Papers by Eunhwan Park
Ask, Assess, and Refine: Rectifying Factual Consistency and Hallucination in LLMs with Metric-Guided Feedback Learning (2024.eacl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have heralded unprecedented capabilities in information seeking and text generation, but challenges remain regarding citation errors and generating information not present in the evidence (hallucination). |
| Approach: | They propose a framework to assess citation errors and hallucination using an explicit evaluation paradigm to formulate actionable natural language feedback. |
| Outcome: | The proposed approach improves correctness, fluency, and citation quality and reduces hallucinations in the results. |
LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory (2022.acl-short)
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| Challenge: | LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example. |
| Approach: | They propose to use auto-generated prompts and add demonstrations to LM-BFF to improve few-shot fine-tuning of language models with multiple soft demonstrations. |
| Outcome: | The proposed method improves few-shot fine-tuning on eight NLP tasks. |
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data (2023.findings-eacl)
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| Challenge: | Experimental results show that Transformer-based questions have a "long" hybrid sequence over tabular and textual elements, causing long-range reasoning problems. |
| Approach: | They propose a moving average-equipped fusion-in-decoder to handle long-range reasoning problems . they use FiD and EMA to combine different levels of reasoning . |
| Outcome: | Experimental results show that the proposed model increases exact matching and F1 by 1.1 and 1.7 on the blind test set. |
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification (2022.coling-1)
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| Challenge: | Existing graph-based methods for fact verification use semantic graphs, which are based on evidence sentences. |
| Approach: | They propose to use semantic-level graph reasoning to inject its reasoning-enhanced representation into other graph-based and sequence-based reasoning methods. |
| Outcome: | The proposed method outperforms the previous graph-based methods and achieves state-of-the-art performance on a large-scale dataset for Fact Extraction and VERification (FEVER). |
RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion (2024.lrec-main)
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| Challenge: | Large language models (LLMs) have demonstrated superior performance to that of small language models in information retrieval for various subtasks including dense retrieval, reranking, query expansion, and pseudo-document generation. |
| Approach: | They propose a retrieval-augmented model specialization that distills the capability of LLMs to generate the chain-of-thoughts (CoT) for query expansion into a RADCoT. |
| Outcome: | The proposed model can generate the chain-of-thoughts (CoT) for query expansion, reducing the burden of internalizing and retaining world knowledge in model parameters. |
MERLIN: Multimodal Embedding Refinement via LLM-based Iterative Navigation for Text-Video Retrieval-Rerank Pipeline (2024.emnlp-industry)
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| Challenge: | Recent advances in text-video retrieval neglect the crucial user perspective, leading to discrepancies between user queries and content retrieved. |
| Approach: | They propose a novel, training-free pipeline that leverages Large Language Models for iterative feedback learning. |
| Outcome: | Experimental results show that MERLIN significantly outperforms existing systems in video retrieval. |