Papers by Benjamin Han
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge (2023.emnlp-demo)
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Farima Fatahi Bayat, Kun Qian, Benjamin Han, Yisi Sang, Anton Belyy, Samira Khorshidi, Fei Wu, Ihab Ilyas, Yunyao Li
| Challenge: | Existing large language models (LLMs) have a tendency to hallucinate and provide creative and fluent responses that are not factually accurate. |
| Approach: | They propose a tool that automatically extracts factual claims from text, gathers evidence from external knowledge sources, evaluates the factuality of each claim, and suggests revisions for identified errors. |
| Outcome: | The proposed tool detects errors in text and evaluates their factuality and suggests revisions based on the collected evidence. |
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness (2023.findings-emnlp)
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| Challenge: | Pre-trained language models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. |
| Approach: | They propose a Knowledge-Enhanced Pre-trained LanguagE model with Topic entity awareness that incorporates the interactions between tokens and mentioned entities in pre-training. |
| Outcome: | The proposed model incorporates the interactions between tokens and mentioned entities in pre-training and is more effective on entity-centric tasks. |
Time Sensitive Knowledge Editing through Efficient Finetuning (2024.acl-short)
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Xiou Ge, Ali Mousavi, Edouard Grave, Armand Joulin, Kun Qian, Benjamin Han, Mostafa Arefiyan, Yunyao Li
| Challenge: | Existing locate-and-edit knowledge editing methods suffer from two limitations: they are infeasible for large scale KE in practice and require long run-time. |
| Approach: | They propose to use parametric fine-tuning techniques to update obsolete knowledge and induce new knowledge into LLMs. |
| Outcome: | The proposed methods improve the performance of KE and knowledge update in a temporal dataset with knowledge update and knowledge injection examples. |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation (2024.findings-naacl)
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| Challenge: | Knowledge-to-text generators often struggle to faithfully generate descriptions for input facts . we propose a decoding-only method to reduce hallucinations . |
| Approach: | They propose a decoding-only method to generate accurate descriptions for input facts . they use a Natural Language Inference model as the model and replace it with a task-specific HVM . |
| Outcome: | The proposed method improves faithfulness with minimal impact on quality and in/out-of-distribution evaluations. |
CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers? (2025.findings-emnlp)
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Jiefu Ou, William Gantt Walden, Kate Sanders, Zhengping Jiang, Kaiser Sun, Jeffrey Cheng, William Jurayj, Miriam Wanner, Shaobo Liang, Candice Morgan, Seunghoon Han, Weiqi Wang, Chandler May, Hannah Recknor, Daniel Khashabi, Benjamin Van Durme
| Challenge: | CLAIMCHECK is an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews from OpenReview. |
| Approach: | They annotate NeurIPS 2023 and 2024 submissions and reviews for weaknesses and dispute them for fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
| Outcome: | The proposed dataset is richly annotated by ML experts for weaknesses statements in the reviews and the claims that they dispute, as well as fine-grained labels of validity, objectivity, and type of the identified weaknesses. |
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models (2025.findings-acl)
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| Challenge: | Existing benchmarks like LOFT often overestimate LCLM performance by providing overly simplified contexts. |
| Approach: | They propose to use retrieval-attention-probing to filter and de-noise long contexts during decoding and joint retrieval head training alongside the generation head to improve LCLM performance. |
| Outcome: | The proposed approach outperforms RAG and GPT-4-Turbo on most tasks despite being a much smaller model. |