Papers by Minsoo Kim
RaDA: Retrieval-augmented Web Agent Planning with LLMs (2024.findings-acl)
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| Challenge: | Agents powered by large language models inherit important limitations such as the restricted context length, dependency on human-engineered exemplars, and insufficient generalization. |
| Approach: | They propose a novel planning method for Web agents that disentangles planning into two stages: for a new given task, it decomposes tasks into high-level subtasks; and then iteratively synthesizes actions based on dynamically retrieved exemplars. |
| Outcome: | The proposed method decomposes tasks into high-level subtasks and iteratively synthesizes actions based on dynamically retrieved exemplars. |
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. |
Enhancing Computation Efficiency in Large Language Models through Weight and Activation Quantization (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are proficient in natural language processing tasks, but their deployment is limited by extensive parameter sizes and computational demands. |
| Approach: | They propose a method to enhance computational efficiency in large language models by 4-bit weight and 8-bit activation quantization. |
| Outcome: | The proposed techniques significantly boost task accuracies to levels comparable with full-precision models. |
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. |
Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents (2025.emnlp-main)
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| Challenge: | Current efforts to bridge the two modes of interaction are reactive, focusing on responding to user inputs rather than coordinating dialogue flows. |
| Approach: | They propose a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. |
| Outcome: | The proposed dataset outperforms baseline models in intent detection and mode transition handling. |
Retrieval-augmented Video Encoding for Instructional Captioning (2023.findings-acl)
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| Challenge: | Instructional videos provide a detailed multimodal context of each procedure in instruction. key-object degeneracy is a problem for machine systems, causing incorrect captions. |
| Approach: | They propose a retrieval-based framework to augment the model representations in the presence of key-object degeneracy. |
| Outcome: | The proposed framework can be extended over baselines using modalities with key-object degeneracy. |
Privacy-Preserving Text Classification on BERT Embeddings with Homomorphic Encryption (2022.naacl-main)
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| Challenge: | Embeddings compress information into low-dimensional vectors, but can leak private information about sensitive attributes of text. |
| Approach: | They propose a method to privatize embeddings based on homomorphic encryption to prevent leakage of sensitive information in the process of text classification. |
| Outcome: | The proposed method can protect embeddings from leakage while preserving their utility on downstream tasks. |
RA-LoRA: Rank-Adaptive Parameter-Efficient Fine-Tuning for Accurate 2-bit Quantized Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) with their extensive parameters and high memory demands are challenging to fine-tune for specific applications with limited resources. |
| Approach: | They propose a method that dynamically adjusts the adapter’s rank using rank-subspace analysis, optimizing performance with fewer parameters. |
| Outcome: | The proposed method improves model accuracy with minimal parameter changes and demonstrates the importance of rank dynamics in optimizing quantized LLMs. |
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. |
Improving Conversational Abilities of Quantized Large Language Models via Direct Preference Alignment (2024.acl-long)
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| Challenge: | Quantization-aware direct preference optimization (QDPO) improves conversational abilities of quantized LLMs . token-flipping is a critical factor for degraded text generation quality . |
| Approach: | They propose a method that aligns quantized LLMs with their full-precision counterparts, improving conversational abilities. |
| Outcome: | The proposed method outperforms established fine-tuning techniques on two instruction-tuned LLMs in various languages and models, setting a new benchmark for conversational chatbot development. |
Relevance-assisted Generation for Robust Zero-shot Retrieval (2023.emnlp-industry)
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| Challenge: | Despite strong in-domain performance, dense retrievers have shown poor generalization to out-of-domain zero-shot tasks where no training queries are available. |
| Approach: | They propose to generate domain-specific pseudo queries for fine-tuning with domain-relevant relevance between PQ and documents. |
| Outcome: | The proposed approach is more robust to domain shifts, validated on BEIR zero-shot tasks. |
InfiniPot: Infinite Context Processing on Memory-Constrained LLMs (2024.emnlp-main)
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| Challenge: | InfiniPot is a KV cache control framework that can handle long input contexts without additional training. |
| Approach: | They propose a KV cache control framework that can handle long input contexts efficiently without additional training. |
| Outcome: | The proposed framework outperforms models trained for long contexts in various NLP tasks and is highly efficient and versatile. |
Disentangling Questions from Query Generation for Task-Adaptive Retrieval (2024.findings-emnlp)
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| Challenge: | Existing work generates synthetic queries from domain-specific documents to jointly train the retriever. |
| Approach: | They propose a query generator that better adapts to wide search intents expressed in the BeIR benchmark. |
| Outcome: | The proposed query generator outperforms baselines and existing models on tasks with underexplored intents while using a query generator 47 times smaller than the previous state-of-the-art. |
Intervention-Based Alignment of Code Search with Execution Feedback (2023.findings-emnlp)
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| Challenge: | Existing code search training datasets approximate text-code co-occurrences as positive execution feedback, but this approximation may misalign models’ retrieval decisions from ground-truth correctness. |
| Approach: | They propose a code intervention-based reinforcement learning approach that perturbs training code to result in misalignment, then tests models’ decisions and corrects them with the execution feedback by reinforcement learning. |
| Outcome: | The proposed method induces the execution feedback from perturbation, without actual execution, and then tests models’ decisions and corrects them with the execution input by reinforcement learning. |
ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent (2023.acl-long)
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| Challenge: | Existing methods for meeting summarization use extract-thengenerate method to select "salient" contents . extract-thangenerates method typically selects "selected" content in a distantly supervised manner . |
| Approach: | They propose a novel extractor-guided method to generate a summary from evidence sentences that "explain" a meeting summary. |
| Outcome: | The proposed method outperforms existing methods with gains of up to 3.13 in the ROUGE-1 score. |
Agent-as-Judge for Factual Summarization of Long Narratives (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated near-human performance in summarization tasks based on traditional metrics such as ROUGE and BERTScore . however, these metrics do not adequately capture critical aspects of summarizing quality, such as factual accuracy, especially for long narratives. |
| Approach: | They propose a framework that evaluates and refines factuality in narrative summarization by leveraging a Character Knowledge Graph extracted from input narrative. |
| Outcome: | The proposed framework evaluates factuality and provides actionable guidance for refinement. |
Collective Relevance Labeling for Passage Retrieval (2022.naacl-main)
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| Challenge: | Existing approaches to improve IR labels are incomplete and require computational overheads. |
| Approach: | They propose to distill knowledge for informed labeling without high computation overheads at evaluation time. |
| Outcome: | The proposed model outperforms state-of-the-art models while distilling the rankings better. |
Learning Contextual Retrieval for Robust Conversational Search (2025.emnlp-main)
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| Challenge: | Effective conversational search requires a deep understanding of user intent across multiple dialogue turns. |
| Approach: | They propose a novel LLM-based retriever that directly incorporates conversational context into the retrieval process. |
| Outcome: | The proposed method outperforms existing methods while incurring no additional inference overhead. |
Understanding and Improving Knowledge Distillation for Quantization Aware Training of Large Transformer Encoders (2022.emnlp-main)
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| Challenge: | Knowledge distillation (KD) has been used for quantization-aware training to improve the ability of a lightweight model with the transferred knowledge from the teacher. |
| Approach: | They propose two methods to improve attention recovery of quantized large Transformers by combining attention-map and attention-output losses. |
| Outcome: | The proposed methods achieve state-of-the-art accuracy for quantized large Transformer encoder models with sub-2-bit weight quantization. |
Teacher Intervention: Improving Convergence of Quantization Aware Training for Ultra-Low Precision Transformers (2023.eacl-main)
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| Challenge: | Quantization-aware training (QAT) is a promising method to lower the implementation cost and energy consumption. |
| Approach: | They propose a method for fast converging QAT of pre-trained Transformers using a layer-wise signal propagation method with the intact signal from the teacher. |
| Outcome: | The proposed method achieves superior accuracy with significantly lower fine-tuning iterations on well-known Transformers of natural language processing as well as computer vision compared to the state-of-the-art methods. |
CoEx – Co-evolving World-model and Exploration (2025.findings-emnlp)
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| Challenge: | Existing LLM agents fail to assimilate new observations into dynamic updates of the world model, leading to divergent and erroneous plans. |
| Approach: | They propose a hierarchical agent architecture that allows LLM planning to co-evolve with a dynamically updated model of the world. |
| Outcome: | The proposed agent outperforms existing agent paradigms in planning and exploration. |
QuBE: Question-based Belief Enhancement for Agentic LLM Reasoning (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to an explosion of interest in their deployment as agents. |
| Approach: | They propose a method that enhances agents’ focus on task-relevant contexts by constructing a belief state via question answering. |
| Outcome: | The proposed method outperforms established baselines and achieves marked improvements on the BeIR zero-shot retrieval benchmark. |