Papers by Kumar Shridhar
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems (2022.emnlp-main)
Copied to clipboard
| Challenge: | We hypothesize that questioning can enhance human performance and assist solvers . |
| Approach: | They propose to use large language models to generate sequential questions for math word problem-solving . they propose to apply these models to a variety of math word problems . |
| Outcome: | The proposed model improves the performance of a math word problem solver by generating more questions than other models. |
First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning (2025.findings-acl)
Copied to clipboard
| 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. |
The ART of LLM Refinement: Ask, Refine, and Trust (2024.naacl-long)
Copied to clipboard
Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ramakanth Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations and self-improve? |
| Approach: | They propose a reasoning with a refinement strategy called *ART: Ask, Refine, and Trust* that asks necessary questions to decide when an LLM should refine its output and uses it to affirm or deny trust. |
| Outcome: | The proposed reasoning with a refinement strategy achieves a performance gain of +5 points over baselines on two multistep reasoning tasks. |
Scaling Within Document Coreference to Long Texts (2021.findings-acl)
Copied to clipboard
Raghuveer Thirukovalluru, Nicholas Monath, Kumar Shridhar, Manzil Zaheer, Mrinmaya Sachan, Andrew McCallum
| Challenge: | Existing end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. |
| Approach: | They propose an approximation to end-to-end coreference resolution models which scales gracefully to documents of any length. |
| Outcome: | The proposed model reduces training and inference time and memory costs compared to current models with minimal loss in accuracy. |
Select-then-Route : Taxonomy guided Routing for LLMs (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Large language models have boosted performance across a broad spectrum of tasks . sending each query to the most suitable model is prohibitively expensive . |
| Approach: | They propose a framework that selects a small pool of LLMs and routes queries through an adaptive cascade. |
| Outcome: | The proposed framework improves accuracy and latency by 4X while reducing inference cost. |
Longtonotes: OntoNotes with Longer Coreference Chains (2023.findings-eacl)
Copied to clipboard
Kumar Shridhar, Nicholas Monath, Raghuveer Thirukovalluru, Alessandro Stolfo, Manzil Zaheer, Andrew McCallum, Mrinmaya Sachan
| Challenge: | Using Ontonotes, documents in certain genres were split into smaller parts for ease of annotation. |
| Approach: | They propose to merge annotations from documents split into smaller parts in Ontonotes for ease of annotation. |
| Outcome: | The proposed corpus restores documents to their original form, revealing dramatic increases in length in certain genres. |
Distilling Reasoning Capabilities into Smaller Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | a step-by-step reasoning approach like chain of thought has proved to be effective in eliciting reasoning abilities in large language models. |
| Approach: | They propose a knowledge distillation approach that leverages CoT reasoning capabilities of larger models and distills them into smaller models. |
| Outcome: | The proposed scheme boosts the performance of smaller models over 70% on multiple reasoning datasets. |
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models (2023.acl-long)
Copied to clipboard
| Challenge: | Recent work shows that language models can rely on shallow patterns in problem description when generating a solution. |
| Approach: | They propose a framework which pins down the causal effect of various factors on the output solution. |
| Outcome: | The proposed framework improves robustness and sensitivity to direct interventions on a test bed of math word problems. |
SIKeD: Self-guided Iterative Knowledge Distillation for Mathematical Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) can generate intermediate reasoning process for multistep reasoning tasks. |
| Approach: | They propose a distillation method that teaches the model to approach a task using different strategies and the model uses its self-generated on-policy outputs to choose the most suitable strategy. |
| Outcome: | The proposed method significantly outperforms distillation techniques on large models of different sizes. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
Copied to clipboard
Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |