Papers by Dohyeon Lee
Query-focused Referentiability Learning for Zero-shot Retrieval (2025.naacl-long)
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| Challenge: | Existing dense representations have shown limitations in zero-shot scenarios . however, passage representations fail to align with their gold queries . |
| Approach: | They propose a query-focused concept of 'referentiable' which ensures passage representations are referenced by their gold queries. |
| Outcome: | The proposed model outperforms existing models on the BEIR benchmark. |
PLM-based World Models for Text-based Games (2022.emnlp-main)
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| Challenge: | a new study shows that pre-trained world models provide a strong base for world models . worldformer is a text-based game environment that can be used to learn world models in text-driven games. |
| Approach: | They propose to use pre-trained language models to build world models in text-based game environments. |
| Outcome: | The proposed model outperforms state-of-the-art model-free algorithms in Atari games while retaining sample efficiency. |
Chaining Event Spans for Temporal Relation Grounding (2024.eacl-long)
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| Challenge: | Existing approaches to understanding temporal relations between events have relied on answer overlaps as a proxy label to distinguish similar and dissimilar questions. |
| Approach: | They propose a timeline reasoning network that elicits proper reasoning behaviors through a module for predicting time spans of events. |
| Outcome: | The proposed approach outperforms existing methods by resolving spurious overlaps using the predicted timeline. |
D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval (2026.eacl-industry)
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| Challenge: | Existing GR systems rely on offline DocID assignment and constrained decoding . offline Doc ID assignment and decoding often prevents GR from capturing query-specific intent . |
| Approach: | They propose a mechanism that adaptively refines DocIDs through query-informed identifier expansion. |
| Outcome: | The proposed mechanism improves retrieval accuracy on unseen and multi-intent documents. |
On Complementarity Objectives for Hybrid Retrieval (2023.acl-long)
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| Challenge: | Existing approaches to hybrid retrieval focus on sparse models to capture “residual” features neglected in spars. |
| Approach: | They propose a new objective to capture a fuller notion of complementarity . they propose to improve the model's Ratio of Complementarity to improve RoC . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative IR benchmarks with statistical significance. |
DADA: Distribution-Aware Domain Adaptation of PLMs for Information Retrieval (2024.findings-acl)
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| Challenge: | Pre-trained language models struggle with out-of-domain data due to distribution shifts . generative domain adaptation (DA) methods have been proposed to address these issues . |
| Approach: | They propose a distribution-aware domain adaptation method to address distribution shifts in domains . they use observation-level feedback and observation- level feedback to adapt to the target domain . |
| Outcome: | The proposed method adapts to the domain distribution knowledge at the level of a single document and the corpus and expands document representation to unseen gold query terms using domain and observation feedback. |
HIL: Hybrid Isotropy Learning for Zero-shot Performance in Dense retrieval (2024.naacl-long)
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| Challenge: | Recent advances in dense retrieval models have brought ColBERT to prominence in information retrieval, but it is underperforming in zero-shot tasks. |
| Approach: | They propose a Hybrid Isotropy Learning architecture that integrates isotropic and anisotropic representations to improve zero-shot retrieval performance. |
| Outcome: | The proposed model outperforms the baseline ColBERT model in BEIR benchmarks. |
ScriptMix: Mixing Scripts for Low-resource Language Parsing (2024.naacl-long)
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| Challenge: | Existing work has considered transliteration and vocabulary augmentation, but the consideration of combining the two has been lacking. |
| Approach: | They propose a multilingual pretrained language model that combines two strengths and overcomes the hurdle of combining them. |
| Outcome: | The proposed model improves POS accuracy by 14% and improves DEP LAS score by 5.6%. |
Beyond Markovian Forgetfulness: Episodic Memory for Reasoning-Intensive Retrieval (2026.acl-long)
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| Challenge: | Existing methods for reasoning-intensive information retrieval suffer from inefficiency . Chain-of-Thought (CoT) approaches suffer from lack of token efficiency . Existing models lack episodic memory, which stores the history of prior states . |
| Approach: | They propose an algorithm that enhances state-based frameworks with an episodic memory module that stores the full history of prior states for a query. |
| Outcome: | The proposed model outperforms CoT and state-based models on the BRIGHT benchmark and is highly token-efficient. |
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding (2024.emnlp-main)
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| Challenge: | Existing methods to increase the robustness of pre-trained language models (PLMs) against unseen ASR systems produce noisy inputs for SLU models, which can significantly degrade their performance. |
| Approach: | They propose to introduce ASR-plausible noises into pre-trained language models by cutting off the non-causal effect of noises. |
| Outcome: | The proposed method improves the robustness and generalizability of SLU models against unseen ASR systems by cutting off the non-causal effect of noises. |
From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval (2025.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting often leads to overthinking in large language models . redundant trajectories that revisit similar states and misguided reasoning that diverges from user intent are two key challenges in information retrieval. |
| Approach: | They propose a transition-based reasoning framework that supports early stopping and fine-grained control. |
| Outcome: | The proposed framework improves retrieval performance by 3.4% while reducing token usage by 74.4%. |
ECoRAG: Evidentiality-guided Compression for Long Context RAG (2025.findings-acl)
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| Challenge: | Existing compression methods do not focus on filtering out non-evidential information, which limit performance in LLMs. |
| Approach: | They propose a framework that leverages external documents through Retrieval-Augmented Generation (RAG) to reduce RAG overhead. |
| Outcome: | Experiments show that ECoRAG outperforms existing compression methods on ODQA tasks. |
tRAG: Term-level Retrieval-Augmented Generation for Domain-Adaptive Retrieval (2025.naacl-long)
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| Challenge: | Neural retrieval models suffer when there is a domain shift between training and test data distributions. |
| Approach: | They propose to generate domain-adapted pseudo-queries using large language models (LLMs) to improve term recall of unseen query terms by using term-level Retrieval-Augmented Generation (tRAG). |
| Outcome: | The proposed method significantly improves recall for unseen terms by 10.6% and outperforms LLM and retrieval-augmented generation baselines on overall retrieval performance. |
Robustifying Multi-hop QA through Pseudo-Evidentiality Training (2021.acl-long)
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| Challenge: | Existing approaches to robustify multi-hop question answering models require expensive annotations. |
| Approach: | They propose a method to supervise answers with right reasoning chains without annotations . they compare answers confidence with and without evidence sentences to generate "pseudo-evidentiality" annotations. |
| Outcome: | The proposed model is accurate and robust in multi-hop reasoning. |