Kavli Affiliate: Ran Wang
| First 5 Authors: Songhao Wu, Quan Tu, Hong Liu, Jia Xu, Zhongyi Liu
| Summary:
Session search involves a series of interactive queries and actions to
fulfill user’s complex information need. Current strategies typically
prioritize sequential modeling for deep semantic understanding, overlooking the
graph structure in interactions. While some approaches focus on capturing
structural information, they use a generalized representation for documents,
neglecting the word-level semantic modeling. In this paper, we propose Symbolic
Graph Ranker (SGR), which aims to take advantage of both text-based and
graph-based approaches by leveraging the power of recent Large Language Models
(LLMs). Concretely, we first introduce a set of symbolic grammar rules to
convert session graph into text. This allows integrating session history,
interaction process, and task instruction seamlessly as inputs for the LLM.
Moreover, given the natural discrepancy between LLMs pre-trained on textual
corpora, and the symbolic language we produce using our graph-to-text grammar,
our objective is to enhance LLMs’ ability to capture graph structures within a
textual format. To achieve this, we introduce a set of self-supervised symbolic
learning tasks including link prediction, node content generation, and
generative contrastive learning, to enable LLMs to capture the topological
information from coarse-grained to fine-grained. Experiment results and
comprehensive analysis on two benchmark datasets, AOL and Tiangong-ST, confirm
the superiority of our approach. Our paradigm also offers a novel and effective
methodology that bridges the gap between traditional search strategies and
modern LLMs.
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