Papers with ICP
ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition (2022.coling-1)
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| Challenge: | Existing paradigms for Implicit Discourse Relation Recognition (IDRR) do not exploit linguistic evidence embedded in the pre-training process. |
| Approach: | They propose a new paradigm to detect and classify relation sense between two text segments without an explicit connective. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art algorithms even with fewer training data. |
On the Feasibility of In-Context Probing for Data Attribution (2025.findings-naacl)
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| Challenge: | In-context probing (ICP) can be used to identify training data that contributes to model outputs, but many data attribution methods, such as influence functions, use model gradients and are computationally expensive. |
| Approach: | They propose to use in-context probing (ICP) to proxy for gradient-based data attribution for data selection under conditions contingent on data similarity. |
| Outcome: | The proposed method can be used to identify training data that contribute to model outputs and fine tune models on training data. |
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations. |
| Approach: | They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples. |
| Outcome: | The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions. |