Papers by Mihir Parmar
In-BoXBART: Get Instructions into Biomedical Multi-Task Learning (2022.findings-naacl)
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| Challenge: | Experimental results show that the proposed model outperforms single-task baseline by 3% and multi-task (without instruction) baseline by 18% on an average. |
| Approach: | They propose a unified model that can learn all 32 instruction tasks of the BoX without any task-specific modules. |
| Outcome: | The proposed model outperforms single-task baseline by 3% and multi-task (without instruction) baseline by 18% on an average. |
Is a Question Decomposition Unit All We Need? (2022.emnlp-main)
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| Challenge: | Large Language Models (LMs) have achieved state-of-the-art performance on many NLP benchmarks. |
| Approach: | They propose to decompose a hard question into simpler questions that are easier for models to answer. |
| Outcome: | The proposed approach significantly improves model performance (24% for GPT3 and 29% for RoBERTa-SQuAD along with a symbolic calculator) by decomposing a hard question into simpler questions that are easier for models to answer. |
Less is More: Summary of Long Instructions is Better for Program Synthesis (2022.emnlp-main)
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| Challenge: | Despite the success of large pre-trained language models, they show below-par performance on the larger and more complicated programming related questions. |
| Approach: | They propose to use a human-generated summary of programming questions to improve LMs' performance. |
| Outcome: | The proposed approach outperforms baseline by 8.13% on the APPS dataset and 11.88% on the CodeContests dataset on an average in terms of strict accuracy. |
How Many Data Samples is an Additional Instruction Worth? (2023.findings-eacl)
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| Challenge: | Recent introduced instruction-paradigm empowers non-expert users to leverage NLP resources by defining a new task in natural language. |
| Approach: | They propose to define a task in natural language without creating task-specific datasets or building models. |
| Outcome: | The proposed model outperforms multitask learning models but is far from state-of-the-art task-specific models. |
Don’t Blame the Annotator: Bias Already Starts in the Annotation Instructions (2023.eacl-main)
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| Challenge: | Recent studies have shown that data collected through crowdsourcing often exhibit various biases that lead to overestimation of model performance. |
| Approach: | They propose to model instruction bias in 14 recent NLU benchmarks by analyzing crowdsourcing instructions and analyzing their results. |
| Outcome: | The proposed model can be over-represented in datasets with a large number of examples, and the results are consistent with previous studies. |
Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models (2024.emnlp-main)
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Nisarg Patel, Mohith Kulkarni, Mihir Parmar, Aashna Budhiraja, Mutsumi Nakamura, Neeraj Varshney, Chitta Baral
| Challenge: | Existing logical reasoning evaluation benchmarks focus on simplistic single-step or multi-step reasoning with limited set of inference rules. |
| Approach: | They propose to use a multi-step logical reasoning evaluation dataset to measure their ability for human-like multi- step logical thinking. |
| Outcome: | The proposed dataset covers three logic types including propositional, first-order, and non-monotonic logic with various inference rules and depths. |
PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving (2025.emnlp-main)
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Mihir Parmar, Xin Liu, Palash Goyal, Yanfei Chen, Long Le, Swaroop Mishra, Hossein Mobahi, Jindong Gu, Zifeng Wang, Hootan Nakhost, Chitta Baral, Chen-Yu Lee, Tomas Pfister, Hamid Palangi
| Challenge: | Existing methods for natural planning lack constraint-guided iterative verification and adaptive selection . a recent study found that LLMs are not good at such planning. |
| Approach: | They propose a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents. |
| Outcome: | The proposed framework improves inference-time algorithms on NATURAL PLAN and OlympiadBench benchmarks. |
Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs (2024.emnlp-main)
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| Challenge: | Existing methods for extractive summarization lack coherence, despite improvements . a human-annotated dataset is used to improve coherency of extractive summary . |
| Approach: | They propose to use human-annotated datasets to create coherent extractive summaries . they use supervised fine-tuning and natural language user feedback to enhance coherence . |
| Outcome: | The proposed dataset shows that LLMs can produce coherent summaries with human feedback. |
Investigating Acceleration of LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with ‘LITE’ (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have remarkable performance across a wide variety of tasks, however, their large size makes their inference slow and computationally expensive. |
| Approach: | They propose to perform 'dynamic confidence-based early exiting' at token level from the intermediate layers which improves the computational efficiency of text generation without sacrificing the quality of the generation. |
| Outcome: | The proposed model achieves significant cost and quality improvements while maintaining the quality of the generation. |
ThinkTuning: Instilling Cognitive Reflections without Distillation (2025.emnlp-main)
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| Challenge: | Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. |
| Approach: | They propose a GRPO-based interactive training approach that augments the rollouts of a student model with the guidance of . a teacher poses a problem, lets the student try an answer, then gives corrective feedback–enough to point the mind in the right direction and then show the correct solution. |
| Outcome: | The proposed method shows 3.69% improvement over zero-shot baselines and 2.08% and 3.99% improvement over the vanilla-GRPO baselines. |
EDM3: Event Detection as Multi-task Text Generation (2024.starsem-1)
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| Challenge: | Existing methods for Event Detection (ED) cannot easily leverage pre-trained semantic knowledge. |
| Approach: | They propose to decompose and reformulate ED and fine-tune over its atomic subtasks to enhance knowledge transfer while mitigating prediction error propagation inherent in pipelined approaches. |
| Outcome: | The proposed method achieves state-of-the-art performance on RAMS, MAVEN, and MLEE, while achieving 90% accuracy over rare event types. |
PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving (2025.emnlp-main)
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Mihir Parmar, Palash Goyal, Xin Liu, Yiwen Song, Mingyang Ling, Chitta Baral, Hamid Palangi, Tomas Pfister
| Challenge: | Recent studies have shown that decomposing complex problems into simple subtasks has significantly boosted the performance of large language models (LLMs). |
| Approach: | They propose a unified post-training framework that distills synthetic task decompositions and fine-tunes smaller LLMs via supervised and reinforcement-learning objectives to improve complex reasoning. |
| Outcome: | The proposed framework outperforms strong baselines on GSM8k and MATH benchmarks and shows that it can improve generalization capabilities on out-of-domain datasets. |
LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models (2024.acl-long)
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Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral
| Challenge: | Existing work investigating the logical reasoning ability of large language models has focused only on a couple of inference rules of propositional and first-order logics. |
| Approach: | They propose to use a natural language question-answering dataset to evaluate the logical reasoning ability of large language models. |
| Outcome: | The proposed model performs poorly on a range of natural language questions using chain-of-thought prompting. |
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks (2022.emnlp-main)
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Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Atharva Naik, Arjun Ashok, Arut Selvan Dhanasekaran, Anjana Arunkumar, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Kuntal Kumar Pal, Maitreya Patel, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Savan Doshi, Shailaja Keyur Sampat, Siddhartha Mishra, Sujan Reddy A, Sumanta Patro, Tanay Dixit, Xudong Shen
| Challenge: | a benchmark of 1,616 diverse NLP tasks and their expert-written instructions is used to test generalization of models to unseen tasks . a recent study shows that instruction-following models outperform instruction-based models by over 9% . |
| Approach: | They build a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. |
| Outcome: | The proposed model outperforms existing instruction-following models by over 9% on the benchmark despite being smaller. |
Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning (2025.findings-naacl)
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Venkatesh Mishra, Bimsara Pathiraja, Mihir Parmar, Sat Chidananda, Jayanth Srinivasa, Gaowen Liu, Ali Payani, Chitta Baral
| Challenge: | Reasoning abilities of LLMs have been a key focus in recent years. |
| Approach: | They propose to use a college-level Multiple Choice Question-Answering task to identify LLM errors and evaluate their performance. |
| Outcome: | The proposed framework can be used in detailed error analysis of reasoning chains for logic-intensive complex tasks. |
LogicAttack: Adversarial Attacks for Evaluating Logical Consistency of Natural Language Inference (2023.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated impressive performance on Natural Language Inference (NLI) tasks. |
| Approach: | They propose a method to attack NLI models using diverse logical forms of premise and hypothesis using propositional logic to generate effective adversarial attacks. |
| Outcome: | The proposed method achieves an average 53% Attack Success Rate (ASR) across multiple logic-based attacks. |
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026.acl-long)
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| Challenge: | Existing methods to learn from preference-based feedback are expensive and scarce. |
| Approach: | They propose a framework that synergistically combines self-rewarding and active learning through human-AI collaboration. |
| Outcome: | The proposed framework outperforms existing methods on three reasoning benchmarks and achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct. |
Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter? (2024.emnlp-main)
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Nemika Tyagi, Mihir Parmar, Mohith Kulkarni, Aswin Rrv, Nisarg Patel, Mutsumi Nakamura, Arindam Mitra, Chitta Baral
| Challenge: | Existing studies evaluate only the final predicted answer of a puzzle, without providing any finer metrics to evaluate them. |
| Approach: | They propose to use a grid-based evaluation dataset to evaluate LLMs' reasoning abilities and a new error taxonomy to evaluate their reasoning chains. |
| Outcome: | The proposed model outperforms existing prompting methods on a wide range of natural language understanding tasks previously thought to be exclusive to humans. |