Papers by Nikhil Mehta
Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media (2024.findings-emnlp)
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| Challenge: | Experimental results show improvements on Reddit and Twitter data . |
| Approach: | They propose to take advantage of Large Language Models (LLMs) to better identify user communities. |
| Outcome: | The proposed model improves on Reddit and Twitter data and tasks of community detection, bot detection, and news media profiling. |
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022.acl-long)
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| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
| Approach: | They propose to use inference operators to analyze social media for fake news spread to uncover unobserved interactions between documents and users' engagement patterns. |
| Outcome: | The proposed algorithms improve the performance of two fake news detection tasks. |
Assessing and Verifying Task Utility in LLM-Powered Applications (2024.emnlp-main)
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Negar Arabzadeh, Siqing Huo, Nikhil Mehta, Qingyun Wu, Chi Wang, Ahmed Awadallah, Charles Clarke, Julia Kiseleva
| Challenge: | Rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans in their daily tasks. |
| Approach: | They propose a framework to propose criteria tailored to the unique purpose of any given application and propose corresponding criteria for the application. |
| Outcome: | The proposed framework provides a comprehensive assessment of the effectiveness and robustness of two open source datasets including Math Problem solving and ALFWorld House-hold related tasks. |
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. |
Improving Answer Selection and Answer Triggering using Hard Negatives (D19-1)
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| Challenge: | Existing approaches to answer selection and answer triggering have been proposed. |
| Approach: | They propose to use hard negatives with a siamese network and a suitable loss function for answer selection and answer triggering. |
| Outcome: | The proposed model improves on InsuranceQA, SelQA, and an internal QA dataset by 2.3 points over previous baselines. |
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes (2021.naacl-industry)
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| Challenge: | Existing methods for training numeric attributes are based on manual labeling and distant supervision leads to incomplete training annotations. |
| Approach: | They propose a multi-task learning architecture to deal with missing attribute values in training data, removing dependency on manual annotations. |
| Outcome: | The proposed framework improves on 20 numeric attributes extracted from 5 product categories and 3 english marketplaces with language-agnostic performance. |
Aligning Large Language Models with Recommendation Knowledge (2024.findings-naacl)
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Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Hulikal Keshavan, Lukasz Heldt, Lichan Hong, Ed Chi, Maheswaran Sathiamoorthy
| Challenge: | Large language models (LLMs) excel at natural language reasoning, but cannot model complex user-item interactions inherent in recommendation tasks. |
| Approach: | They propose to equip large language models with recommendation-specific knowledge to address this gap by combining Masked Item Modeling and Bayesian Personalized Ranking (BPR) auxiliary task data samples are generated that encode item correlations and user preferences. |
| Outcome: | Experiments on Amazon Toys & Games, Beauty, and Sports & Outdoors show that the proposed method outperforms conventional and LLM-based baselines by significant margins in retrieval. |
An Interactive Framework for Profiling News Media Sources (2024.naacl-long)
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| Challenge: | Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased? |
| Approach: | They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media. |
| Outcome: | The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source . |
Improving Natural Language Interaction with Robots Using Advice (N19-1)
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| Challenge: | Recent studies focus on learning models for physically grounded language understanding tasks such as the blocks world domain. |
| Approach: | They propose a protocol for including advice, high-level observations about the task, which can help constrain the agent’s prediction. |
| Outcome: | The proposed approach can be extended to include advice, high-level observations about the task, and reduce the effort involved in supplying the advice. |
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback (2024.findings-eacl)
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| Challenge: | In many approaches to Natural Language Processing tasks, language is inherently interactive. |
| Approach: | They propose to use human-AI collaboration to improve human-human interaction by providing feedback that the agent can understand and utilize. |
| Outcome: | The proposed task is an interactive grounded language understanding task in a MineCraft-like world. |