As the scale of large language models (LLMs) grows and natural language tasks become increasingly diverse, Parameter-Efficient Fine-Tuning (PEFT) has become the standard paradigm for fine-tuning LLMs. Among PEFT methods, LoRA is widely adopted for not introducing additional inference overhead. However, existing LoRA’s shared parameter space paradigm introduces parameter interference, leading to a gap in generalization performance for specific tasks compared to full fine-tuning. To address this issue, we propose a parameter-separated low-rank adapter, called Subspace Low-Rank Adaptation (SuLoRA). The core idea of SuLoRA is to account for task differences by decomposing LoRA’s parameter matrix into multiple independent subspaces and assigning them differentially to distinct tasks. This prevents interference across tasks and enhances the effectiveness of low-rank adaptation. Additionally, SuLoRA achieves higher rank expansion by freezing the A matrix, further improving generalization capability. We conduct extensive experiments on various NLP tasks, demonstrating that SuLoRA significantly outperforms LoRA in trainable parameter efficiency and overall model performance. Furthermore, we validate SuLoRA’s effectiveness in domain generalization and multi-modal tasks, showcasing its strong generalization ability.

Despite the remarkable progress made by large language models in mathematical reasoning, interactive theorem proving in formal logic still remains a prominent challenge. Previous methods resort to neural models for proofstep generation and search. However, they suffer from exploring possible proofsteps empirically in a large search space. Moreover, they directly use a less rigorous informal proof for proofstep generation, neglecting the incomplete reasoning within. In this paper, we propose BC-Prover, a backward chaining framework guided by pseudo steps. Specifically, BC-Prover prioritizes pseudo steps to proofstep generation. The pseudo steps boost the proof construction in two aspects: (1) Backward Chaining that decomposes the proof into sub-goals for goal-oriented exploration. (2) Step Planning that makes a fine-grained planning to bridge the gap between informal and formal proofs. Experiments on the miniF2F benchmark show significant performance gains by our framework over the state-of-the-art approaches. Our framework is also compatible with existing provers and further improves their performance with the backward chaining technique.

Argumentative Essay Generation (AEG) is a challenging task in computational argumentation, where detailed logical reasoning and effective rhetorical skills are essential.Previous methods on argument generation typically involve planning prior to generation.However, the planning strategies in these methods overlook the exploration of the logical reasoning process.Inspired by argument structure-related theories, we propose an argumentative planning strategy for prompting large language models (LLMs) to generate high-quality essays.This strategy comprises two stages: (1) Sketch planning, which creates a rough outline of the essay, and (2) Dialectical planning, which refines the outline through critical self-reflection.Such a planning strategy enables LLMs to write argumentative essays that are more logical, diverse, and persuasive.Furthermore, due to the scarcity of existing AEG datasets, we construct three new datasets.These datasets are from two domains: exam essays and news editorials, covering both Chinese and English.Automatic and manual evaluation on four datasets show that our method can generate more dialectical and persuasive essays with higher diversity compared to several strong baselines.

Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks.To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task.However, such methods largely require complex pre- and post-processing to realize the task transformation.In this paper, we investigate the end-to-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence. Then, we employ a pre-trained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence. Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm.Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.