Large language models (LLMs) have made significant strides in natural language processing by leveraging their ability to comprehend and reason with factual knowledge. However, a significant amount of factual knowledge is stored in structured data, which has unique characteristics not typically encountered in the unstructured texts used for pretraining LLMs. To evaluate the capability of LLMs in handling facts structurally stored, we introduce a benchmark called StructFact, which includes meticulously annotated factual questions, spanning five tasks that reflect the intrinsic properties of structured data. This benchmark aims to delineate the strengths and limitations of LLMs in reasoning with structured data for knowledge-intensive tasks in practical applications. Extensive experiments conducted on 10 common LLMs have yielded several insights, one notable finding being that these models struggle significantly with the heterogeneity of structured data during reasoning.

Aligning small language models (SLMs) with human values typically involves distilling preference knowledge from large language models (LLMs). However, existing distillation methods model preference knowledge in teacher LLMs by comparing pairwise responses, overlooking the extent of difference between responses. This limitation hinders student SLMs from capturing the nuanced preferences for multiple responses. In this paper, we propose a Preference-Aligned Distillation (PAD) framework, which models teacher’s preference knowledge as a probability distribution over all potential preferences, thereby providing more nuanced supervisory signals. Our insight in developing PAD is rooted in the demonstration that language models can serve as reward functions, reflecting their intrinsic preferences. Based on this, PAD comprises three key steps: (1) sampling diverse responses using high-temperature; (2) computing rewards for both teacher and student to construct their intrinsic preference; and (3) training the student’s intrinsic preference distribution to align with the teacher’s. Experiments on four mainstream alignment benchmarks demonstrate that PAD consistently and significantly outperforms existing approaches, achieving over 20% improvement on AlpacaEval 2 and Arena-Hard, indicating superior alignment with human preferences. Notably, on MT-Bench, using the Gemma model family, the student trained by PAD surpasses its teacher, further validating the effectiveness of our PAD.

This work revisits the topic of jointly parsing constituency and dependency trees, i.e., to produce compatible constituency and dependency trees simultaneously for input sentences, which is attractive considering that the two types of trees are complementary in representing syntax. The original work of Zhou and Zhao (2019) performs joint parsing only at the inference phase. They train two separate parsers under the multi-task learning framework (i.e., one shared encoder and two independent decoders). They design an ad-hoc dynamic programming-based decoding algorithm of O(n⁵) time complexity for finding optimal compatible tree pairs. Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of O(n⁴) time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction. We conduct experiments and analysis on seven languages, covering both rich-resource and low-resource scenarios. Results and analysis show that joint modeling leads to a modest overall performance boost over separate modeling, but substantially improves the complete matching ratio of whole trees, thanks to the explicit modeling of tree compatibility.

“本文介绍了我们在第二十二届中国计算语言学大会中文抽象语义表示解析评测中提交的参赛系统。抽象语义表示(Abstract Meaning Representation,AMR)以有向无环图的形式表示一个句子的语义。本次评测任务针对中文抽象语义表示(Chinese AMR,CAMR),参赛系统不仅需要对常规的AMR图解析预测,还需要预测CAMR数据特有的概念节点对齐、虚词关系对齐、概念同指。我们同时使用多个自回归模型和多个非自回归模型,然后基于图融合的方法将多个模型输出结果融合起来。最终,我们在两个赛道共六个测试集上取得了五项第一名,一项第二名。”