Papers with domain-agnostic
HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs (2024.eacl-srw)
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| Challenge: | Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chatbot applications. |
| Approach: | They propose a framework that combines benchmarking LLMs’ hallucination tendencies with efficient hallucinian detection. |
| Outcome: | The proposed framework provides opportunities to test and improve LLMs and can generate benchmarking datasets tailored to specific domains. |
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (P19-1)
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| Challenge: | Existing models that use a large-scale knowledge graph to create a conversational reasoning model are domain-agnostic and scalable. |
| Approach: | They propose a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph to introduce engaging and contextually diverse entities and attributes. |
| Outcome: | The proposed model retrieves more natural responses than state-of-the-art models in both in-domain and cross-domain tasks. |
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights (2024.findings-emnlp)
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| Challenge: | despite its importance, little work exists on modelling rigour in scientific writing . despite widespread use of term, scientific literature lacks definition of rigor . |
| Approach: | They propose a framework to automatically identify and define rigour criteria and assess their relevance in scientific writing. |
| Outcome: | The proposed framework can be tailored to the evaluation of scientific rigour for different areas. |
ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly (2025.coling-main)
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Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, Jordan Boyd-Graber
| Challenge: | Existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s needs. |
| Approach: | They propose a framework that uses human-in-the-loop refinement to adapt to changing user questions. |
| Outcome: | The proposed framework is domain-agnostic, responsive, efficient for helping users access useful information while quickly reorganizing information in response to evolving information needs. |
A Large-Scale Corpus of E-mail Conversations with Standard and Two-Level Dialogue Act Annotations (2020.coling-main)
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| Challenge: | e-mail conversations have domain-agnostic and two-level dialogue act annotations . et al. (2017): a better understanding of asynchronous conversations. |
| Approach: | They present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act annotations . they use ISO standard 24617-2 as the annotation scheme to annotate over 6,000 messages and 35,000 sentences . |
| Outcome: | The proposed model outperforms other neural networks but falls short of human performance. |
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have improved IE, but their potential for ATE has not been explored. |
| Approach: | They propose a retrieval-based prompting strategy that selects demonstrations according to syntactic rather than semantic similarity in a few-shot setting. |
| Outcome: | The proposed method improves performance on three specialized ATE benchmarks. |
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond (2025.findings-emnlp)
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| Challenge: | Comp-Comp is an iterative benchmarking framework grounded in the principles of comprehensiveness and compactness. |
| Approach: | They propose a benchmark framework that incorporates the principle of comprehensiveness and compactness. |
| Outcome: | The proposed framework is domain-agnostic and adaptable to a wide range of specialized fields. |