Papers with open-domain tasks
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering (2023.emnlp-industry)
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Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Bo Qiao, Jue Zhang, Mohit Garg, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Large Language Models (LLMs) have gained popularity but lack specific domain knowledge in domain-specific tasks. |
| Approach: | They propose a model interaction paradigm that empowers LLM to achieve better performance on domain-specific tasks where it is not proficient. |
| Outcome: | The proposed approach outperforms the commonly used LLM with retrieval methods in domain-specific tasks. |
Questions Are All You Need to Train a Dense Passage Retriever (2023.tacl-1)
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| Challenge: | Existing methods for dense retrieval require large supervised datasets with custom hard-negative mining and denoising of positive examples. |
| Approach: | They propose a new corpus-level autoencoding approach for training dense retrieval models that does not require labeled training data. |
| Outcome: | The proposed method matches or surpasses strong supervised performance levels on multiple QA benchmarks with no labeled training data or task-specific losses. |
AutoKB: Automated Creation of Structured Knowledge Bases for Domain-Specific Support (2025.naacl-industry)
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| Challenge: | Effective customer support requires domain-specific solutions tailored to users’ issues. |
| Approach: | They propose an automated pipeline for building a domain-specific KB with a hierarchical tree structure that maps user issues to precise and domain-compliant solutions. |
| Outcome: | Experiments in troubleshooting and medical domains show that the proposed pipeline outperforms LLMs and unstructured knowledge bases and is 75 times more cost-effective than manual methods. |
Optimizing Retrieval-augmented Reader Models via Token Elimination (2023.emnlp-main)
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| Challenge: | Existing methods for ODQA use a retrieval-augmented language model . a generative model can cause a significant bottleneck in decoding time . |
| Approach: | They propose to eliminate some of the retrieved information that might not contribute essential information to the answer generation process. |
| Outcome: | The proposed method reduces run-time by up to 62.2% with only 2% reduction in performance and improves performance. |
TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval (2022.findings-acl)
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| Challenge: | Existing methods for entity retrieval use mention boundaries but unstructured text . state-of-the-art methods struggle to retrieve rare entities for ambiguous mentions . |
| Approach: | They propose a method to jointly train bi-encoders on knowledge graph types and unstructured text for entity retrieval for open-domain tasks. |
| Outcome: | The proposed method improves retrieval of rare entities on Ambiguous Entity Retrieval sets while maintaining strong overall retrieval performance on open-domain tasks. |
Distilling Cross-Modal Knowledge into Domain-Specific Retrievers for Enhanced Industrial Document Understanding (2025.emnlp-industry)
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Jinhyeong Lim, Jeongwan Shin, Seeun Lee, Seongdeok Kim, Joungsu Choi, Jongbae Kim, Chun Hwan Jung, Youjin Kang
| Challenge: | Retrieval-Augmented Generation (RAG) has shown strong performance in open-domain tasks, but its effectiveness in industrial domains is limited by a lack of domain understanding and document structural elements (DSE) such as tables, figures, charts, and formula. |
| Approach: | They propose a knowledge distillation framework that transfers complementary knowledge from Large Language Models and Vision-Language Models into a compact domain-specific retriever. |
| Outcome: | The proposed framework outperforms larger baselines while requiring significantly less computational complexity. |
How Chain-of-Thought Works? Tracing Information Flow from Decoding, Projection, and Activation (2026.findings-acl)
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| Challenge: | Chain-of-Thought (CoT) prompting significantly enhances model reasoning, yet its internal mechanisms remain poorly understood. |
| Approach: | They reversely traced information flow across decoding, projection, and activation phases and found that CoT may serve as a decoding space pruner . |
| Outcome: | The proposed framework can be used to design more efficient and robust prompts. |
Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP (2021.acl-long)
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| Challenge: | Existing studies show that retrievers underperform on rarer entities that share a name . open-domain tasks require a knowledge source to perform reasoning and produce an answer . |
| Approach: | They propose an evaluation benchmark for retrieving entities that share a name . they define Ambiguous Entity Retrieval sets as a collection of entities that have a common name - and query about those entities. |
| Outcome: | The proposed sets underperform on rarer entities that share a name . the retrievers exhibit popularity bias, and are twice as likely to retrieve erroneous documents . |
Automatic Engineering of Long Prompts (2024.findings-acl)
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| Challenge: | Recent research has explored automatic prompt engineering for short prompts, typically consisting of one or a few sentences. |
| Approach: | They propose an algorithm that automatically improves long prompts by combining a greedy algorithm with beam-search to enhance the effectiveness of LLM-based mutation. |
| Outcome: | The proposed algorithm achieves 9.2% accuracy gain on eight tasks in Big Bench Hard and consistent improvements on GSM8K with various models. |
Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Existing RAG models are sensitive to the order in which evidence is presented, resulting in unstable performance and biased reasoning. |
| Approach: | They propose to quantify position bias in multimodal RAG systems by using position sensitivity index . they also develop a visualization framework to trace attention allocation patterns across decoder layers . |
| Outcome: | The proposed framework shows that multimodal interactions intensify position bias compared to unimodal settings and that this bias increases logarithmically with retrieval range. |
Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)
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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang
| Challenge: | Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios . |
| Approach: | They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text . |
| Outcome: | The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task . |