| Challenge: | Recent Open Information Extraction systems allow us to extract ever larger (yet incomplete) open-domain Knowledge Bases from text. |
| Approach: | They propose a baseline model which gives competitive results in a previously defined protocol and provides an independent source of signal to judge arbitrary fact plausibility. |
| Outcome: | The proposed model gives competitive results in the previously defined protocol and provides an independent source of signal to judge arbitrary fact plausibility. |
Similar Papers
Scalable Construction and Reasoning of Massive Knowledge Bases (N18-6)
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| Challenge: | Existing knowledge mining systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. |
| Approach: | They introduce how to extract structured facts from text corpora to construct knowledge bases. |
| Outcome: | The proposed methods are weakly-supervised and domain-independent for knowledge base construction across various domains. |
Estimating Knowledge in Large Language Models Without Generating a Single Token (2024.emnlp-main)
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| Challenge: | Existing methods to evaluate knowledge in large language models require querying and evaluating the model's generated responses. |
| Approach: | They ask whether it is possible to estimate how knowledgeable a model is about a subject entity only from its internal computation. |
| Outcome: | The proposed model performs well with QA accuracy and FActScore . it can be leveraged to guide decisions on how to apply further training or augment queries with retrieval. |
IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models (2022.emnlp-main)
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| Challenge: | Recent studies show pre-trained LMs store linguistic and relational knowledge . pre-training LM models can answer "fill-in-the-blank" questions based on pre-defined relations . |
| Approach: | They propose an open information extraction benchmark for pre-trained language models . they turn pre-trained LMs into zero-shot OIE systems to examine open relational information . |
| Outcome: | The proposed benchmark outperforms state-of-the-art methods on factual OIE datasets without training sets. |
Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) produce incomplete or selectively omit key information . omissions of key information or misrepresentation of conflicting evidence can cause harm . |
| Approach: | They propose a method that decomposes texts into atomic statements and uses natural language inference to identify missing facts and a Q A-based metric that extracts question-answer pairs and compares responses across sources. |
| Outcome: | The proposed evaluation metrics show they perform better than more complex metrics, but at a cost. |
Improving Open Information Extraction via Iterative Rank-Aware Learning (P19-1)
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| Challenge: | Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. |
| Approach: | They propose an additional binary classification loss to calibrate the extraction likelihood . they propose an iterative learning process where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. |
| Outcome: | Experiments on open information extraction (IE) show that the extraction likelihood is not well calibrated when comparing quality of extracted assertions. |
Generating Benchmarks for Factuality Evaluation of Language Models (2024.eacl-long)
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Dor Muhlgay, Ori Ram, Inbal Magar, Yoav Levine, Nir Ratner, Yonatan Belinkov, Omri Abend, Kevin Leyton-Brown, Amnon Shashua, Yoav Shoham
| Challenge: | Existing methods for factuality evaluation of LLM generation focus on facts sampled from the LM itself and might under-represent domain specific or rare facts. |
| Approach: | They propose a method that transforms a factual corpus into a benchmark evaluating an LM's propensity to generate true facts from the corpus . |
| Outcome: | The proposed framework transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. |
Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)
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| Challenge: | Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. |
| Approach: | They propose a framework for evaluating claim extraction in the context of fact-checking . they also introduce Claimify, an LLM-based claim extraction method . |
| Outcome: | The proposed evaluation framework outperforms existing methods in the evaluation of claim extraction methods. |
IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)
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| Challenge: | Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities . |
| Approach: | They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens. |
| Outcome: | The proposed model improves the performance of LLMs for IE with zero-shot generalization. |
Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction (2022.emnlp-main)
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| Challenge: | Open information extraction (OIE) is the task of extracting facts from natural language text. |
| Approach: | They propose a method for computing syntactically rich text embeddings using the structure of dependency trees and a discriminative approach to OIE where tokens in the generated fact are classified as "real" and "fake" they propose to reduce repetitive tokens and improve models' ability to generate implicit facts by a factor of 23%. |
| Outcome: | The proposed method reduces repetitive tokens by a factor of 23% on the CaRB, OIE2016, and LSOIE datasets and improves on augmented datasets. |
A Survey on Open Information Extraction (C18-1)
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| Challenge: | Existing approaches to open information extraction (Open IE) focus on narrow, well-defined requests over a predefined set of target relations on small, homogeneous corpora. |
| Approach: | They propose to use unsupervised methods to extract all types of relations found in text . they propose to implement a system that can be automated to detect possible relations . |
| Outcome: | The proposed approaches have been compared with existing methods and are based on the results of a literature review. |