The remarkable performance of Large language models (LLMs) relies heavily on the availability of abundant high-quality training data. However, the high cost of acquiring annotated data often prevents models from obtaining capabilities to tackle downstream tasks. In this paper, we introduce a novel method, EpiCoDe that boosts model performance in data-scarcity scenarios without extra training. We first employ model extrapolation to enhance a finetuned model with its inferior version, and then adopt contrastive decoding to further reduce predicted errors, by comparing the logit scores given by the extrapolated and the vanilla finetuned model. Experiments across three domains over four different LLMs show that EpiCoDe consistently outperforms existing methods with significant and robust improvement. We also propose a new theoretical framework to reveal the mechanism behind contrastive decoding in data-scarcity scenarios, which further helps better understand the effectiveness of our EpiCoDe.

In real-world applications, large language models (LLMs) often need to handle diverse and complex instructions. Specifically, when instructions are subject to multiple constraints, some of which are somewhat ambiguous, LLMs often fail to produce answers that satisfy all constraints, limiting their effectiveness in various tasks. To address this challenge, we examine the different constraints in the instructions and discover that the difficulty of determining whether an answer meets a constraint varies widely, from extremely straightforward to exceptionally perplexing. Correspondingly, we propose to assign constraints to different constraint levels. Furthermore, inspired by chain-of-thought (CoT) and self-taught reasoner (STaR), we propose a two-stage method named CARE-STaR (Constraint-AwaRE STaR). Our method distinguishes constraints within instructions by generating different CoTs and guides LLMs to autonomously learn optimal answers by setting the positive rewards to the CoTs that are beneficial to generating accurate answers and iteratively optimizing these answers. We have conducted extensive experiments on three instruction-following benchmarks, taking three existing LLMs as base LLMs, respectively. Experimental results indicate that our method substantially enhances the capability of these LLMs to handle complex instructions, outperforming supervised fine-tuning (SFT). Our code is available at https://github.com/lzl0124/carestar.

Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversarial attacks, and (3) the risk of feedback bias and reward hacking. To address these challenges, we introduce Adversarial Preference Learning (APL), an iterative adversarial training method incorporating three key innovations. First, a direct harmfulness metric based on the model’s intrinsic preference probabilities, eliminating reliance on external assessment. Second, a conditional generative attacker that synthesizes input-specific adversarial variations. Third, an iterative framework with automated closed-loop feedback, enabling continuous adaptation through vulnerability discovery and mitigation. Experiments on Mistral-7B-Instruct-v0.3 demonstrate that APL significantly enhances robustness, achieving 83.33% harmlessness win rate over the base model (evaluated by GPT-4o), reducing harmful outputs from 5.88% to 0.43% (measured by LLaMA-Guard), and lowering attack success rate by up to 65% according to HarmBench. Notably, APL maintains competitive utility, with an MT-Bench score of 6.59 (comparable to the baseline 6.78) and an LC-WinRate of 46.52% against the base model.