Papers by David Wan
On Positional Bias of Faithfulness for Long-form Summarization (2025.naacl-long)
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| Challenge: | Large language models exhibit positional bias in long-context settings, under-attending to information in the middle. |
| Approach: | They compile eight human-annotated long-form summarization datasets to evaluate faithfulness . they find that LLMs faithfully summarize beginning and end of documents but neglect middle content . |
| Outcome: | The proposed methods show that LLMs under-attend to information in the middle of inputs. |
PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are prone to factually inconsistent statements, known as hallucinations. |
| Approach: | They propose to train a specialized model that detects inconsistencies over text prefixes to improve generation faithfulness by 5-14 F1 points. |
| Outcome: | The proposed model outperforms baseline models by 5-14 F1 points in prefix-level entailment. |
HistAlign: Improving Context Dependency in Language Generation by Aligning with History (2023.emnlp-main)
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| Challenge: | Language models (LMs) can generate hallucinations and incoherent outputs due to their weak context dependency. |
| Approach: | They propose a training approach to ensure good cache alignment so that the model receives useful signals from the history. |
| Outcome: | The proposed approach improves text coherence and faithfulness on diverse language generation tasks. |
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning (2026.eacl-long)
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Nithin Sivakumaran, Justin Chen, David Wan, Yue Zhang, Jaehong Yoon, Elias Stengel-Eskin, Mohit Bansal
| Challenge: | a key strength of human intelligence is the ability to debate and discuss reasoning with others. |
| Approach: | They propose a multi-agent framework that uses disagreements between visual agents to identify useful visual tools that can resolve inter-agency disagreement. |
| Outcome: | The proposed framework beats the strongest baseline on A-OKVQA and MMMU, respectively. |
Evaluating and Improving Factuality in Multimodal Abstractive Summarization (2022.emnlp-main)
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| Challenge: | Current factuality metrics do not account for vision modality, thus are not adequate for vision-and-language summarization. |
| Approach: | They propose a weighted combination of CLIPScore and BERTScore to evaluate factuality for abstractive document summarization. |
| Outcome: | The proposed metric outperforms existing factuality metrics on four factuity metric-evaluation benchmarks and is robust to human judgments. |
FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization (2022.naacl-main)
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| Challenge: | Abstractive summarization models suffer from the problem of hallucinations, where a summary contains facts or entities not present in the original document. |
| Approach: | They propose an abstractive summarization model that addresses the problem of factuality during pre-training and fine-tuning. |
| Outcome: | Experiments on three downstream tasks show that FactPEGASUS significantly improves factuality compared to the original pre-training objective in zero-shot and few-shot settings. |
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization (2023.acl-long)
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| Challenge: | Abstractive summarization is less prone to unfaithfulness issues than abstractive summaries . but, unfaitfulness problems, i.e., hallucinating new information, are still a problem in extractive summarisation . |
| Approach: | They propose a typology with five types of broad unfaithfulness problems that can appear in extractive summaries, including and beyond not-entailment. |
| Outcome: | The proposed metric shows that it detects unfaithful summaries faster than existing faithfulness evaluation metrics. |
LAQuer: Localized Attribution Queries in Content-grounded Generation (2025.acl-long)
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| Challenge: | Existing attribution methods associate entire sentences with source documents, which can be overwhelming for users seeking to fact-check specific claims. |
| Approach: | They propose a task that localizes selected spans of generated output to their corresponding source spans, allowing fine-grained and user-directed attribution. |
| Outcome: | The proposed task localizes selected spans of generated output to their corresponding source spans, allowing fine-grained and user-directed attribution. |
ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization (2024.findings-acl)
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| Challenge: | Recent-proposed evaluation metrics for large language models have a preference-bias . however, such metrics often lack interpretability and only offer a single score . |
| Approach: | They propose a metric that leverages the power of large language models to perform two sub-tasks: decomposing summaries into atomic content units and validating them against the source document. |
| Outcome: | The proposed metric improves faithfulness scores on three summarization evaluation benchmarks by 3% compared to the next-best metric. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
Faithfulness-Aware Decoding Strategies for Abstractive Summarization (2023.eacl-main)
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| Challenge: | Existing studies on faithfulness of abstractive summarization have focused on decoding strategies. |
| Approach: | They propose two faithfulness-aware generation methods to further improve faithfulness . they propose to use a distillation approach to generate faithful summaries with greedy decoding . |
| Outcome: | The proposed methods improve faithfulness across two datasets as evaluated by automatic faithfulness metrics and human evaluation. |
MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration (2025.naacl-long)
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| Challenge: | Multi-agent collaboration among models has shown promise in reasoning tasks but is underexplored in long-form generation tasks like summarization and question-answering. |
| Approach: | They propose a multi-agent multi-model reasoning recipe to improve faithfulness through refinement. |
| Outcome: | The proposed method improves faithfulness and error detection on three summarization datasets and on long-form question-answering tasks. |
Localizing Factual Inconsistencies in Attributable Text Generation (2026.tacl-1)
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Arie Cattan, Paul Roit, Shiyue Zhang, David Wan, Roee Aharoni, Idan Szpektor, Mohit Bansal, Ido Dagan
| Challenge: | Existing methods for detecting hallucinations in model-generated texts fail to pinpoint errors. |
| Approach: | They propose a formalism for localizing factual inconsistencies in attributable text generation . they propose to decompose the generated text into simple question-answer pairs . |
| Outcome: | The proposed method achieves substantial inter-annotator agreement while achieving a substantial consistency score. |
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)
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Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
| Challenge: | Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term. |
| Approach: | They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document . |
| Outcome: | The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document. |