Papers by Jongwuk Lee
From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have recently exhibited performance gains owing to a wide variety of prompting techniques, including Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT), and In-Context Learning (ICL). |
| Approach: | They propose a prompt compression method that captures the global context without compromising semantic consistency while detouring the necessity of pseudo-labels for training the compressor. |
| Outcome: | Empirical results show that the proposed method retains key contexts while reducing the prompt length by 80%. |
It Ain’t Over: A Multi-aspect Diverse Math Word Problem Dataset (2023.emnlp-main)
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| Challenge: | Existing studies lack diversity in problem types, lexical usage patterns, languages, and intermediate solution forms for the math word problem. |
| Approach: | They propose a new MWP dataset with a wide range of diversity in problem types, lexical usage patterns, languages, and intermediate solutions. |
| Outcome: | The proposed dataset provides an opportunity to evaluate the capability of large language models. |
Enhancing Time Awareness in Generative Recommendation (2025.findings-emnlp)
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| Challenge: | Existing models focus on sequential order of items and neglect to handle temporal dynamics . existing models neglect to capture hidden user preferences via various temporal signals . |
| Approach: | They propose a model that generates recommendations into a text-to-text generation task . they introduce Time-aware Prompting and Trend-awful Inference . |
| Outcome: | The proposed model outperforms state-of-the-art models with gains of 15.4% and 14.3% . it is based on time-aware Prompting and Trend-awful Inference . |
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories (2021.naacl-main)
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| Challenge: | Existing studies have developed computational models to recognize metaphorical words in sentences. |
| Approach: | They propose a model that leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical. |
| Outcome: | The proposed model outperforms baseline models on four benchmark datasets . it leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical. |
Multi-view-guided Passage Reranking with Large Language Models (2025.emnlp-main)
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| Challenge: | Existing models rely on autoregressive generation and sliding window strategies to rank passages, which incur heavy computational overhead as the number of passages increases. |
| Approach: | They propose a non-generative LLM-based reranking method that encodes query-passage information into diverse view embeddings without being influenced by external biases. |
| Outcome: | The proposed model matches the performance of much larger 7B-scale fine-tuned models while achieving a 100x reduction in inference latency. |
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval (2025.acl-long)
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| Challenge: | Existing methods for table-text retrieval are limited due to the need to bridge structured tables and unstructured passages. |
| Approach: | They propose a table-text retrieval system that combines the strengths of both approaches . they propose bipartite subgraph retrieval and query-relevant node expansion . |
| Outcome: | The proposed method outperforms state-of-the-art models with a 42.6% and 39.9% improvement on the OTT-QA benchmark. |
GLEN: Generative Retrieval via Lexical Index Learning (2023.emnlp-main)
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| Challenge: | Existing methods for document retrieval bypass auxiliary index structures and can be optimized through end-to-end learning. |
| Approach: | They propose a method to generate a relevant document's identifier using an index learning strategy. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance on benchmark datasets. |
From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines (2026.acl-industry)
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| Challenge: | Existing methods that optimize for relevance overlook document trustworthiness . Generative information retrieval (GenIR) is a promising paradigm for retrieval tasks . |
| Approach: | They propose an Authority-aware Generative Retriever (AuthGR) that incorporates authority into GenIR. |
| Outcome: | The proposed framework improves authority and accuracy in real-world user engagement and reliability. |
GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion (2025.acl-long)
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| Challenge: | Existing studies rely on item metadata to construct abbreviated item IDs, leading to a loss of valuable details. |
| Approach: | They propose a Generative Recommender via semantic-aware multi-granular late fusion to integrate rich semantics efficiently with minimal information loss. |
| Outcome: | The proposed model outperforms eight state-of-the-art recommendation models on four benchmark datasets and achieves significant improvements of 11.5-16.0% in Recall@5 and 5.3-13.6% in NDCG@5. |
Empowering Retrieval-based Conversational Recommendation with Contrasting User Preferences (2025.naacl-long)
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| Challenge: | Existing CRSs assume positive and negative user preferences, but assume that the entities in the dialogue history are positive. |
| Approach: | They propose a conversational recommender model that captures user sentiments and uses the reasoning capacity of the LLMs to extract user's hidden preferences. |
| Outcome: | The proposed model outperforms existing methods in three benchmark datasets, improving up to 99.72% in Recall@10. |
Conflict-Aware Soft Prompting for Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Existing studies show that REtrieval-augmented generation (RAG) fails to resolve the conflict between incorrect external context and correct parametric knowledge. |
| Approach: | They propose a conflict-aware REtrieval-augmented generation system that encodes external context into compact memory embeddings and captures a guidance signal that directs reasoning toward the more reliable knowledge source. |
| Outcome: | Extensive experiments show that CARE effectively mitigates context-memory conflicts, leading to an average performance gain of 5.0% on QA and fact-checking benchmarks. |
Multi-Granularity Guided Fusion-in-Decoder (2024.findings-naacl)
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| Challenge: | Open-domain question answering requires deriving factual responses without explicit evidence . recent approaches combine retrieval of relevant information with response generation . |
| Approach: | They propose a model that concatenates multiple contexts in the decoding phase . they propose MGFiD, which harmonizes passage re-ranking with sentence classification . |
| Outcome: | The proposed model outperforms existing models on Natural Questions and TriviaQA datasets . it aggregates evident sentences into an anchor vector that instructs the decoder . |