Papers by Quanshi Zhang

3 papers
Identifying Semantic Induction Heads to Understand In-Context Learning (2024.findings-acl)

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Challenge: Large language models (LLMs) have demonstrated remarkable performance, but lack of transparency in their inference logic raises concerns about their trustworthiness.
Approach: They conduct a detailed analysis of the operations of attention heads to understand their in-context learning of LLMs.
Outcome: The proposed analysis of attention heads reveals that they increase the output logits of object tokens and recall objects . the proposed model is a novel approach to understand the in-context learning of large language models.
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (2022.emnlp-main)

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Challenge: Experimental results show RASAT can leverage a variety of relational structures while inheriting the pretrained parameters from the T5 model.
Approach: They propose a Transformer seq2seq architecture augmented with relation-aware self-attention that leverages relational structures while inheriting pretrained parameters from the T5 model.
Outcome: The proposed model can leverage relational structures while inheriting pretrained parameters from the T5 model effectively.
Challenging the Explanation Based on Preceding Tokens: Discovering Transferable Non-Literal Biasing (2026.acl-short)

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Challenge: et al. (2017) show that the generated preceding tokens may push the large language model towards the target answer.
Approach: They find that generated preceding tokens may push large language models towards the target answer . they suggest that the LLM may intentionally use the semantically unrelated tokens to help generation of the target .
Outcome: The generated preceding tokens may push the large language model towards the target answer . the biased connotations of the target response can also transfer to other prompts .

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