Papers by Sihao Chen
Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection (2021.naacl-main)
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| Challenge: | Recent studies have shown that current models are prone to generating unfaithful summaries . a proposed method is effective in identifying and correcting extrinsic hallucinations . |
| Approach: | They propose a model-agnostic post-processing technique to correct unfaithful summaries . they generate alternative candidates where names and quantities are replaced with compatible ones . |
| Outcome: | The proposed method corrects extrinsic hallucinations in unfaithful summaries. |
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations (2024.naacl-long)
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Sihao Chen, Hongming Zhang, Tong Chen, Ben Zhou, Wenhao Yu, Dian Yu, Baolin Peng, Hongwei Wang, Dan Roth, Dong Yu
| Challenge: | Sentence embeddings are typically learned to recognize the semantic relation between two text inputs. |
| Approach: | They introduce a contrastively-learned contextual embedding model for fine-grained semantic representation of text. |
| Outcome: | The proposed model is able to produce contextual embeddings corresponding to different atomic propositions, i.e. semantic equivalence between propositions across different text sequences. |
Design Challenges for a Multi-Perspective Search Engine (2022.findings-naacl)
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| Challenge: | a document retrieval system fails to deliver diverse and direct responses to controversial questions . classical document retrievals provide a ranked list of references to relevant but not necessarily trustworthy web documents . |
| Approach: | They propose a perspective-oriented document retrieval paradigm to address these challenges . they propose sponses with different perspectives within topically-related web documents . |
| Outcome: | The proposed system is based on a user survey and a prototype . it will be used to assess the utility and understanding of the system . |
Talking Point based Ideological Discourse Analysis in News Events (2025.findings-acl)
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Nishanth Sridhar Nakshatri, Nikhil Mehta, Siyi Liu, Sihao Chen, Daniel Hopkins, Dan Roth, Dan Goldwasser
| Challenge: | Existing models of ideological discourse analysis fail to capture the key elements that shape real-world narratives and lack the ability to integrate contextual information required for understanding abstract ideological views. |
| Approach: | They propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events. |
| Outcome: | The proposed framework can generate ideology-specific viewpoints (partisan perspectives) it can be used to generate event snapshots, a visual way of interpreting event discourse. |
Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries (2023.findings-emnlp)
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| Challenge: | Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams. |
| Approach: | They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. |
| Outcome: | The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis . |
PerspectroScope: A Window to the World of Diverse Perspectives (P19-3)
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| Challenge: | PerspectroScope is a web-based system that lets users query a discussion-worthy natural language claim . |
| Approach: | They propose a web-based system which lets users query a discussion-worthy natural language claim and extract and visualize various perspectives in support or against the claim. |
| Outcome: | The proposed system lets users query a discussion-worthy natural language claim and extract and visualize various perspectives in support or against the claim. |
MixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity (2024.emnlp-main)
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| Challenge: | Recent studies show the importance of document retrieval in the scientific domain. |
| Approach: | They propose a zero-shot approach to measure query-document similarity using atomic components in queries and documents to combine them into a united score. |
| Outcome: | The proposed approach outperforms previous document retrieval methods by 24.7%, 9.8%, and 6.9% on nDCG@5 with unsupervised, supervised, and LLM-based retrievers. |
On Reference (In-)Determinacy in Natural Language Inference (2025.findings-naacl)
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Sihao Chen, Chaitanya Malaviya, Alex Fabrikant, Hagai Taitelbaum, Tal Schuster, Senaka Buthpitiya, Dan Roth
| Challenge: | Using reference determinacy, models fail to recognize context mismatch in NLI examples . |
| Approach: | They propose a benchmark to identify reference ambiguity in NLI examples . they propose RD as a possible assumption for natural language inference . |
| Outcome: | The proposed benchmark identifies reference ambiguity in natural language inference examples . 80% false contradiction and >50% entailment predictions are found . |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition (2023.findings-acl)
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| Challenge: | Existing systems for Natural Language Inference (NLI) only recognize textual entailment relations on sentence-level . however, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence . |
| Approach: | They propose a system to recognize whether one text is textually entailed by another . they use a corpus of over 45K propositions annotated by human raters to study the textual entailment relation of each proposition in a sentence individually. |
| Outcome: | The proposed dataset can be used to understand the compositionality of NLI labels. |
LogiCoL: Logically-Informed Contrastive Learning for Set-based Dense Retrieval (2025.emnlp-main)
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| Challenge: | Current dense retrievers struggle with queries with logical connectives, a use case that is often overlooked but important in downstream applications. |
| Approach: | They propose a logically-informed contrastive learning objective for dense retrievers that learns to respect the subset and mutually exclusive set relation between query results. |
| Outcome: | The proposed model improves retrieval performance and consistency on entity retrieval tasks. |
MultiOpEd: A Corpus of Multi-Perspective News Editorials (2021.naacl-main)
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| Challenge: | a news editorial is a genre of persuasive text where argumentation structure is usually implicit. |
| Approach: | They propose an open-domain news editorial corpus that supports automatic perspective discovery by identifying and abstracting natural language perspectives from editorials. |
| Outcome: | The proposed system supports automatic perspective discovery tasks in news editorials. |
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims (N19-1)
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| Challenge: | a number of fact checking techniques are used to identify and eliminate biases in text data. |
| Approach: | They propose to use search engines to expand and diversify a dataset of claims, perspectives and evidence to address a selection bias. |
| Outcome: | The proposed approach outperforms existing methods in a language understanding task. |
Dense X Retrieval: What Retrieval Granularity Should We Use? (2024.emnlp-main)
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| Challenge: | a learned dense retrieval model is often overlooked when using a corpus for inference, resulting in a design choice of retrieval unit . granularity of retrievals is important for both retrieval and downstream tasks . |
| Approach: | They propose a retrieval unit for dense retrieval that uses propositions to index corpus . propositions are defined as atomic expressions within text, each encapsulating a distinct factoid . |
| Outcome: | The proposed retrieval unit outperforms passage-level units on retrieval and downstream tasks. |
WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)
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Taiwei Shi, Zhuoer Wang, Longqi Yang, Ying-Chun Lin, Zexue He, Mengting Wan, Pei Zhou, Sujay Kumar Jauhar, Sihao Chen, Shan Xia, Hongfei Zhang, Jieyu Zhao, Xiaofeng Xu, Xia Song, Jennifer Neville
| Challenge: | Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences. |
| Approach: | They propose a framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. |
| Outcome: | The proposed framework identifies and classifies user feedback to LLM responses between conversation turns and creates examples of preferred and dispreferred responses according to user preferences. |
The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)
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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi
| Challenge: | Recent studies show that malicious prompt instructions could solicit objectionable content from LLMs. |
| Approach: | They compare how state-of-the-art LLMs respond to malicious prompts in different languages . they find that LLM's generate unsafe responses more often when a prompt is written in a lower-resource language . |
| Outcome: | The proposed model can generate unsafe responses more often when a malicious prompt is written in a lower-resource language, and less irrelevant responses when written in lower-source languages. |
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters (2022.findings-emnlp)
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| Challenge: | Recent advances in modeling and datasets demonstrate promising performance for NLI. |
| Approach: | They explore the direct zero-shot applicability of NLI models to real applications . they analyze the robustness of models to longer and out-of-domain inputs . |
| Outcome: | The proposed models are robust to longer and out-of-domain inputs and can perform on full documents. |
ExpertQA: Expert-Curated Questions and Attributed Answers (2024.naacl-long)
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| Challenge: | a recent study examined the attribution and factuality of language models in domains . experts from various fields are using large language models for information-seeking scenarios . |
| Approach: | They evaluate language models' attribution and factuality by bringing domain experts in the loop . they collect expert-curated questions from 484 participants across 32 fields of study . |
| Outcome: | The results show that language models can provide factually correct answers in high-stakes fields, but they can also be harmful to experts. |
Teaching Language Models To Gather Information Proactively (2025.findings-emnlp)
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| Challenge: | Large language models are often defaulted to passive responses or narrow clarifications when faced with incomplete or under-specified prompts. |
| Approach: | They propose a new task paradigm where LLMs must identify gaps in context and strategically elicit implicit user knowledge through targeted questions. |
| Outcome: | The proposed framework outperforms o3-mini on evaluation metrics and human annotators favor clarification questions and final outlines. |