Papers with information retrieval techniques
Streamlining Biomedical Research with Specialized LLMs (2025.coling-demos)
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| Challenge: | Using large language models, we can generate accurate, context-aware responses with minimal prompts. |
| Approach: | They propose a system that integrates domain-specific large language models with advanced information retrieval techniques to deliver comprehensive and context-aware responses. |
| Outcome: | The proposed system improves quality of dialogue generation and improves efficiency in the biomedical and pharmaceutical domains. |
Improving Toponym Resolution with Better Candidate Generation, Transformer-based Reranking, and Two-Stage Resolution (2023.starsem-1)
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| Challenge: | Geocoding is the task of converting location mentions in text into structured data that encodes the geospatial semantics. |
| Approach: | They propose a new architecture that generates a list of geospatial ontologies and reranks them using a transformer-based neural network. |
| Outcome: | The proposed architecture achieves state-of-the-art on multiple datasets. |
Open-Domain Question Answering with Pre-Constructed Question Spaces (2021.naacl-srw)
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| Challenge: | Open-domain question answering aims at locating answers to user-generated questions in massive collections of documents. |
| Approach: | They propose an algorithm with a novel reader-retriever design that differs from both families of algorithms. |
| Outcome: | The proposed algorithm outperforms retrieval-based methods with two large-scale datasets and is state-of-the-art. |
ReQA: An Evaluation for End-to-End Answer Retrieval Models (D19-58)
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| Challenge: | Popular QA benchmarks like SQuAD have driven progress on identifying answer spans within a specific passage . retrieving relevant answers from a huge corpus of documents is still a challenging problem . |
| Approach: | They propose a benchmark for evaluating large-scale sentence-level answer retrieval models . they establish baselines using both neural encoding models and classical retrieval techniques . |
| Outcome: | The proposed model outperforms human models on identifying answer spans within a specific passage . the proposed model is scalable and can bypass the typical document retrieval step . |
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)
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| Challenge: | Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory . |
| Approach: | They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response . |
| Outcome: | The proposed approach significantly improves the informativeness of the generated responses. |
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification (2024.eacl-long)
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| Challenge: | Existing systems for fact-checking scientific claims assume that the documents containing the evidence are already provided and annotated or contained in a limited corpus. |
| Approach: | They perform an array of experiments to test the performance of open-domain claim verification systems on four datasets of biomedical and health claims in different settings. |
| Outcome: | The proposed system performs better with biomedical and health claims, while Wikipedia is more suited for everyday health concerns. |
Mining Evidences for Concept Stock Recommendation (N18-1)
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| Challenge: | a recent announcement of a state plan to build a new economic region has led to the rise of hundreds of stocks . concepts can be useful for investors to find out relevant concept stocks for making investment decisions . a chinese research team uses deep learning to mine evidences from large textual data . |
| Approach: | They use distributed word similarities and deep reinforcement learning to learn a strategy of topic expansion from large scale textual data. |
| Outcome: | The proposed method outperforms a baseline method on two Chinese stock market datasets. |
Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators (2023.emnlp-main)
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| Challenge: | Large language models outperform information retrieval techniques for downstream knowledge-intensive tasks when being prompted to generate world knowledge. |
| Approach: | They propose a COmpreheNsive kNowledge Evaluation framework to evaluate generated knowledge from six important perspectives . they conduct extensive empirical analysis of generated knowledge on two widely studied knowledge-intensive tasks . |
| Outcome: | The proposed framework evaluates generated knowledge from six important perspectives on two knowledge-intensive tasks. |
GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering (2025.findings-acl)
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| Challenge: | Question answering systems have been used for various domains and languages. |
| Approach: | They propose a novel approach for question answering (QA) that combines a dataset of Romanian legal questions with a CROL corpus of laws. |
| Outcome: | The proposed approach achieves competitive results with generally accepted state-of-the-art methods and even exceeds them in most settings. |
People and Places of Historical Europe: Bootstrapping Annotation Pipeline and a New Corpus of Named Entities in Late Medieval Texts (2023.findings-acl)
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| Challenge: | Pre-trained named entity recognition models are inaccurate on modern corpora due to differences in language OCR errors. |
| Approach: | They develop a named entity recognition (NER) corpus of 3.6M sentences from medieval charters written mainly in Czech, Latin, and German. |
| Outcome: | The proposed model achieves entity-level Precision of 72.81–93.98% with 58.14–81.77% Recall on a manually-annotated test dataset. |