With the rapid development of Large Language Models (LLMs), research related to LLM-based persuasion has become a hotspot in the field. However, existing studies mostly focus on enhancing LLM agents’ persuasive capabilities, while generally neglecting the issue of their vulnerability to persuasion. In this paper, we analyse the root causes of LLM agents’ vulnerability to persuasion and find that it mainly stems from two contradictions and limitations. First, the purpose of catering to human preferences in pre-training phase conflicts with the need to resist manipulation in persuasion scenarios. Second, LLMs are often restricted to play a helpful assistant during training, and the singularity of this personality setting makes it unusually easy to develop targeted persuasion techniques for them. To eliminate LLMs’ vulnerability to persuasion, we propose the Backfire-R1 framework, which helps LLMs build persuasion resistance and better align with user objective by enabling them to learn the reasoning logic of humans with different personalities in persuasion scenarios. Comprehensive experimental results show that, the proposed method significantly improves the persuasion robustness of LLM agents, reducing the opinion shift score by 62.36% while not impairing the model’s general capabilities. Code is now available at: https://github.com/super-jw/Backfire-R1.

Lexical relation refers to the way words are related within a language. Prior work has demonstrated that pretrained language models (PLMs) can effectively mine lexical relations between word pairs. However, they overlook the potential of graph structures composed of lexical relations, which can be integrated with the semantic knowledge of PLMs. In this work, we propose a parameter-efficient fine-tuning method through graph context, which integrates graph features and semantic representations for lexical relation classification (LRC) and lexical entailment (LE) tasks. Our experiments show that graph features can help PLMs better understand more complex lexical relations, establishing a new state-of-the-art for LRC and LE. Finally, we perform an error analysis, identifying the bottlenecks of language models in lexical relation mining tasks and providing insights for future improvements.

Large language models (LLMs) have demonstrated powerful capabilities in natural language processing, yet their vast number of parameters poses challenges for deployment and inference efficiency. Structured model pruning emerges as a viable approach to reduce model size and accelerate inference, without requiring specialized operators and libraries for deployment. However, structured pruning often severely weakens the model’s capability.Despite repetitive fine-tuning can restore the capability to a certain extent, it impairs LLMs’ utility as versatile problem solvers.To address this issue, we propose a novel structured pruning algorithm tailored for LLMs. It derives the importance of different components, namely rows and columns in parameter matrices, based on intermediate data dependencies. Then it removes coupled components across different layers simultaneously and preserves dependency relationships within remaining parameters, avoiding significant performance degradation. The pruned model requires only few epochs of fine-tuning to restore its performance, ensuring the model’s ability to generalize.Empirical evaluations on LLaMA, Vicuna, and ChatGLM3 demonstrate our algorithm’s efficacy, yielding 20% parameter reduction while retaining at least 94.4% of original performance metrics.