Papers with weaker models
Removing RLHF Protections in GPT-4 via Fine-Tuning (2024.naacl-short)
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| Challenge: | Large language models (LLMs) have increased in their capabilities, which increases their potential for dual use. |
| Approach: | They show that fine-tuning can remove RLHFprotections with as few as 340 examples and a 95% success rate. |
| Outcome: | The proposed method removes RLHFprotections with as few as 340 examples and a 95% success rate on non-censored outputs. |
How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers (2022.findings-emnlp)
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| Challenge: | Pretrained language models use the attention mechanism to contextualize input inputs . but, we find that it is not as important as thought for pretrained models . |
| Approach: | They propose a probing method that replaces input-dependent attention matrices with constant ones. |
| Outcome: | The proposed method improves performance of pretrained language models without input-dependent attention. |
RotBench: Evaluating Multi-modal Large Language Models on Identifying Image Rotation (2026.eacl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) can identify the orientation of input images rotated 0°, 90°, 180°, and 270°. |
| Approach: | They propose a manually-filtered benchmark to evaluate MLLMs' ability to accurately identify rotation in input images. |
| Outcome: | The proposed model improves on the 'rotational cues' of 360° and 180° images, but not 90° and 270° rotations. |
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)
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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian
| Challenge: | LLM-based agents for machine learning engineering rely on tree search to rank candidates. |
| Approach: | They propose an LLM-based agent that operationalizes gradient-based optimization. |
| Outcome: | The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU. |
ECON: On the Detection and Resolution of Evidence Conflicts (2024.emnlp-main)
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Cheng Jiayang, Chunkit Chan, Qianqian Zhuang, Lin Qiu, Tianhang Zhang, Tengxiao Liu, Yangqiu Song, Yue Zhang, Pengfei Liu, Zheng Zhang
| Challenge: | Recent studies have shown that AI generated content is more likely to dominate search results, making it difficult to detect when compared to human-produced content. |
| Approach: | They propose a method for generating diverse, validated evidence conflicts to simulate real-world misinformation scenarios. |
| Outcome: | The proposed method enables the detection of conflicting information in real-world scenarios and shows that weaker models struggle with similar answer conflicts while stronger models show robust performance. |