Papers by Niket Tandon
Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension (N18-1)
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| Challenge: | Using synthetic data, existing models struggle with questions that require inference. |
| Approach: | They propose a dataset and two new neural models that exploit alternative mechanisms for state prediction. |
| Outcome: | The proposed dataset improves accuracy by 19% over previous models. |
Using Commonsense Knowledge to Answer Why-Questions (2022.emnlp-main)
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Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
| Challenge: | Existing approaches to integrating commonsense knowledge into large language models are implicit and explicit. |
| Approach: | They analyze the effects of model size and methods of injecting knowledge into TellMeWhy datasets to determine what aspects of commonsense knowledge are available in large language models. |
| Outcome: | The largest models yield substantial improvements over base models, but the amount of improvement decreases with larger model size. |
proScript: Partially Ordered Scripts Generation (2021.findings-emnlp)
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| Challenge: | Scripts represent structured commonsense knowledge about prototypical events in everyday situations/scenarios such as bake a cake. |
| Approach: | They collect 6.4k crowdsourced partially ordered scripts and develop models that combine language generation and graph structure prediction to generate scripts. |
| Outcome: | The proposed models perform well on two tasks: edge prediction and script generation. |
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
A Dataset for Tracking Entities in Open Domain Procedural Text (2020.emnlp-main)
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Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy
| Challenge: | Existing tasks require only a small set of attributes to track state changes in procedural text. |
| Approach: | They propose a task where given a procedural text as input, the task is to generate a set of state change tuples for each step. |
| Outcome: | The proposed task generates state change tuples from a set of pre-defined attributes for each step and predicts them from an open vocabulary. |
Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback (2022.findings-naacl)
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| Challenge: | Our approach pairs an LM with a growing memory of cases where the user identified an output error and provided general feedback on how to correct it. |
| Approach: | They propose to use an existing script generator to train a model to repair output errors without retraining. |
| Outcome: | The proposed model learns to apply user feedback to repair output errors while avoiding similar past mistakes on new, unseen examples. |
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations (2023.findings-emnlp)
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Kavel Rao, Liwei Jiang, Valentina Pyatkin, Yuling Gu, Niket Tandon, Nouha Dziri, Faeze Brahman, Yejin Choi
| Challenge: | Moral or ethical judgments rely heavily on the contexts in which they occur . a student model that produces defeasible contexts with improved validity, diversity, and defasibility is superior to intermediate student models . |
| Approach: | a new study uses a student model to provide contextualizations that make an action morally acceptable . the model is based on a dataset of 115K defeasible moral actions rated highly by human annotators . |
| Outcome: | The proposed model outperforms all intermediate models in a high-quality dataset . the model is based on 1.2M entries of contextualizations and rationales for 115K moral actions . |
First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning (2025.findings-acl)
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| Challenge: | Language models can solve complex reasoning tasks better by learning to generate rationales for their predictions. |
| Approach: | They propose to use a larger model to guide smaller models to start . this allows them to generate rationales for their predictions when correct . |
| Outcome: | The proposed method improves performance on multistep reasoning datasets over multiple smaller models. |
WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models (2024.lrec-main)
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| Challenge: | a global dataset for multi-cultural value prediction task is lacking in the computer science community . a multi-culture awareness of LMs is critical to generating safe and personalized responses . |
| Approach: | They present a global multi-cultural value prediction task using a world value survey dataset . they construct more than 20 million examples of the type "(demographic attributes, value question) answer" they show that the task is challenging for strong open and closed-source models . |
| Outcome: | The proposed model can generate a rating response to a value question based on demographic contexts on 11.1%, 25.0%, 72.2%, and 75.0% of the questions. |
OpenPI2.0: An Improved Dataset for Entity Tracking in Texts (2024.eacl-long)
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| Challenge: | Recent work shows that explicit modeling entity states benefits LMs in procedural tasks. |
| Approach: | They propose a dataset where entities and attributes are fully canonicalized and additional entity salience annotations are added. |
| Outcome: | The proposed dataset outperforms existing models on question answering and classical planning tasks. |
On the Reliability of Large Language Models for Causal Discovery (2025.acl-long)
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| Challenge: | Existing statistical methods to identify causal relationships from observational data remain elusive. |
| Approach: | They examine the impact of memorization for accurate causal relation prediction, the influence of incorrect causal relations in pre-training data and the contextual nuances that influence LLMs’ understanding of causal relations. |
| Outcome: | The proposed models are effective in recognizing causal relations that occur frequently in pre-training data, but their ability to generalize to new or rare causal relations is limited. |
What-if I ask you to explain: Explaining the effects of perturbations in procedural text (2020.findings-emnlp)
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| Challenge: | QUARTET constructs explanations from paragraphs using procedural text . qartet achieves 18 points better on explanation accuracy compared to strong baselines on a recent process comprehension benchmark. |
| Approach: | They propose a system that constructs explanations from paragraphs by modeling the explanation task as a multitask learning problem. |
| Outcome: | The proposed system achieves 18 points better on explanation accuracy compared to strong baselines on a process comprehension benchmark. |
Cache Saver: A Modular Framework for Efficient, Affordable, and Reproducible LLM Inference (2025.findings-emnlp)
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Nearchos Potamitis, Lars Henning Klein, Bardia Mohammadi, Chongyang Xu, Attreyee Mukherjee, Niket Tandon, Laurent Bindschaedler, Akhil Arora
| Challenge: | Inference is the major cost throughout the lifecycle of a large language model (LLM). |
| Approach: | They propose a plug-and-play, asynchronous framework that facilitates high-level inference optimizations. |
| Outcome: | The proposed framework reduces cost and CO2 by 35% across methods, tasks, and LLMs. |
Tailoring with Targeted Precision: Edit-Based Agents for Open-Domain Procedure Customization (2024.findings-acl)
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| Challenge: | Using a set of over 200 WikiHow procedures, we test several simple multi-LLM-agent architectures for customization. |
| Approach: | They propose to use a set of WikiHow procedures to test how-to procedures can be customized by multiple LLMs. |
| Outcome: | The proposed architecture outperforms an end-to-end LLM in the evaluation set of over 200 WikiHow procedures. |
Could you give me a hint ? Generating inference graphs for defeasible reasoning (2021.findings-acl)
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| Challenge: | Defeasible reasoning is a mode of reasoning where conclusions can be overturned by taking into account new evidence. |
| Approach: | They propose to automatically generate inference graphs for a defeasible inference task by transfer learning from a related NLP task. |
| Outcome: | The proposed method generates meaningful graphs for a defeasible inference task and human accuracy improves by 20%. |
Conditional set generation using Seq2seq models (2022.emnlp-main)
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| Challenge: | Several NLP tasks are instances of set generation. |
| Approach: | They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality. |
| Outcome: | The proposed method improves performance on four benchmark datasets with no additional annotations. |
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data (2025.acl-long)
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| Challenge: | Existing statistical methods for causal discovery are expensive, require high-quality structured tabular data, and are often not available for a wide range of NLP applications. |
| Approach: | They propose a framework that combines statistical and large language model methods to discover causal relations from a set of initial variables. |
| Outcome: | The proposed method combines statistical and LLM-based methods to discover known and novel causal relations. |
Be Consistent! Improving Procedural Text Comprehension using Label Consistency (N19-1)
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| Challenge: | Existing systems for procedural text comprehension still struggle with this task . evaluative work shows that consistent predictions from multiple entities can improve performance . |
| Approach: | They propose a framework that leverages label consistency during training to improve prediction performance. |
| Outcome: | The proposed framework significantly improves prediction performance over previous state-of-the-art systems on a standard benchmark dataset for procedural text, ProPara. |
Let Me Teach You: Pedagogical Foundations of Feedback for Language Models (2024.emnlp-main)
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| Challenge: | Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models to human preferences. |
| Approach: | They propose a feedback framework for Large Language Models that outlines various characteristics of the feedback space and a taxonomy based on these variables. |
| Outcome: | The proposed framework provides a general mapping of the feedback space and provides examples for mapping to future research. |
PDDLEGO: Iterative Planning in Textual Environments (2024.starsem-1)
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| Challenge: | Existing methods to plan in textual environments rely on a fully-observed environment where all entity states are known, but are not interpretable. |
| Approach: | They propose to use LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. |
| Outcome: | The proposed model outperforms existing methods in the Coin Collector simulation and Cooking World simulations. |
Memory-assisted prompt editing to improve GPT-3 after deployment (2022.emnlp-main)
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| Challenge: | Large LMs such as GPT-3 can commit mistakes that are obvious to humans, such as interpreting “What word is similar to good?” to mean a homophone, while the user intended a synonym. |
| Approach: | They pair GPT-3 with a growing memory of cases where the model misunderstood the user’s intents, along with user feedback for clarification. |
| Outcome: | The proposed model can correct misunderstandings on four lexical tasks and two advanced ethical reasoning tasks without retraining. |
Aligning Language Models to User Opinions (2023.findings-emnlp)
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| Challenge: | Personality is a defining feature of human beings, shaped by a complex interplay of demographic characteristics, moral principles, and social experiences. |
| Approach: | They use public opinion surveys to model past user opinions in addition to user demographics and ideology to achieve up to 7 points accuracy gains in predicting public opinions from survey questions. |
| Outcome: | The proposed model achieves 7 points accuracy gains in predicting public opinions from public opinion surveys across a broad set of topics. |
Think about it! Improving defeasible reasoning by first modeling the question scenario. (2021.emnlp-main)
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| Challenge: | Existing literature suggests that a person forms a mental model of the problem scenario before answering questions. |
| Approach: | They propose to have a model first create a graph of relevant influences and leverage that graph as an additional input when answering a defeasible query. |
| Outcome: | The proposed model achieves state-of-the-art on three different defeasible reasoning datasets. |
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge (D18-1)
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| Challenge: | Recent work has shown impressive progress in comprehending procedural text, but their predictions can be inconsistent or highly improbable. |
| Approach: | They propose to incorporate global constraints and bias reading with corpora-based preferences to improve the predicted effects of actions in a paragraph. |
| Outcome: | The proposed model significantly outperforms earlier models on a benchmark dataset for procedural text comprehension (+8% relative gain) it avoids nonsensical predictions that earlier models make, and it is more robust than previous models. |
WIQA: A dataset for “What if...” reasoning over procedural text (D19-1)
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| Challenge: | a dataset of “What if...” questions is available for procedural text comprehension . we present the dataset as an open challenge to the community . |
| Approach: | They propose a dataset of “What if...” questions over procedural text . they use paragraphs annotated with multiple influence graphs to create the questions . |
| Outcome: | The proposed dataset achieves 73.8% accuracy, well below the human performance of 96.3%. |
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text (D19-1)
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| Challenge: | XPAD is a new model that predicts actions' effects and their dependencies based on background knowledge . previous work on extracting sequences of actions from text has focused on identifying why they are the way they are . |
| Approach: | They propose a new model that biases effect predictions towards those that explain more of the actions in the paragraph and are more plausible with respect to background knowledge. |
| Outcome: | The proposed model outperforms existing systems on explaining actions by predicting dependencies while maintaining the performance on the original task in ProPara. |
Editing Common Sense in Transformers (2023.emnlp-main)
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Anshita Gupta, Debanjan Mondal, Akshay Sheshadri, Wenlong Zhao, Xiang Li, Sarah Wiegreffe, Niket Tandon
| Challenge: | Currently, the performance of transformer-based model editing methods is limited to statements about encyclopedic knowledge with a single correct answer. |
| Approach: | They propose to improve MEMIT's model editing algorithm by varying edit tokens and improving the layer selection strategy to improve commonsense knowledge. |
| Outcome: | The MEMIT editing algorithm outperforms baseline models on PEP3k and 20Q datasets while fine-tuning baselines shows significant trade-offs. |
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs (2023.acl-long)
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| Challenge: | Despite their success, even the largest language models make mistakes. |
| Approach: | They propose a framework where one language model can generate critiques to improve its peer's performance. |
| Outcome: | The proposed framework improves the performance of a fixed model 200 times its size by 10% over other models. |