Papers by Bhavana Dalvi Mishra
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic (2024.emnlp-main)
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Nathaniel Weir, Kate Sanders, Orion Weller, Shreya Sharma, Dongwei Jiang, Zhengping Jiang, Bhavana Dalvi Mishra, Oyvind Tafjord, Peter Jansen, Peter Clark, Benjamin Van Durme
| Challenge: | Recent language models allow structured reasoning with text, but lack of a clear protocol for discerning entailment causes noisy datasets and limited performance gains. |
| Approach: | They propose a consistent approach to annotating decompositional entailment and evaluate its impact on LLM-based textual inference. |
| Outcome: | The proposed approach has higher internal consistency than prior decompositional entailment datasets and significantly improves proof quality and accuracy. |
Do language models have coherent mental models of everyday things? (2023.acl-long)
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| Challenge: | Psychologists and cognitive scientists hypothesize that humans develop mental models of the world, namely internal, conceptual representations of the environment which we base our decisions and actions on. |
| Approach: | They propose to add a constraint satisfaction layer to the LM's raw predictions to apply commonsense constraints to reduce incoherence. |
| Outcome: | The proposed extension removes inconsistencies and improves accuracy by 16-20%. |
Towards Teachable Reasoning Systems: Using a Dynamic Memory of User Feedback for Continual System Improvement (2022.emnlp-main)
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| Challenge: | Using simulated feedback, our system (called TeachMe) continually improves with time, and without model retraining. |
| Approach: | They propose to augment a QA model with a dynamic memory of user feedback, containing user-supplied corrections toerroneous model beliefs that users identify during interaction. |
| Outcome: | The proposed system improves with time and without model retraining, and with real users, by 15% on a hidden test set after teaching. |
HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance (2025.emnlp-main)
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| Challenge: | Existing approaches focus on retrieval augmentation and focus on the quality of the output . Existing methods focus on generating a highly specific declarative statement ignoring the underlying reasoning process behind ideation. |
| Approach: | They propose a large language model that generates evidence-based hypotheses using literature-guided reasoning and a multi-task setting. |
| Outcome: | The proposed model outperforms the base model and generates evidence-grounded hypotheses with high feasibility and impact as judged by human experts. |
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. |
Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning (2022.emnlp-main)
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| Challenge: | a system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning would allow better understanding of why a model produced the answer it did. |
| Approach: | They propose to combine a backward-chaining model with a verifier that checks that the model itself believes those premises through self-querying to generate multistep chains that are both faithful (the answer follows from the reasoning) |
| Outcome: | The proposed model generates chains that are faithful and truthful while maintaining answer accuracy. |