Beong-woo Kwak


2024

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Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Hyungjoo Chae | Yeonghyeon Kim | Seungone Kim | Kai Tzu-iunn Ong | Beong-woo Kwak | Moohyeon Kim | Sunghwan Kim | Taeyoon Kwon | Jiwan Chung | Youngjae Yu | Jinyoung Yeo
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Algorithmic reasoning tasks that involve complex logical patterns, such as completing Dyck language, pose challenges for large language models (LLMs), despite their recent success. Prior work has used LLMs to generate programming language and applied external compilers for such tasks. Yet, when on the fly, it is hard to generate an executable code with the correct logic for the solution. Even so, code for one instance cannot be reused for others, although they might require the same logic to solve. We present Think-and-Execute, a novel framework that improves LLMs’ algorithmic reasoning: (1) In Think, we discover task-level logic shared across all instances, and express such logic with pseudocode; (2) In Execute, we tailor the task-level pseudocode to each instance and simulate the execution of it. Think-and-Execute outperforms several strong baselines (including CoT and PoT) in diverse algorithmic reasoning tasks. We manifest the advantage of using task-level pseudocode over generating instance-specific solutions one by one. Also, we show that pseudocode can better improve LMs’ reasoning than natural language (NL) guidance, even though they are trained with NL instructions.

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Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code
Hyungjoo Chae | Taeyoon Kwon | Seungjun Moon | Yongho Song | Dongjin Kang | Kai Tzu-iunn Ong | Beong-woo Kwak | Seonghyeon Bae | Seung-won Hwang | Jinyoung Yeo
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing. Coffee-Gym includes two major components: (1) Coffee, a dataset containing humans’ code edit traces for coding questions and human-written feedback for editing erroneous code; (2) CoffeeEval, a reward function that faithfully reflects the helpfulness of feedback by assessing the performance of the revised code in unit tests. With them, Coffee-Gym addresses the unavailability of high-quality datasets for training feedback models with RL, and provides more accurate rewards than the SOTA reward model (i.e., GPT-4). By applying Coffee-Gym, we elicit feedback models that outperform baselines in enhancing open-source code LLMs’ code editing, making them comparable with closed-source LLMs. We make the dataset and the model checkpoint publicly available in https://huggingface.co/spaces/Coffee-Gym/Project-Coffee-Gym.

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Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Minjin Kim | Minju Kim | Hana Kim | Beong-woo Kwak | SeongKu Kang | Youngjae Yu | Jinyoung Yeo | Dongha Lee
Findings of the Association for Computational Linguistics: ACL 2024

Conversational recommender systems are an emerging area that has garnered increasing interest in the community, especially with the advancements in large language models (LLMs) that enable sophisticated handling of conversational input. Despite the progress, the field still has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such challenges, we present a novel conversational recommendation dataset named PEARL, synthesized with persona- and knowledge-augmented LLM simulators. We obtain detailed persona and knowledge from real-world reviews and construct a large-scale dataset with over 57k dialogues. Our experimental results demonstrate that PEARL contains more specific user preferences, show expertise in the target domain, and provides recommendations more relevant to the dialogue context than those in prior datasets. Furthermore, we demonstrate the utility of PEARL by showing that our downstream models outperform baselines in both human and automatic evaluations. We release our dataset and code.

2022

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Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
Yu Jin Kim | Beong-woo Kwak | Youngwook Kim | Reinald Kim Amplayo | Seung-won Hwang | Jinyoung Yeo
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Commonsense reasoning systems should be able to generalize to diverse reasoning cases. However, most state-of-the-art approaches depend on expensive data annotations and overfit to a specific benchmark without learning how to perform general semantic reasoning. To overcome these drawbacks, zero-shot QA systems have shown promise as a robust learning scheme by transforming a commonsense knowledge graph (KG) into synthetic QA-form samples for model training. Considering the increasing type of different commonsense KGs, this paper aims to extend the zero-shot transfer learning scenario into multiple-source settings, where different KGs can be utilized synergetically. Towards this goal, we propose to mitigate the loss of knowledge from the interference among the different knowledge sources, by developing a modular variant of the knowledge aggregation as a new zero-shot commonsense reasoning framework. Results on five commonsense reasoning benchmarks demonstrate the efficacy of our framework, improving the performance with multiple KGs.