Papers with ranking score
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models (2024.acl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced the capabilities in natural language processing. |
| Approach: | They propose a method to poison large language models by using annotators to rank a set of collected responses to generate longer tokens. |
| Outcome: | The proposed method can generate longer tokens without harming the original safety alignment performance. |
Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks (2023.findings-acl)
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Sugyeong Eo, Hyeonseok Moon, Jinsung Kim, Yuna Hur, Jeongwook Kim, SongEun Lee, Changwoo Chun, Sungsoo Park, Heuiseok Lim
| Challenge: | Recent advances in QA pair generation (QAG) have raised interest in applying this technique to the educational field. |
| Approach: | They propose a QAG framework that enhances QA type diversity by producing different interrogative sentences and implicit/explicit answers. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by significant margins, achieving improved diversity and quality. |
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (2021.acl-long)
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| Challenge: | Existing personalized news recommendation methods have difficulties in making accurate recommendations to cold-start users. |
| Approach: | They propose to incorporate news popularity information to improve cold-start recommendations . they propose to use a popularity-aware user encoder to eliminate popularity bias . |
| Outcome: | The proposed method improves accuracy and diversity of personalized news recommendation on two real-world datasets. |
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)
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| Challenge: | Existing training methods for code generation do not improve code correctness and efficiency. |
| Approach: | They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency. |
| Outcome: | The proposed framework improves code correctness and efficiency by integrating preference learning into code generation. |