Papers by Aman Madaan
Neural Language Modeling for Contextualized Temporal Graph Generation (2021.naacl-main)
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| Challenge: | Existing methods for temporal reasoning have been used for a number of applications, but their potential for tempor reasoning over event graphs has not been explored. |
| Approach: | They propose to use large-scale pre-trained language models to generate an event-level temporal graph from a document using existing IE/NLP tools. |
| Outcome: | The proposed method outperforms the closest existing method on several metrics on a hand-labeled, out-of-domain corpus. |
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. |
Let’s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs (2023.emnlp-main)
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| Challenge: | Existing methods for improving the correctness of output from large language models generate a constant number of samples per question, but Adaptive-Consistency reduces sample budget by up to 7.9 times with an average accuracy drop of less than 0.1%. |
| Approach: | They propose a model-agnostic technique that dynamically adjusts the number of samples per question using a lightweight stopping criterion. |
| Outcome: | The proposed technique reduces sample budget by 7.9 times with an average accuracy drop of less than 0.1%. |
What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study (2023.findings-emnlp)
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| Challenge: | Using a few-shot prompt, we examine the effects of symbols and patterns on in-context learning in large language models. |
| Approach: | They employ a counterfactual prompting approach by manipulating examples and testing the consequences on model behavior. |
| Outcome: | The proposed approach allows us to understand the relative contributions of symbols and patterns on in-context learning. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |
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. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
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. |
Program-Aided Reasoners (Better) Know What They Know (2024.naacl-long)
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| Challenge: | Prior work shows that program-aided reasoning improves accuracy but also requires reasoners to "know what they know". |
| Approach: | They compare the calibration of program-aided language models (PAL) and text-based Chain-of-thought (COT) prompting techniques over 5 datasets and 2 model types . |
| Outcome: | The proposed methods improve accuracy and calibrate the models over 5 datasets and 2 model types. |
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. |
Politeness Transfer: A Tag and Generate Approach (2020.acl-main)
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Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
Language Models of Code are Few-Shot Commonsense Learners (2022.emnlp-main)
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| Challenge: | Existing approaches to generate graphs using pre-trained language models hinder their ability to generate them correctly. |
| Approach: | They propose to frame structured commonsense reasoning tasks as code generation tasks instead of serializing the output graph as a flat list of nodes and edges. |
| Outcome: | The proposed approach outperforms natural-language LMs in three natural language tasks even when the downstream task does not involve source code at all. |