Papers by Neeraj Varshney
Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA (2023.acl-long)
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| Challenge: | Despite remarkable progress made in natural language processing, even the state-of-the-art systems often make incorrect predictions. |
| Approach: | They propose to use selective prediction to enable models to abstain from answering when their predictions are likely to be incorrect. |
| Outcome: | The proposed method improves performance on 11 QA datasets and in- and out-of-domain settings. |
Chaos with Keywords: Exposing Large Language Models Sycophancy to Misleading Keywords and Evaluating Defense Strategies (2024.findings-acl)
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| Challenge: | sycophancy is a type of hallucination in Large Language Models, which can lead to false information being presented. |
| Approach: | They explore the sycophantic tendencies of Large Language Models where models provide accurate answers even if they are not entirely correct. |
| Outcome: | The proposed models generate factually correct statements even when they are not completely correct. |
ILDAE: Instance-Level Difficulty Analysis of Evaluation Data (2022.acl-long)
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| Challenge: | Instance-level difficulty analysis of evaluation data is a new field of research that focuses on leveraging instance difficulty in natural language processing. |
| Approach: | They conduct Instance-Level Difficulty Analysis of Evaluation data in a large-scale setup of 23 datasets and demonstrate its five novel applications. |
| Outcome: | The proposed model improves efficiency and accuracy, improves quality and improves Out-of-Domain performance. |
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. |
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. |
The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness (2024.findings-acl)
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| Challenge: | Recent work on Large Language Models (LLMs) has identified a number of approaches to protect against their vulnerabilities and safety. |
| Approach: | They propose to use a large collection of safe and unsafe prompts to evaluate various LLM defense strategies over both ‘safety’ and ‘over-defensiveness’. |
| Outcome: | The proposed defense strategies are compared on multiple state-of-the-art LLMs and show that they are effective against both ‘safety’ and ‘over-defensiveness’. |
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. |
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution (2023.findings-acl)
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| Challenge: | State-of-the-art natural language processing models have been shown to achieve remarkable performance in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. |
| Approach: | They propose a multi-stage task to evaluate a system's performance on pipelined novelty ‘detection’ and ‘accommodation’ tasks. |
| Outcome: | The proposed model performs poorly in ‘closed-world’ settings where all the labels in the evaluation set are known at training time. |
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. |
“John is 50 years old, can his son be 65?” Evaluating NLP Models’ Understanding of Feasibility (2023.eacl-main)
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Himanshu Gupta, Neeraj Varshney, Swaroop Mishra, Kuntal Kumar Pal, Saurabh Arjun Sawant, Kevin Scaria, Siddharth Goyal, Chitta Baral
| Challenge: | Recent work has found that large-scale language models lack commonsense reasoning ability . a dataset evaluating large-level language models is needed to evaluate their understanding of feasibility . |
| Approach: | They propose a question-answering dataset that tests understanding of feasibility . they propose to use commonsense reasoning to reason about when an action is feasible . |
| Outcome: | The proposed dataset shows that state-of-the-art models struggle to answer feasibility questions correctly. |
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. |
Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems (2022.emnlp-main)
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| Challenge: | Pre-trained language models such as RoBERTa, ELECTRA, and T5 have a large number of parameters which makes them slow and computationally expensive. |
| Approach: | They propose a technique that utilizes a collection of models of varying capacities to accurately yet efficiently output predictions. |
| Outcome: | The proposed technique saves up to 88.93% computation cost and consistently achieves superior prediction accuracy with an improvement of up to 2.18%. |
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks (2022.acl-long)
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Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, Ashwin Kalyan
| Challenge: | Existing AI systems fail to perform basic mathematical reasoning when presented in a slightly different manner. |
| Approach: | They propose a multi-task benchmark that evaluates the performance of AI systems on eight different tasks that at their core require simple arithmetic understanding. |
| Outcome: | The proposed benchmark compares the performance of AI systems on eight different tasks. |
Unsupervised Natural Language Inference Using PHL Triplet Generation (2022.findings-acl)
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| Challenge: | In some cases, training samples may not be available or collecting them could be time-consuming and resource-intensive. |
| Approach: | They propose a procedural approach that leverages sentence transformations to collect PHL triplets for training NLI models. |
| Outcome: | The proposed model outperforms existing models on several NLI benchmarks with a set of sentence transformations. |
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings (2022.findings-acl)
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| Challenge: | Existing approaches to selective prediction are not consistently outperform the simplest baseline MaxProb in all three settings. |
| Approach: | They propose to use a large-scale setup of 17 datasets to study selective prediction in NLP tasks using in-domain, out-of-domain and adversarial settings. |
| Outcome: | The proposed approaches outperform the simplest baseline MaxProb in in-domain, out-of-domain and adversarial settings, but none consistently outperformed in all three settings. |