SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for aligning Large Language Models with human values are limited and results of DPO are not resilient. |
| Approach: | They propose a self-guided direct preference optimization algorithm that incorporates a pilot term to steer the gradient flow during the optimization process. |
| Outcome: | The proposed method can generate human-preferred response up to 9.19% higher than previous methods. |
Similar Papers
Causal Direct Preference Optimization for Language Model Alignment (2026.findings-eacl)
Copied to clipboard
| Challenge: | Empirical evaluations show that CDPO surpasses DPO-based baselines by achieving unbiased fine-tuning through causal reasoning. |
| Approach: | They propose a framework that incorporates causal inference principles to mitigate the influence of confounders and sharpen the signal of genuine human preferences. |
| Outcome: | The proposed framework preserves the tractability of direct optimization while enhancing robustness to spurious correlations and annotation biases. |
BiasDPO: Mitigating Bias in Language Models through Direct Preference Optimization (2024.acl-srw)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have been shown to be effective in complex language tasks, but their potential to perpetuate biases poses significant concerns. |
| Approach: | They propose a new framework employing Direct Preference Optimization to mitigate biases in LLMs. |
| Outcome: | The proposed model outperforms the baseline model on almost all bias benchmarks and achieves better performance than open-source models. |
Direct Preference Optimization with an Offset (2024.findings-acl)
Copied to clipboard
| Challenge: | Direct preference optimization (DPO) fine-tunes language models with human preferences . but not all preference pairs are equal; sometimes, the preferred response is only slightly better than the dispreferred one. |
| Approach: | They propose a generalization of direct preference optimization that does not treat every preference pair equally during fine-tuning. |
| Outcome: | The proposed algorithm outperforms DPO on tasks with limited preference pairs . it requires the difference between likelihood of preferred and dispreferred response to be greater than offset value . |
CRPO: Confidence-Reward Driven Preference Optimization for Machine Translation (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation remains challenging due to pretraining on predominantly English-centric datasets. |
| Approach: | They propose a method that combines reward scores with model confidence to improve model selection for fine-tuning. |
| Outcome: | The proposed method outperforms existing methods in translation accuracy and data efficiency. |
mDPO: Conditional Preference Optimization for Multimodal Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have attempted to apply DPO to multimodal scenarios but have found it challenging to achieve consistent improvement. |
| Approach: | They propose a multimodal DPO objective that prevents the over-prioritization of language-only preferences by also optimizing image preference. |
| Outcome: | The proposed method significantly improves performance on two multimodal LLMs of different sizes and three widely used benchmarks. |
Instantly Learning Preference Alignment via In-context DPO (2025.naacl-long)
Copied to clipboard
| Challenge: | Presently, mainstream approaches to HPA heavily depend on fine-tuning . however, the huge computational and annotation costs of fine-timing are hard to ignore . |
| Approach: | They propose a tuning-free approach to HPA using LLMs' decoding . they first rethink the derivation procedures of DPO and build an instant scorer . |
| Outcome: | The proposed approach outperforms existing methods even with tuning-free baselines and an upgraded scorer. |
Beyond Online Sampling: Bridging Offline-to-Online Alignment via Dynamic Data Transformation for LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Direct Preference Optimization (DPO) eliminates complex reward modeling in aligning large language models with human preferences, but its online variant faces significant efficiency bottlenecks due to costly real-time preference sampling and the reward model annotation. |
| Approach: | They propose a framework that transforms static datasets into dynamically adaptive equivalents without the need for an explicit reward model. |
| Outcome: | The proposed approach matches or exceeds the performance of a fully online DPO. |
Self-supervised Preference Optimization: Enhance Your Language Model with Preference Degree Awareness (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have focused on replacing the reward model in Reinforcement Learning with Human Feedback (RLHF) methods for Large Language Models (LLMs). |
| Approach: | They propose a self-supervised preference optimization framework that replaces the reward model with a preference loss and alignment loss to improve LLMs' ability to understand human preferences. |
| Outcome: | The proposed framework can be integrated with existing preference optimization methods and significantly boost their performance. |
Expectation Preference Optimization: Reliable Preference Estimation for Improving the Reasoning Capability of Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Pairwise preference optimization is used to improve supervised fine-tuning performance of large language models. |
| Approach: | They propose an algorithm that takes pairs of sample groups instead of single samples for preference learning. |
| Outcome: | The proposed algorithm outperforms baseline methods on reasoning benchmarks. |
RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Reinforcement learning with human feedback (RLHF) is widely employed to align large language models with user intent. |
| Approach: | They propose to combine rejection sampling and direct preference optimization to improve alignment with user intent by identifying pairs of contrastive samples from human annotator and alternative LLMs. |
| Outcome: | The proposed method outperforms existing methods including RS, PPO, and DPO in a limited resource environment. |