Papers by Bodhisattwa Prasad Majumder
To Tell The Truth: Language of Deception and Language Models (2024.naacl-long)
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| Challenge: | Existing evidence of people’s ability to discern truth from text-based false information is scarce. |
| Approach: | They propose to use a large language model to learn discernible cues from TV game show data to investigate whether textual cue is more likely to detect fraud . |
| Outcome: | The proposed model detects novel but accurate language cues in many cases where humans failed to detect deception. |
Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding (2020.emnlp-main)
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| Challenge: | Discourse analysis has been limited to small news corpora, but this study is expanding to tens of thousands of interviews. |
| Approach: | They propose a large-scale analysis of discourse in media dialog and its impact on dialog modeling with a focus on interrogative patterns and use of external knowledge. |
| Outcome: | The proposed model outperforms strong discourse-agnostic baselines for dialog modeling, generating more specific and topical responses in interview-style conversations. |
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning (2024.findings-acl)
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Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley
| Challenge: | Motivational Interviewing (MI) requires a system that can infer how to motivate users to adopt positive lifestyle changes. |
| Approach: | They propose a framework that can learn and apply conversation strategies from expert demonstrations by using natural language inductive rules. |
| Outcome: | The proposed framework outperforms in-context demonstrations that are over 50 times longer and can learn natural language strategies from demonstrations. |
Detect and Perturb: Neutral Rewriting of Biased and Sensitive Text via Gradient-based Decoding (2021.findings-emnlp)
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| Challenge: | Written language carries explicit and implicit biases that can distract from meaningful signals; at worst they can lead to unfair outcomes. |
| Approach: | They propose a gradient-based rewriting framework that detects and perturbs sensitive components and regenerates fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes. |
| Outcome: | The proposed framework regenerates fluent alternatives that are neutral in the sensitive attribute while maintaining the semantics of other attributes. |
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations (2023.acl-short)
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| Challenge: | eIA is an adversarial attack that generates inconsistent natural language explanations (NLEs) a model that generate In-NLE is undesirable, as it has a faulty decision-making process or is prone to inconsistencies. |
| Approach: | They propose an off-the-shelf mitigation method to alleviate inconsistencies by grounding the model into external background knowledge. |
| Outcome: | The proposed method reduces inconsistencies detected by previous models . it is based on external knowledge bases and a novel approach to mitigate inconsistent models based upon the proposed method . |
Controlling Bias Exposure for Fair Interpretable Predictions (2022.findings-emnlp)
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| Challenge: | Existing approaches to reduce bias in NLP tasks focus on protecting or isolating information related to a sensitive attribute, but they lack control over how much bias is required to be removed. |
| Approach: | They propose a favorable debiasing method that uses sensitive information ‘fairly’, rather than blindly eliminating it. |
| Outcome: | The proposed method achieves a trade-off between debiasing and task performance along with producing debiased rationales as evidence. |
Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization (2024.findings-acl)
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| Challenge: | Using a set of over 200 WikiHow procedures, we test several simple multi-LLM-agent architectures for customization. |
| Approach: | They propose to use a set of WikiHow procedures to test how-to procedures can be customized by multiple LLMs. |
| Outcome: | The proposed architecture outperforms an end-to-end LLM in the evaluation set of over 200 WikiHow procedures. |
Unsupervised Enrichment of Persona-grounded Dialog with Background Stories (2021.acl-short)
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| Challenge: | Existing dialog models do not contain such narratives, so we propose a gradient-based rewriting technique to enrich dialog personas with relevant background events. |
| Approach: | They propose to use existing dialog datasets to enrich dialog responses with 'background stories' based on a gradient-based rewriting technique which encourages the generated response to be fluent with the dialog history, minimally different from the retrieved story, and consistent with the original persona. |
| Outcome: | The proposed method generates responses that are more diverse and human-like compared to outputs from existing dialog models. |
Social Intelligence in the Age of LLMs (2025.naacl-tutorial)
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| Challenge: | Large Language Models (LLMs) are a powerful tool for integrating human-like communication and context-aware interactions into artificial systems. |
| Approach: | They propose to introduce and overview different aspects of artificial social intelligence and their relationship with LLMs by introducing scientific methods for evaluating social intelligence in LLM. |
| Outcome: | This tutorial will introduce scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. |
Improving Neural Story Generation by Targeted Common Sense Grounding (D19-1)
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| Challenge: | Recent advances in language modeling have yielded thematic and stylistic coherence in story generation through large scale pretraining of Transformer models. |
| Approach: | They propose a multi-task learning scheme to achieve better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. |
| Outcome: | The proposed model achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts dataset. |
Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions (2020.emnlp-main)
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| Challenge: | Existing persona-grounded dialog models fail to capture simple implications of given persona descriptions. |
| Approach: | They propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to expanded and richer set of persona descriptions. |
| Outcome: | The proposed model outperforms baselines on the Persona-Chat dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation. |
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge (2021.naacl-main)
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| Challenge: | Existing models that generate clarification questions fail to identify useful information in contexts . human ability to generate fluent and relevant questions is important in reducing ambiguity . |
| Approach: | They propose a model that first identifies what is missing and then generates a question about it. |
| Outcome: | The proposed model outperforms baselines as judged by automatic metrics and humans. |
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision (2025.naacl-long)
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Zhouhang Xie, Tushar Khot, Bhavana Dalvi Mishra, Harshit Surana, Julian McAuley, Peter Clark, Bodhisattwa Prasad Majumder
| Challenge: | Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purpose of the discovery (i.e., goal). |
| Approach: | They propose a goal-oriented latent factor discovery system that integrates LLM’s instruction-following ability with statistical models to handle large, noisy datasets where LLM reasoning alone falls short. |
| Outcome: | The proposed system improves task performance by 5-52% over baselines and 1.8 times as often as the best alternative, on average, in human evaluation. |
CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation (2025.findings-acl)
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Peter Jansen, Oyvind Tafjord, Marissa Radensky, Pao Siangliulue, Tom Hope, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Daniel S Weld, Peter Clark
| Challenge: | Automated scientific discovery (ASD) systems are limited in their evaluation of software artifacts and large volumes of research artifs are typically evaluated using conference-style paper review with limited evaluation of code. |
| Approach: | They propose a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain. |
| Outcome: | The proposed system returns 19 discoveries on machine-generated ideas in the domain of agents and virtual environments. |
Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection (2022.acl-long)
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| Challenge: | Existing neural dialog models lack specificity and informativeness due to limited knowledge available during training. |
| Approach: | They propose a method to extract relevant knowledge from external sources at decoding time and incorporate it into a dialog response. |
| Outcome: | The proposed method in goal-oriented and knowledge-grounded dialog settings shows that human annotators judge the outputs more engaging and informative compared to responses from prior dialog systems. |
Representation Learning for Information Extraction from Form-like Documents (2020.acl-main)
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| Challenge: | Form-like documents like invoices, purchase orders, tax forms and insurance quotes are common in day-to-day business workflows, but current techniques for processing them largely still employ manual effort or brittle and error-prone heuristics for extraction. |
| Approach: | They propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. |
| Outcome: | The proposed system generates extraction candidates based on neighboring words in the document and is interpretable, as shown using loss cases. |
Evaluating Language Model Pluralism through In-the-wild Crowd Discussions (2026.acl-long)
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Gagan Mundada, Rohan Surana, Nandhini Swaminathan, Bodhisattwa Prasad Majumder, Junda Wu, Julian McAuley, Zhouhang Xie
| Challenge: | Existing evaluation methods focus predominantly on multiple-choice and question-answering tasks, leaving open-ended generation largely unaddressed. |
| Approach: | They propose an evaluation framework that assesses LLM pluralism in open-ended generation by comparing outputs against free-form crowd responses. |
| Outcome: | The proposed evaluation framework decomposes ground-truth responses into atomic, non-overlapping claims and evaluates whether LLMs adequately cover this diverse claim space. |
Generating Personalized Recipes from Historical User Preferences (D19-1)
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| Challenge: | Existing methods to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. |
| Approach: | They propose to expand a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. |
| Outcome: | The proposed model generates plausible recipes from user-aware representations of recipes from 180K recipes and 700K interactions. |