Recent Works

(For a full list of publications, see below)

Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization

Language Understanding, Reasoning, and Generation Learning and Language

Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We argue this metric adopts a narrow notion of faithfulness and confuses unfaithfulness with incompleteness, the lossy compression needed to turn distributed transformer computation into a linear natural language narrative. On multi-hop reasoning tasks with instruct-tuned and reasoning models, many CoTs flagged as unfaithful by Biasing Features are judged faithful by other metrics, exceeding 50% in some models. With a new faithful@k metric, we show that larger inference-time budgets greatly increase hint verbalization (up to 90% in some settings), suggesting much apparent unfaithfulness is due to tight token limits.

Kerem Zaman and Shashank Srivastava

Proceedings of Association for Computational Linguistics (ACL) [Oral], 2026.

[pdf]

DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing

Miscellaneous

Current image immunization defense techniques against diffusion-based editing embed imperceptible noise into target images to disrupt editing models. However, these methods face scalability challenges, as they require time-consuming optimization for each image separately, taking hours for small batches. To address these challenges, we introduce DiffVax, a scalable, lightweight, and optimization-free framework for image immunization, specifically designed to prevent diffusion-based editing. Our approach enables effective generalization to unseen content, reducing computational costs and cutting immunization time from days to milliseconds, achieving a speedup of 250,000x. This is achieved through a loss term that ensures the failure of editing attempts and the imperceptibility of the perturbations. Extensive qualitative and quantitative results demonstrate that our model is scalable, optimization-free, adaptable to various diffusion-based editing tools, robust against counter-attacks, and, for the first time, effectively protects video content from editing.

Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali,  and James M. Rehg

International Conference on Learning Representations (ICLR), 2026.

[pdf]

Explaining Differences Between Model Pairs in Natural Language through Sample Learning

Learning and Language

With the growing adoption of machine learning models in critical domains, techniques for explaining differences between models have become essential for trust, debugging, and informed deployment. Previous approaches address this by identifying input transformations that cause divergent predictions (Shah et al., 2022) or by learning joint surrogate models to align and contrast behaviors (Haldar et al., 2023). These methods often require access to training data and do not produce natural language explanations. In this paper, we introduce SLED , a framework that generates faithful natural language explanations of when and how two ML models converge or diverge in their predictions. SLED first uses gradient-based optimization to synthesize input samples that highlight divergence and convergence patterns, and then leverages a large language model (LLM) to generate explanations grounded in these synthetic samples. Across both text-based (3 tasks, 7 models) and structured (10 tasks, 4 models) classification tasks, we show that SLED explanations are 18–24% more faithful than the strongest baselines. User studies also indicate that SLED explanations achieve a real-world simulatability of 63.5%. Importantly, SLED requires minimal access to training data and generalizes well to real-world samples, enabling transparent model comparison.

Advaith Malladi, Yuvraj Jain, Rakesh R Menon,  and Shashank Srivastava

Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2025.

[pdf]

Chameleon LLMs: User Personas Influence Chatbot Personality Shifts

Learning and Language

As large language models (LLMs) integrate into society, their ability to adapt to users is as critical as their accuracy. While prior work has used personality tests to examine the perceived personalities of LLMs, little research has explored whether LLMs adapt their perceived personalities in response to user interactions. We investigate whether and how LLMs exhibit conversational adaptations over prolonged interactions. Using controlled simulations where a user and chatbot engage in dialogue, we measure the chatbot’s perceived personality shifts before and after the conversation. Across multiple models, we find that traits such as Agreeableness, Extraversion, and Conscientiousness are highly susceptible to user influence, whereas Emotional Stability and Intellect remain relatively more stable. Our results suggest that LLMs dynamically adjust their conversational style in response to user personas, raising important implications for model alignment, trust, and safety

Jane Xing, Tianyi Niu,  and Shashank Srivastava

Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2025.

[pdf]

A Causal Lens for Evaluating Faithfulness Metrics

Learning and Language

Large Language Models (LLMs) offer natural language explanations as an alternative to feature attribution methods for model interpretability. However, despite their plausibility, they may not reflect the model’s true reasoning faithfully. While several faithfulness metrics have been proposed, they are often evaluated in isolation, making principled comparisons between them difficult. We present CAUSAL DIAGNOSTICITY, a testbed framework for evaluating faithfulness metrics for natural language explanations. We use the concept of diagnosticity, and employ model-editing methods to generate faithful-unfaithful explanation pairs. Our benchmark includes four tasks: fact-checking, analogy, object counting, and multi-hop reasoning. We evaluate prominent faithfulness metrics, including post-hoc explanation and chainof-thought methods. Diagnostic performance varies across tasks and models, with Filler Tokens performing best overall. Additionally, continuous metrics are generally more diagnostic than binary ones but can be sensitive to noise and model choice. Our results highlight the need for more robust faithfulness metrics.

Kerem Zaman and Shashank Srivastava

Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2025.

[pdf]

INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models

Learning and Language

Large language models (LLMs) excel at answering questions but remain passive learners—absorbing static data without the ability to question and refine knowledge. This paper explores how LLMs can transition to interactive, question-driven learning through studentteacher dialogues. We introduce INTERACT (INTERactive learning for Adaptive Concept Transfer), a framework in which a “student” LLM engages a “teacher” LLM through iterative inquiries to acquire knowledge across 1,347 contexts, including song lyrics, news articles, movie plots, academic papers, and images. Our experiments show that across a wide range of scenarios and LLM architectures, interactive learning consistently enhances performance, achieving up to a 25% improvement, with ‘cold-start’ student models matching static learning baselines in as few as five dialogue turns. Interactive setups can also mitigate the disadvantages of weaker teachers, showcasing the robustness of question-driven learning

Aum Kendapadi, Kerem Zaman, Rakesh R Menon,  and Shashank Srivastava

Findings of Association for Computational Linguistics (ACL), 2025.

[pdf]

 

Full List of Publications

Tags: Learning and Language Datasets and Benchmarks Learning from Limited Labels Neuro-symbolic Learning Fairness and Social Applications Active Learning Language Understanding, Reasoning, and Generation Syntax and Semantics Miscellaneous

2026

  1. Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization
    Kerem Zaman and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL) [Oral], 2026.
    Language Understanding, Reasoning, and Generation Learning and Language
    [pdf], [arXiv]

  2. DiffVax: Optimization-Free Image Immunization Against Diffusion-Based Editing
    Tarik Can Ozden, Ozgur Kara, Oguzhan Akcin, Kerem Zaman, Shashank Srivastava, Sandeep P. Chinchali, and James M. Rehg
    International Conference on Learning Representations (ICLR), 2026.
    Miscellaneous
    [pdf], [arXiv]

2025

  1. Chameleon LLMs: User Personas Influence Chatbot Personality Shifts
    Jane Xing, Tianyi Niu, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2025.
    Learning and Language
    [pdf], [code]

  2. Explaining Differences Between Model Pairs in Natural Language through Sample Learning
    Advaith Malladi, Yuvraj Jain, Rakesh R Menon, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2025.
    Learning and Language
    [pdf]

  3. A Causal Lens for Evaluating Faithfulness Metrics
    Kerem Zaman and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2025.
    Learning and Language
    [pdf], [code]

  4. INTERACT: Enabling Interactive, Question-Driven Learning in Large Language Models
    Aum Kendapadi, Kerem Zaman, Rakesh R Menon, and Shashank Srivastava
    Findings of Association for Computational Linguistics (ACL), 2025.
    Learning and Language
    [pdf], [code]

2024

  1. SOCIALGAZE: Improving the Integration of Human Social Norms in Large Language Models
    Anvesh Rao Vijjini, Rakesh R Menon, Jiayi Fu, Shashank Srivastava, and Snigdha Chaturvedi
    Findings of Empirical Methods in Natural Language Processing (EMNLP Findings), 2024.
    Fairness and Social Applications
    [pdf], [code]

  2. Fuse to Forget: Bias Reduction and Selective Memorization through Model Fusion
    Kerem Zaman, Leshem Chosen, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2024.
    Learning and Language Fairness and Social Applications
    [pdf], [code]

  3. DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
    Rakesh R Menon and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2024.
    Learning and Language Fairness and Social Applications Learning from Limited Labels
    [pdf], [code]

2023

  1. Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
    Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, and Dakuo Wang
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    Active Learning
    [pdf], [arxiv]

  2. Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers
    Kangda Wei, Sayan Ghosh, Rakesh R Menon, and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    Learning and Language
    [pdf], [arxiv], [code]

  3. Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
    Yiyuan Li, Rakesh R Menon, Sayan Ghosh, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    Learning and Language Datasets and Benchmarks
    [pdf], [arxiv], [code]

  4. Identifying and Manipulating the Perceived Personality Traits of Language Models
    Graham Caron and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    Fairness and Social Applications Datasets and Benchmarks
    [pdf], [arxiv]

  5. MaNtLE: Model-agnostic Natural Language Explainer
    Rakesh R Menon, Kerem Zaman, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    Learning and Language
    [pdf], [arxiv], [code]

  6. LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning
    Sayan Ghosh*, Rakesh R Menon*, and Shashank Srivastava
    Findings of Association for Computational Linguistics (ACL), 2023.
    Learning and Language
    [pdf], [arxiv], [code]

  7. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
    Big-Bench Collaboration
    Transactions of Machine Learning Research (TMLR), 2023.
    Learning and Language Datasets and Benchmarks
    [openreview], [pdf], [arxiv], [code], [dataset]

2022

  1. What do Large Language Models Learn beyond Language?
    Avinash Madasu and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022.
    Learning and Language
    [pdf], [arxiv], [code]

  2. Compositional Generalization for Kinship Prediction through Data Augmentation
    Kangda Wei, Sayan Ghosh, and Shashank Srivastava
    Proceedings of the 4th Workshop of Narrative Understanding (WNU), 2022.
    Learning from Limited Labels
    [pdf], [code]

  3. Predicting Difficulty and Discrimination of Natural Language Questions
    Matthew Byrd and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    Language Understanding, Reasoning, and Generation Datasets and Benchmarks
    [pdf], [code], [dataset]

  4. ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding
    Sayan Ghosh and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    Datasets and Benchmarks Language Understanding, Reasoning, and Generation Fairness and Social Applications
    [pdf], [arxiv], [code], [dataset]

  5. CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
    Rakesh R Menon*, Sayan Ghosh*, and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    Learning and Language Datasets and Benchmarks
    [pdf], [arxiv], [code], [dataset]

2021

  1. Does Social Pressure Drive Persuasion in Online Fora?
    Ayush Jain and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    Fairness and Social Applications
    [pdf]

  2. Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning
    Sayan Ghosh and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    Neuro-symbolic Learning
    [pdf], [arxiv], [code]

  3. Adversarial Scrubbing of Demographic Information for Text Classification
    Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier B Oliva, Shashank Srivastava, and Snigdha Chaturvedi
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    Fairness and Social Applications
    [pdf], [arxiv], [code]

  4. Improving and Simplifying Pattern Exploiting Training
    Derek Tam*, Rakesh R Menon*, Mohit Bansal, Shashank Srivastava, and Colin Raffel
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    Learning from Limited Labels
    [pdf], [arxiv], [code]

  5. How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?
    Sayan Ghosh*, Zheng Qi*, Snigdha Chaturvedi, and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2021.
    Language Understanding, Reasoning, and Generation
    [pdf], [code]

2020

  1. PRover: Proof Generation for Interpretable Reasoning over Rules
    Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2020.
    Language Understanding, Reasoning, and Generation
    [pdf], [arxiv], [code]

  2. Learning Web-based procedures by Reasoning over Explanations and Demonstrations in Context
    Shashank Srivastava, Oleksandr Polozov, Nebojsa Jojic, and Christopher Meek
    Proceedings of the Association of Computational Linguistics (ACL), 2020.
    Learning and Language Learning from Limited Labels Neuro-symbolic Learning Datasets and Benchmarks
    [pdf], [dataset]

  3. An Agent for Learning New Natural Language Commands
    Amos Azaria, Shashank Srivastava, Jayant Krishnamurthy, Igor Labutov, and Tom Mitchell
    Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2020.
    Learning and Language Learning from Limited Labels
    [link]

2019

  1. Learning to Ask for Conversational Machine Learning
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019.
    Learning and Language Learning from Limited Labels
    [pdf]

2018

  1. LIA: A Natural Language Programmable Personal Assistant
    Igor Labutov, Shashank Srivastava, and Tom Mitchell
    Systems Demo, Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2018.
    Learning and Language
    [pdf]

  2. A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text
    Shashank Srivastava and Nebojsa Jojic
    Proceedings of the Association of Computational Linguistics (ACL), 2018.
    Syntax and Semantics
    [pdf]

  3. Zero-shot Learning of Classifiers from Natural Language Quantification
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of the Association of Computational Linguistics (ACL), 2018.
    Learning and Language Learning from Limited Labels
    [pdf]

  4. Where have I heard this story before? : Identifying Narrative Similarity in Movie Remakes
    Snigdha Chaturvedi, Shashank Srivastava, and Dan Roth
    Proceedings of the North Americal Chapter of Association of Computational Linguistics (NAACL), 2018.
    Fairness and Social Applications Datasets and Benchmarks
    [pdf]

2017

  1. Learning Classifiers from Declarative Language
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    NeurIPS Workshop on Learning from Limited Data, 2017.
    Learning and Language Learning from Limited Labels
    [pdf]

  2. Joint Concept Learning and Semantic Parsing from Natural Language Explanations
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2017.
    Learning and Language Learning from Limited Labels Datasets and Benchmarks
    [pdf]

  3. Parsing Natural Language Conversations with Contextual Cues
    Shashank Srivastava, Amos Azaria, and Tom Mitchell
    Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017.
    Neuro-symbolic Learning Datasets and Benchmarks
    [pdf]

2016

  1. CMUML Micro-Reader System for KBP 2016 Cold Start Slot Filling, Event Nugget Detection, and Event Argument Linking
    Bishan Yang, Ndapandula Nakashole, Bryan Kisiel, Emmanouil A. Platanios, Abulhair Saparov, Shashank Srivastava, Derry Wijaya, and Tom Mitchell
    Proceedings of the Text Analysis Conference (TAC), 2016.
    Syntax and Semantics
    [pdf]

  2. Inferring Interpersonal Relations in Narrative Summaries
    Shashank Srivastava, Snigdha Chaturvedi, and Tom Mitchell
    Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), 2016.
    Fairness and Social Applications Datasets and Benchmarks
    [pdf]

  3. Modeling Evolving Relationships Between Characters in Literary Novel
    Snigdha Chaturvedi, Shashank Srivastava, Hal Daume III, and Chris Dyer
    Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), 2016.
    Fairness and Social Applications
    [pdf]

2015

  1. CMU-ML System for KBP Cold Start Slot Filling
    Bryan Kisiel, Bill McDowell, Matt Gardner, Ndapandula Nakashole, Emmanouil A. Platanios, Abulhair Saparov, Shashank Srivastava, Derry Wijaya, and Tom Mitchell
    Proceedings of the Text Analysis Conference (TAC), 2015.
    Syntax and Semantics
    [pdf]

2014

  1. Vector space semantics with frequency-driven motifs
    Shashank Srivastava and Eduard Hovy
    Proceedings of the Association of Computational Linguistics (ACL), 2014.
    Syntax and Semantics
    [pdf]

  2. Spatial Compactness meets Topical Consistency: Jointly modeling link and content for community detection
    Mrinmaya Sachan, Avinava Dubey, Shashank Srivastava, Eric P Xing, and Eduard Hovy
    Proceedings of Web Search and Data Mining (WSDM), 2014.
    Miscellaneous
    [pdf]

2013

  1. A Walk-based Semantically Enriched Tree Kernel Over Distributed Word Representations
    Shashank Srivastava, Dirk Hovy, and Eduard Hovy
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2013.
    Syntax and Semantics
    [pdf]

  2. A Structured Distributional Semantic Model for Event Co-reference
    Kartik Goyal*, Sujay Kumar Jauhar*, Huiying Li*, Mrinmaya Sachan*, Shashank Srivastava*, and Eduard Hovy
    Proceedings of the Association of Computational Linguistics (ACL), 2013.
    Syntax and Semantics
    [pdf]

  3. A Structured Distributional Semantic Model : Integrating Structure with Semantics
    Kartik Goyal*, Sujay Kumar Jauhar*, Huiying Li*, Mrinmaya Sachan*, Shashank Srivastava*, and Eduard Hovy
    Workshop on Continuous Vector Space Models and their Compositionality, ACL, 2013.
    Syntax and Semantics
    [pdf]

  4. Identifying Metaphorical Word Use with Tree Kernels
    Dirk Hovy, Shashank Srivastava, Sujay Kumar Jauhar, Mrinmaya Sachan, Kartik Goyal, Huiying Li, Whitney Sanders, and Eduard Hovy
    NAACL-HLT Meta4NLP Workshop, 2013.
    Syntax and Semantics
    [pdf]

2012

  1. A Topical graph-kernel for Link Prediction in Labeled Graphs
    Snigdha Chaturvedi, Hal Daume III, Taesun Moon, and Shashank Srivastava
    ICML Workshop on Mining and Learning with Graphs (MLG), 2012.
    Miscellaneous
    [pdf]