Min‐Soo Kim

Eunkyu Oh, Taehun Kim, Minsoo Kim et al.

Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions and dependencies by modeling the sessions into graph-structured data. However, they still face fundamental challenges in terms of data and learning methodology such as sparse supervision signals and noisy interactions in sessions, leading to sub-optimal performance. In this paper, we propose SR-GCL, a novel contrastive learning framework for a session-based recommendation. As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session. The extensive experiment results on two real-world E-commerce datasets demonstrate the superiority of SR-GCL as compared to other state-of-the-art methods.

Myeonghwa Lee, Seonho An, Min-Soo Kim

In this paper, we conduct a study to utilize LLMs as a solution for decision making that requires complex data analysis. We define Decision QA as the task of answering the best decision, $d_{best}$, for a decision-making question $Q$, business rules $R$ and a database $D$. Since there is no benchmark that can examine Decision QA, we propose Decision QA benchmark, DQA. It has two scenarios, Locating and Building, constructed from two video games (Europa Universalis IV and Victoria 3) that have almost the same goal as Decision QA. To address Decision QA effectively, we also propose a new RAG technique called the iterative plan-then-retrieval augmented generation (PlanRAG). Our PlanRAG-based LM generates the plan for decision making as the first step, and the retriever generates the queries for data analysis as the second step. The proposed method outperforms the state-of-the-art iterative RAG method by 15.8% in the Locating scenario and by 7.4% in the Building scenario, respectively. We release our code and benchmark at https://github.com/myeon9h/PlanRAG.

Keonho Lee, Minsoo Kim, Dong-Wan Choi

Although network pruning has been highly popularized to compress deep neural networks, its resulting accuracy heavily depends on a fine-tuning process that is often computationally expensive and requires the original data. However, this may not be the case in real-world scenarios, and hence a few recent works attempt to restore pruned networks without any expensive retraining process. Their strong assumption is that every neuron being pruned can be replaced with another one quite similar to it, but unfortunately this does not hold in many neural networks, where the similarity between neurons is extremely low in some layers. In this article, we propose a more rigorous and robust method of restoring pruned networks in a fine-tuning free and data-free manner, called LBYL (Leave Before You Leave). LBYL significantly relaxes the aforementioned assumption in a way that each pruned neuron leaves its pieces of information to as many preserved neurons as possible and thereby multiple neurons together obtain a more robust approximation to the original output of the neuron who just left. Our method is based on a theoretical analysis on how to formulate the reconstruction error between the original network and its approximation, which nicely leads to a closed form solution for our derived loss function. Through the extensive experiments, LBYL is confirmed to be indeed more effective to approximate the original network and consequently able to achieve higher accuracy for restored networks, compared to the recent approaches exploiting the similarity between two neurons. The very first version of this work, which contains major technical and theoretical components, was submitted to NeurIPS 2021 and ICML 2022.

Jongho Kim, Dohyeon Lee, Minsoo Kim et al.

Accurately understanding temporal relations between events is a critical building block of diverse tasks, such as temporal reading comprehension (TRC) and relation extraction (TRE). For example in TRC, we need to understand the temporal semantic differences between the following two questions that are lexically near-identical: "What finished right before the decision?" or "What finished right after the decision?". To discern the two questions, existing solutions have relied on answer overlaps as a proxy label to contrast similar and dissimilar questions. However, we claim that answer overlap can lead to unreliable results, due to spurious overlaps of two dissimilar questions with coincidentally identical answers. To address the issue, we propose a novel approach that elicits proper reasoning behaviors through a module for predicting time spans of events. We introduce the Timeline Reasoning Network (TRN) operating in a two-step inductive reasoning process: In the first step model initially answers each question with semantic and syntactic information. The next step chains multiple questions on the same event to predict a timeline, which is then used to ground the answers. Results on the TORQUE and TB-dense, TRC and TRE tasks respectively, demonstrate that TRN outperforms previous methods by effectively resolving the spurious overlaps using the predicted timeline.