Search Results for author: Bodhisattwa Prasad Majumder

Found 36 papers, 19 papers with code

Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding

no code implementations EMNLP 2020 Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley

In this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and use of external knowledge.

The Good, the Bad, and the Ugly: The Role of AI Quality Disclosure in Lie Detection

no code implementations30 Oct 2024 Haimanti Bhattacharya, Subhasish Dugar, Sanchaita Hazra, Bodhisattwa Prasad Majumder

We investigate how low-quality AI advisors, lacking quality disclosures, can help spread text-based lies while seeming to help people detect lies.

DiscoveryBench: Towards Data-Driven Discovery with Large Language Models

1 code implementation1 Jul 2024 Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Bhavana Dalvi Mishra, Abhijeetsingh Meena, Aryan Prakhar, Tirth Vora, Tushar Khot, Ashish Sabharwal, Peter Clark

Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of provided datasets?

Code Generation Sociology

DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents

1 code implementation10 Jun 2024 Peter Jansen, Marc-Alexandre Côté, Tushar Khot, Erin Bransom, Bhavana Dalvi Mishra, Bodhisattwa Prasad Majumder, Oyvind Tafjord, Peter Clark

However, developing and evaluating an AI agent's capacity for end-to-end scientific reasoning is challenging as running real-world experiments is often prohibitively expensive or infeasible.

Benchmarking scientific discovery

Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning

no code implementations23 Mar 2024 Zhouhang Xie, Bodhisattwa Prasad Majumder, Mengjie Zhao, Yoshinori Maeda, Keiichi Yamada, Hiromi Wakaki, Julian McAuley

We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing.

Instruction Following

Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos

no code implementations8 Mar 2024 Tarun Kalluri, Bodhisattwa Prasad Majumder, Manmohan Chandraker

We introduce LaGTran, a novel framework that utilizes text supervision to guide robust transfer of discriminative knowledge from labeled source to unlabeled target data with domain gaps.

Data-driven Discovery with Large Generative Models

no code implementations21 Feb 2024 Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra, Ashish Sabharwal, Peter Clark

With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially.

scientific discovery

Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills

1 code implementation5 Feb 2024 Kolby Nottingham, Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Sameer Singh, Peter Clark, Roy Fox

We evaluate our method in the classic videogame NetHack and the text environment ScienceWorld to demonstrate SSO's ability to optimize a set of skills and perform in-context policy improvement.

Decision Making Language Modeling +3

Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization

no code implementations16 Nov 2023 Yash Kumar Lal, Li Zhang, Faeze Brahman, Bodhisattwa Prasad Majumder, Peter Clark, Niket Tandon

Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need.

To Tell The Truth: Language of Deception and Language Models

1 code implementation13 Nov 2023 Sanchaita Hazra, Bodhisattwa Prasad Majumder

Text-based misinformation permeates online discourses, yet evidence of people's ability to discern truth from such deceptive textual content is scarce.

Language Modeling Language Modelling +2

CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization

no code implementations16 Oct 2023 Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tandon, Li Zhang, Chris Callison-Burch, Peter Clark

Language agents have shown some ability to interact with an external environment, e. g., a virtual world such as ScienceWorld, to perform complex tasks, e. g., growing a plant, without the startup costs of reinforcement learning.

Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena

1 code implementation9 Oct 2023 Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, Kyle Richardson

Recent advancements in Large Language Models (LLMs) showcase advanced reasoning, yet NLP evaluations often depend on static benchmarks.

Management

Large Language Models as Zero-Shot Conversational Recommenders

1 code implementation19 Aug 2023 Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian McAuley

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.

Conversational Recommendation

KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations

no code implementations5 Jun 2023 Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley, Thomas Lukasiewicz, Oana-Maria Camburu

While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs.

Adversarial Attack

Aligning Language Models to User Opinions

no code implementations24 May 2023 EunJeong Hwang, Bodhisattwa Prasad Majumder, Niket Tandon

An important aspect of developing LLMs that interact with humans is to align models' behavior to their users.

Open-Ended Question Answering

Controlling Bias Exposure for Fair Interpretable Predictions

1 code implementation14 Oct 2022 Zexue He, Yu Wang, Julian McAuley, Bodhisattwa Prasad Majumder

However, when sensitive information is semantically entangled with the task information of the input, e. g., gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve.

Attribute Task 2 +2

InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictions

no code implementations14 Oct 2022 Bodhisattwa Prasad Majumder, Zexue He, Julian McAuley

In the other setup, human feedback was able to disentangle associated bias and predictive information from the input leading to superior bias mitigation and improved task performance (4-5%) simultaneously.

Attribute

On Faithfulness and Coherence of Language Explanations for Recommendation Systems

no code implementations12 Sep 2022 Zhouhang Xie, Julian McAuley, Bodhisattwa Prasad Majumder

Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance.

Recommendation Systems Review Generation

Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection

1 code implementation ACL 2022 Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley

In this paper, we propose a post-hoc knowledge-injection technique where we first retrieve a diverse set of relevant knowledge snippets conditioned on both the dialog history and an initial response from an existing dialog model.

Informativeness Specificity

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

no code implementations25 Jun 2021 Bodhisattwa Prasad Majumder, Oana-Maria Camburu, Thomas Lukasiewicz, Julian McAuley

Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations.

Decision Making

Unsupervised Enrichment of Persona-grounded Dialog with Background Stories

1 code implementation ACL 2021 Bodhisattwa Prasad Majumder, Taylor Berg-Kirkpatrick, Julian McAuley, Harsh Jhamtani

Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich.

Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge

1 code implementation NAACL 2021 Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley, Julian McAuley

The ability to generate clarification questions i. e., questions that identify useful missing information in a given context, is important in reducing ambiguity.

Question Generation Question-Generation

Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions

1 code implementation EMNLP 2020 Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley

Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly.

Diversity

Interview: A Large-Scale Open-Source Corpus of Media Dialog

no code implementations7 Apr 2020 Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley

Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance on existing spoken dialog datasets, demonstrating its usefulness in modeling real-world conversations.

ReZero is All You Need: Fast Convergence at Large Depth

13 code implementations10 Mar 2020 Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao, Garrison W. Cottrell, Julian McAuley

Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties.

Language Modeling Language Modelling

Generating Personalized Recipes from Historical User Preferences

1 code implementation IJCNLP 2019 Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley

Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes.

Recipe Generation Text Generation

Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce

no code implementations29 Mar 2018 Bodhisattwa Prasad Majumder, Aditya Subramanian, Abhinandan Krishnan, Shreyansh Gandhi, Ajinkya More

Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search.

Attribute Attribute Extraction

Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM

1 code implementation16 Oct 2016 Bodhisattwa Prasad Majumder, Ayan Sengupta, Sajal jain, Parikshit Bhaduri

With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc.

Community Detection Fault Detection

Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation

1 code implementation28 Mar 2016 Satrajit Mukherjee, Bodhisattwa Prasad Majumder, Aritran Piplai, Swagatam Das

The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights.

Clustering Image Segmentation +1

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