3-098f-48b9-9fae-e868f0180f37 · outbound

This paper cites Self-rag: Learning to retrieve, generate, and critique through self-reflection.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Self-rag: Learning to retrieve, generate, and critique through self-reflection

Reference 2

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Observation 1dbfdcb7-d9ad-4d43-a0ef-9692f14f28de · outbound

This paper cites LLM augmented llms: Expanding capabilities through composition.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning LLM augmented llms: Expanding capabilities through composition

Reference 3

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This paper cites Semantic parsing on freebase from question-answer pairs.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Semantic parsing on freebase from question-answer pairs

Reference 4

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Observation 13721b42-c642-423d-ac3d-600910ebe54b · outbound

This paper cites Discovering latent knowledge in language models without supervision.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Discovering latent knowledge in language models without supervision

Reference 5

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Observation 64450ba7-0338-41a3-a1ee-1a1a8dcf5554 · outbound

This paper cites INSIDE: llms' internal states retain the power of hallucination detection.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning INSIDE: llms' internal states retain the power of hallucination detection

Reference 6

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Observation d58c419e-bbfd-4b6a-ac28-144e1dbfc612 · outbound

This paper cites Can AI assistants know what they don't know? In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Can AI assistants know what they don't know? In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024

Reference 7

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Observation fbc77aa2-d660-41d3-9779-66f0c003dfb3 · outbound

This paper cites Glass, and Pengcheng He.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Glass, and Pengcheng He

Reference 8

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This paper cites Scaling Instruction-Finetuned Language Models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Scaling Instruction-Finetuned Language Models

Reference 9

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This paper cites Yu, and Wenpeng Yin.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Yu, and Wenpeng Yin

Reference 10

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Observation 5c893672-f1ea-4661-906d-10302b2ee8a4 · outbound

This paper cites Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training

Reference 11

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Observation 73e71acc-819f-45bd-b72c-44fa5b27caea · outbound

This paper cites Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?

Reference 12

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This paper cites Ctooleval: A chinese benchmark for llm-powered agent evaluation in real-world API interactions.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Ctooleval: A chinese benchmark for llm-powered agent evaluation in real-world API interactions

Reference 13

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Observation 33eeff8d-f500-4cce-a5f7-aae1bb1a4620 · outbound

This paper cites LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles

Reference 14

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This paper cites Weld, and Luke Zettlemoyer.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Weld, and Luke Zettlemoyer

Reference 15

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Observation c78e0ab0-dd5e-428e-bb7f-f582dde1bb48 · outbound

This paper cites Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey

Reference 16

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Observation c4e33470-e4d7-41a8-a9e5-c806e7baddc0 · outbound

This paper cites Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming - Wei Chang, Andrew M.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming - Wei Chang, Andrew M

Reference 17

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Observation e7c01fa4-661c-423d-92ab-53f6bedcf682 · outbound

This paper cites The dawn after the dark: An empirical study on factuality hallucination in large language models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning The dawn after the dark: An empirical study on factuality hallucination in large language models

Reference 18

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Observation bcc6ed43-7810-47b4-97cc-b157cc52a905 · outbound

This paper cites Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

Reference 19

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This paper cites MESED: A multi-modal entity set expansion dataset with fine-grained semantic classes and hard negative entities.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning MESED: A multi-modal entity set expansion dataset with fine-grained semantic classes and hard negative entities

Reference 20

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This paper cites The past mistake is the future wisdom: Error-driven contrastive probability optimization for chinese spell checking.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning The past mistake is the future wisdom: Error-driven contrastive probability optimization for chinese spell checking

Reference 21

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This paper cites On the (In)Effectiveness of Large Language Models for Chinese Text Correction.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning On the (In)Effectiveness of Large Language Models for Chinese Text Correction

Reference 22

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Observation ff8844fc-b6fd-45f8-9f01-dfc1b8bb5a26 · outbound

This paper cites Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Bidirectional End-to-End Learning of Retriever-Reader Paradigm for Entity Linking

Reference 23

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Observation 1ca4c8be-0206-4421-bc2b-5ac4b0915084 · outbound

This paper cites Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

Reference 24

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Observation e68c46f1-3b6e-4f6d-ab63-defeb903858f · outbound

This paper cites Towards real-world writing assistance: A chinese character checking benchmark with faked and misspelled characters.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Towards real-world writing assistance: A chinese character checking benchmark with faked and misspelled characters

Reference 25

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Observation b71c4cb2-9a90-4214-b15c-1ad5cca0ab1d · outbound

This paper cites When llms meet cunning texts: A fallacy understanding benchmark for large language models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning When llms meet cunning texts: A fallacy understanding benchmark for large language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.668400Z

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Observation 747e1e1c-6409-4331-a4f6-b82434b0e9d6 · outbound

This paper cites Correct like humans: Progressive learning framework for chinese text error correction.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Correct like humans: Progressive learning framework for chinese text error correction

Reference 27

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Observation b16c9b72-1302-4687-b31f-3ef1faee5851 · outbound

This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 28

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Observation 251c6f10-50d7-4ae1-887e-457500e91654 · outbound

This paper cites Are we ready for a new paradigm shift? A survey on visual deep MLP.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Are we ready for a new paradigm shift? A survey on visual deep MLP

Reference 29

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Observation e7f1e729-d640-4796-9559-3303d4011ed3 · outbound

This paper cites What makes good data for alignment? A comprehensive study of automatic data selection in instruction tuning.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning What makes good data for alignment? A comprehensive study of automatic data selection in instruction tuning

Reference 30

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Observation bd5d393c-0a40-4060-9995-5b9cf5acc59b · outbound

This paper cites Radev, and Graham Neubig.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Radev, and Graham Neubig

Reference 31

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Observation f6f7b727-739e-4355-a57b-1d5734dfe145 · outbound

This paper cites Le, Barret Zoph, Jason Wei, and Adam Roberts.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Le, Barret Zoph, Jason Wei, and Adam Roberts

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.618139Z

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Observation 4355487f-815c-465f-9244-e52a17894a4d · outbound

This paper cites Coarse-to-fine highlighting: Reducing knowledge hallucination in large language models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Coarse-to-fine highlighting: Reducing knowledge hallucination in large language models

Reference 33

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Observation 239957e4-eca9-4815-b221-25cd49812587 · outbound

This paper cites Explicitly stating assumptions reduces hallucinations in natural language inference.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Explicitly stating assumptions reduces hallucinations in natural language inference

Reference 34

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Observation 5c8d811b-bbb1-4b66-8cae-944756db9dcf · outbound

This paper cites Octopack: Instruction tuning code large language models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Octopack: Instruction tuning code large language models

Reference 35

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Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Unresolved cited work

Reference 36

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Observation c2861799-1b3d-4e18-9dc5-4c867ba2a793 · outbound

This paper cites Ragtruth: A hallucination corpus for developing trustworthy retrieval-augmented language models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Ragtruth: A hallucination corpus for developing trustworthy retrieval-augmented language models

Reference 37

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Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Unresolved cited work

Reference 38

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Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Unresolved cited work

Reference 39

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Observation 2653432f-ca20-4624-9834-f1d0a7e1a2a9 · outbound

This paper cites Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment

Reference 40

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Observation 144451d9-da79-4b3d-b5b1-e461984fbaae · outbound

This paper cites Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models

Reference 41

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Observation 998d2a46-a72c-4b8a-a5c6-ca9917907a13 · outbound

This paper cites DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition

Reference 42

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Observation b5405f3b-843b-47c9-940b-cd8ba95e7010 · outbound

This paper cites MINT: evaluating llms in multi-turn interaction with tools and language feedback.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning MINT: evaluating llms in multi-turn interaction with tools and language feedback

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.515153Z

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Observation 3e493c23-cea5-4668-8f44-19b57a5d5ea9 · outbound

This paper cites Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M

Reference 44

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Observation 1b0b7540-02d6-4d6e-9f2d-3290bdd87b97 · outbound

This paper cites Let llms take on the latest challenges! A chinese dynamic question answering benchmark.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Let llms take on the latest challenges! A chinese dynamic question answering benchmark

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.499557Z

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Observation 69c154c3-5128-4a55-a795-75c649ac6649 · outbound

This paper cites Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, and Muhao Chen.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Huang, Wenxuan Zhou, Fan Yin, Aram Galstyan, Wenpeng Yin, and Muhao Chen

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.489610Z

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Observation c190fd76-65f5-4487-9345-04b3d62f2277 · outbound

This paper cites ALCUNA : Large language models meet new knowledge.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning ALCUNA : Large language models meet new knowledge

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T13:36:24.770949Z

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Observation c4c8d3b9-a585-4f6e-a7f8-6fb3fe79c021 · outbound

This paper cites Do large language models know what they don't know? In Anna Rogers, Jordan L.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Do large language models know what they don't know? In Anna Rogers, Jordan L

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.480168Z

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Observation aa76264b-ee25-47af-8fbb-4ac31b0bf860 · outbound

This paper cites Seqgpt: An out-of-the-box large language model for open domain sequence understanding.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Seqgpt: An out-of-the-box large language model for open domain sequence understanding

Reference 49

Resolution: verified exact; doi, observed 2026-08-08T13:36:25.470272Z

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Observation a16154c7-0b99-49c8-aa09-ff702bebcaf8 · outbound

This paper cites When scaling meets LLM finetuning: The effect of data, model and finetuning method.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning When scaling meets LLM finetuning: The effect of data, model and finetuning method

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.459721Z

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Observation d4dd3ca1-747c-46db-9e98-63f8c6c3124e · outbound

This paper cites Enhancing hallucination detection through perturbation-based synthetic data generation in system responses.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Enhancing hallucination detection through perturbation-based synthetic data generation in system responses

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.449429Z

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Observation e08db887-1aef-48b9-91d6-d3b74139f460 · outbound

This paper cites Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Fung, Qing Lian, Xingyao Wang, Yangyi Chen, Heng Ji, and Tong Zhang

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.438512Z

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Observation d45e2a2f-9a07-4fb8-955f-2d60ed2d658c · outbound

This paper cites an unresolved cited work.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:36:25.427045Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ba748dc7-0b4a-443c-b789-6259e35bf197 · outbound

This paper cites Self-alignment for factuality: Mitigating hallucinations in llms via self-evaluation.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Self-alignment for factuality: Mitigating hallucinations in llms via self-evaluation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T13:36:25.415378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 780e26fb-6a48-4fd6-ab9a-87f207ed756b · outbound

This paper cites A Survey on Model Compression for Large Language Models.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning A Survey on Model Compression for Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T13:36:24.795641Z

Source-reported events for the cited work

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Observation a9b3aa21-f2a4-45b3-93fd-f08cbfab76e1 · outbound

This paper cites write newline.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning write newline

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T13:36:24.799157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29078e34-4c18-4f4a-af07-36c6cffccc98 · outbound

This paper cites @esa (Ref.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning @esa (Ref

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T13:36:24.803027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5b3eadd-8425-43a6-bde0-1d34bc8aa91e · outbound

This paper cites an unresolved cited work.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T13:36:24.806773Z

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Unavailable: canonical work link unavailable.

Observation a5613fc4-5175-4cba-b3f1-0e7f658fca54 · outbound

This paper cites an unresolved cited work.

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T13:36:24.810891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers