Enhancing Attributed Question Answering using Tailored Progressive Curriculum Learning (2025)

Abstract: We study Attributed Question Answering (abbr., AQA), a newly-released long-form answer generation task. The tailored and efficient training programmes haven’t yet been leveraged to strengthen AQA models. This hinders the simultaneous enhancement of their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning. To address the issue, we propose a tailored progressive curriculum learning approach, and use it to optimize both encoder-decoder and decoder-only AQA models. Experiments on the benchmark QuoteSum show that our approach yields substantial improvements and enables the AQA performance to reach 73.9% Sem-F1 score.

## introduction for a question, aqa aims to generate a long-form answer that consists of source-dependent evidence and free-style contexts @xcite . the evidence is forcibly taken from the given passagelevel sources, while free-style contexts can be produced at will for linguistic coherence during generation. figure 1 shows an example, where evidence is labeled in red font, while contexts the blue. recent progress @xcite has been made through end-to-end generation frameworks, which have significantly improved the overall quality of aqa. however, these approaches still face critical limitations as follows: to address these challenges, we propose a tailored progressive curriculum learning (tpcl) approach, which introduces a modular and dynamic training strategy. tpcl constructs complexitycontrollable training curricula by decomposing aqa into specialized sub-skills and progressively training the model on samples aligned to those subskills. such samples are curated through different tailored training programmes, each focusing on a distinct reasoning or generation aspect. inspired by curriculum learning theory, tpcl orders the training trajectory from simpler and focused tasks to more complex and integrative ones. this progression not only stabilizes learning but also encourages the model to incrementally acquire and retain aqa capabilities, rather than monolithic sft. ## approach the tpcl architecture is shown in figure 2 . it comprises three components, including 1) tailored programme construction, 2) complexity based curriculum development, and 3) curriculum learning. progressive and one-off curriculum learning approaches are respectively proposed to enhance encoder-decoder and decoder-only aqa models. ## conclusion we propose a novel curriculum learning approach (tpcl) for aqa, which provides tailored and complexity-aware curricula for sft, and avoids unstable transition and catastrophic forgetting during learning. experiments show that tpcl obtains substantial improvements, and outperforms the previous arts when both encoder-decoder and decoderonly backbones are used. in the future, we will explore a gpt4-oriented icl method using tpcl.