Papers by Zhiqi Shen
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition (2023.findings-emnlp)
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| Challenge: | Emotion Recognition in Conversation (ERC) models are often expensive to train and fine-tune . |
| Approach: | They propose a derivative-free optimization method for few-shot conversational emotion recognition that leverages sharable cross-task knowledge by exploiting external knowledge from other source tasks. |
| Outcome: | The proposed method improves on few-shot scenarios and zero-shot transfers on five different contextual conversation datasets. |
GiFT: Gibbs Fine-Tuning for Code Generation (2025.acl-long)
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| Challenge: | Training Large Language Models (LLMs) with synthetic data is a prevalent practice in code generation. |
| Approach: | They propose a method to fine-tune large language models with code drawn from a conditional distribution, conditioned on a specific seed description. |
| Outcome: | The proposed method improves performance on four datasets and shows that it can be used to fine-tune LLMs with code derived from the marginal distribution. |
Rewriting the Code: A Simple Method for Large Language Model Augmented Code Search (2024.acl-long)
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| Challenge: | Experimental results show that ReCo significantly boosts retrieval accuracy across sparse, zero-shot dense and fine-tuned dense search settings. |
| Approach: | They propose a generation-augmented retrieval framework that additionally Rewrites the Code (ReCo) within the codebase for style normalization. |
| Outcome: | The proposed method significantly boosts retrieval accuracy across sparse, zero-shot dense, and fine-tuned dense retrieval settings in diverse search scenarios. |
A Survey on Natural Language Counterfactual Generation (2024.findings-emnlp)
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| Challenge: | Recent advances in NLP are driven by a variety of Large Language Models (LLMs), such as GPT-3 (175B) and PaLM (540B). |
| Approach: | They propose a taxonomy that categorizes the methods into four groups and summarizes the metrics for evaluating the generation quality. |
| Outcome: | The proposed taxonomy categorizes the generation methods into four groups and summarizes the metrics for evaluating the quality. |