Papers by Keqin Peng
Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)
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| Challenge: | Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal. |
| Approach: | They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors. |
| Outcome: | Empirically, the proposed method yields significant improvements on three translation tasks. |
Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding (2025.acl-short)
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| Challenge: | Prior research has found that large language models overlook input-label mapping information in ICL, relying more on their pre-trained knowledge. |
| Approach: | They propose a novel method that contrasts input-label mappings between positive and negative in-context examples to improve model performance. |
| Outcome: | The proposed method improves performance on 7 natural language understanding tasks without additional training. |
Revisiting Demonstration Selection Strategies in In-Context Learning (2024.acl-long)
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| Challenge: | Large language models (LLMs) have shown an impressive ability to perform a wide range of tasks using in-context learning (ICL). |
| Approach: | They propose a data- and model-dependent method to select models using in-context learning, TopK + ConE, and propose unified explanations for the effectiveness of previous methods. |
| Outcome: | The proposed method improves language understanding and generation tasks with different model scales. |
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction (2024.lrec-main)
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| Challenge: | Recent research shows that pre-trained language models suffer from “prompt bias” in factual knowledge extraction. |
| Approach: | They propose a representation-based approach to mitigate prompt bias during inference time by querying the model and removing it from its internal representations to generate debiased representations. |
| Outcome: | The proposed approach corrects the overfitted performance caused by prompt bias and significantly improves prompt retrieval capability. |
Towards Making the Most of ChatGPT for Machine Translation (2023.findings-emnlp)
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| Challenge: | Prior studies have shown that ChatGPT achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resourced and distant-language-pairs translation. |
| Approach: | They propose task-specific prompts and domain-specific prompts which are based on task information and domain information and a task-specific prompt. |
| Outcome: | The proposed prompts improve the performance of ChatGPT in complex tasks and generate hallucinations for non-English-centric tasks. |