Papers by Subhajit Chaudhury
X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization (2022.emnlp-main)
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Subhajit Chaudhury, Sarathkrishna Swaminathan, Chulaka Gunasekara, Maxwell Crouse, Srinivas Ravishankar, Daiki Kimura, Keerthiram Murugesan, Ramón Fernandez Astudillo, Tahira Naseem, Pavan Kapanipathi, Alexander Gray
| Challenge: | Abstractive summarization models produce factually inconsistent summaries that are not supported by the original article. |
| Approach: | They propose a fact-aware filtering mechanism that improves the factuality of abstractive summarization models. |
| Outcome: | The proposed method improves the quality of training data and the factuality of generated summaries. |
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games (2020.emnlp-main)
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Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula, Michiaki Tatsubori, Asim Munawar, Ryuki Tachibana
| Challenge: | Reinforcement Learning methods for text-based games fail to generalize on unseen games, especially in small data regimes. |
| Approach: | They propose a Context Relevant Episodic State Truncation method for irrelevant token removal in observation text for improved generalization. |
| Outcome: | The proposed method shows that it can generalize on unseen games using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring fewer number of training episodes. |
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning (2024.eacl-long)
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Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
| Challenge: | Text-based games (TBGs) combine natural language understanding with reasoning. |
| Approach: | They propose an exploration-guided reasoning agent for textual reinforcement learning that integrates natural language with reasoning. |
| Outcome: | The proposed agent outperforms baseline agents on TWG and TWC games. |
ImReasoner: Improving Memory-based Language Models for Reasoning-in-a-Haystack Tasks (2026.acl-long)
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Ching-Yun Ko, Payel Das, Sihui Dai, Georgios Kollias, Subhajit Chaudhury, Aurelie C. Lozano, Pin-Yu Chen
| Challenge: | despite advances, large language models exhibit brittleness on tasks that require multi-step reasoning over long contexts. |
| Approach: | They propose to explicitly encode contexts as ordered memory and perform iterative retrieval to construct reasoning chains. |
| Outcome: | The proposed frameworks fail to show emergent reasoning generalization in a weakly supervised scenario . the proposed framework is based on a synthetic benchmark to stress-test the models . |
ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval (2026.acl-long)
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David H. Yang, Yuxuan Zhu, Mohammad Mohammadi Amiri, Keerthiram Murugesan, Tejaswini Pedapati, Subhajit Chaudhury, Pin-Yu Chen
| Challenge: | Large language models generate long chain of thoughts but memory footprint grows with output length . prior work on KV cache optimization focused on compressing long input context . |
| Approach: | They propose a new approach that compresses verbose reasoning thoughts into summaries . they use a dynamic KV cache selection policy that leverages these summary keys . |
| Outcome: | The proposed approach reduces memory usage while avoiding full-cache attention at each step. |
Answering the Wrong Question: Reasoning Trace Inversion for Abstention in LLMs (2026.acl-long)
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| Challenge: | Large Language Models have been shown to have worse abstention abilities than reasoning models . a new class of abstraction methods is developed to improve absttention performance . |
| Approach: | They propose a class of abstention methods that generate reasoning trace and reconstruct most likely query from it. |
| Outcome: | The proposed method beats baselines in 33 out of 36 settings. |
LOA: Logical Optimal Actions for Text-based Interaction Games (2021.acl-demo)
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Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray
| Challenge: | et al., 2019) have proposed a neuro-symbolic approach for reinforcement learning in non-simultaneous environments. |
| Approach: | They propose an action decision architecture with a neuro-symbolic framework for natural language interaction games. |
| Outcome: | The proposed framework provides an open-source implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. |
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types (2023.findings-acl)
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Keerthiram Murugesan, Sarathkrishna Swaminathan, Soham Dan, Subhajit Chaudhury, Chulaka Gunasekara, Maxwell Crouse, Diwakar Mahajan, Ibrahim Abdelaziz, Achille Fokoue, Pavan Kapanipathi, Salim Roukos, Alexander Gray
| Challenge: | Existing evaluation metrics for machine text are inadequate to capture quality of text . a recent study has focused on task-specific evaluation metrics or on properties of machine-generated text based on mismatch errors . |
| Approach: | They propose a new evaluation scheme based on fine-grained mismatch errors . they propose 13 mismatch error types to guide the model for better prediction of human judgments . |
| Outcome: | The proposed evaluation scheme is based on mismatch errors in 7 NLP tasks . the mismatch error types guide the model for better prediction of human judgments . |
Language-based General Action Template for Reinforcement Learning Agents (2021.findings-acl)
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| Challenge: | Prior knowledge is important in decision-making, and humans preserve it in the form of natural language (NL). |
| Approach: | They propose an environmentagnostic action framework that incorporates prior knowledge into decision-making . they propose to use general semantic schemes to facilitate agent in finding plausible actions . |
| Outcome: | The proposed agent performs better than agents that rely on gamespecific actions. |
On the Effects of Fine-tuning Language Models for Text-Based Reinforcement Learning (2025.coling-main)
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| Challenge: | Text-based reinforcement learning is a form of interactive fiction where players manipulate the environment using text and admissible actions in natural language. |
| Approach: | They show that rich semantic understanding leads to efficient training of text-based RL agents . they also show that semantic degeneration occurs when LMs are inappropriately fine-tuned . |
| Outcome: | The results suggest that semantic understanding is not important for the task . they also show that fine-tuning language models can degenerate the agent's performance . |
Neuro-Symbolic Reinforcement Learning with First-Order Logic (2021.emnlp-main)
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Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray
| Challenge: | Existing deep reinforcement learning methods require many trials before convergence and no direct interpretability of trained policies is provided. |
| Approach: | They propose a novel RL method which can learn symbolic and interpretable rules in their differentiable network. |
| Outcome: | The proposed method can learn symbolic and interpretable rules in their differentiable network. |
Self-Supervised Rule Learning to Link Text Segments to Relational Elements of Structured Knowledge (2023.findings-emnlp)
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Shajith Ikbal, Udit Sharma, Hima Karanam, Sumit Neelam, Ronny Luss, Dheeraj Sreedhar, Pavan Kapanipathi, Naweed Khan, Kyle Erwin, Ndivhuwo Makondo, Ibrahim Abdelaziz, Achille Fokoue, Alexander Gray, Maxwell Crouse, Subhajit Chaudhury, Chitra Subramanian
| Challenge: | Various approaches have been tried to map predicate components of a natural language (NL) text segment onto their corresponding predicates within a knowledge base (KB). |
| Approach: | They propose a neuro-symbolic approach to self-learn rules that serve as interpretable knowledge to perform relation linking in knowledge base question answering systems. |
| Outcome: | The proposed approach achieves an average performance gain of 17% on CLUTRR and relation linking in a KBQA system. |
Granite Guardian: Comprehensive LLM Safeguarding (2025.naacl-industry)
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Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin, Keerthiram Murugesan, Erik Miehling, Martín Santillán Cooper, Kieran Fraser, Giulio Zizzo, Muhammad Zaid Hameed, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
| Challenge: | a suite of advanced models is designed to detect and mitigate risks associated with prompts and responses. |
| Approach: | a team of researchers develop a model family to detect and mitigate risks associated with prompts and responses. the model family is based on the Granite 3.0 language models. |
| Outcome: | a new model family is designed to detect and mitigate risks associated with prompts and responses. |
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning (2023.acl-long)
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Subhajit Chaudhury, Sarathkrishna Swaminathan, Daiki Kimura, Prithviraj Sen, Keerthiram Murugesan, Rosario Uceda-Sosa, Michiaki Tatsubori, Achille Fokoue, Pavan Kapanipathi, Asim Munawar, Alexander Gray
| Challenge: | Existing text-based reinforcement learning agents use embeddings as representations for observation and are fed to an action scorer for predicting the next action. |
| Approach: | They propose a novel neurosymbolic agent that combines a semantic parser and a rule induction system to learn interpretable rules as policies. |
| Outcome: | The proposed method outperforms deep learning-based methods on established text-based game benchmarks on unobserved games and on unseen games. |
EpMAN: Episodic Memory AttentioN for Generalizing to Longer Contexts (2025.acl-long)
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Subhajit Chaudhury, Payel Das, Sarathkrishna Swaminathan, Georgios Kollias, Elliot Nelson, Khushbu Pahwa, Tejaswini Pedapati, Igor Melnyk, Matthew Riemer
| Challenge: | Recent advances in Large Language Models (LLMs) have yielded impressive successes on many language tasks, but efficient processing of long contexts remains a significant challenge. |
| Approach: | They propose a method for processing long contexts in an episodic memory module while holistically attending to semantically-relevant context chunks. |
| Outcome: | The proposed method outperforms baseline decoders on multiple long-context recall and question-answering benchmarks on 16k to 256k tokens. |
Neuro-Symbolic Approaches for Text-Based Policy Learning (2021.emnlp-main)
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| Challenge: | Text-based games are important testbeds for reinforcement learning in the natural language domain. |
| Approach: | They propose a method that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. |
| Outcome: | The proposed method outperforms existing methods in RL using 5-10x fewer training games. |
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs (2024.acl-long)
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Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Vernon Austel, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis Lastras
| Challenge: | Existing methods to train and test large language models that involve calls to tools and APIs are lacking. |
| Approach: | They propose a large corpora for training and systematic testing of tool-augmented LLMs. |
| Outcome: | The proposed datasets mimic real-world scenarios involving API-tasks and slot filling. |
A Grounded Preference Model for LLM Alignment (2024.findings-acl)
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Tahira Naseem, Guangxuan Xu, Sarathkrishna Swaminathan, Asaf Yehudai, Subhajit Chaudhury, Radu Florian, Ramón Astudillo, Asim Munawar
| Challenge: | Large Language Models (LLMs) suffer from factual inconsistency and hallucination despite recent advances . training a preference model requires substantial human annotation, which is expensive and labor-intensive. |
| Approach: | They propose to generate synthetic grounded preference data and train a Grounded Preference Model to assess the overall quality of grounded responses. |
| Outcome: | The proposed model can generate much better grounded responses as judged by GPT4 and achieves the TRUE faithfulness Benchmark. |
Laziness Is a Virtue When It Comes to Compositionality in Neural Semantic Parsing (2023.acl-long)
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Maxwell Crouse, Pavan Kapanipathi, Subhajit Chaudhury, Tahira Naseem, Ramon Fernandez Astudillo, Achille Fokoue, Tim Klinger
| Challenge: | Compositional generalization is a key feature of human intelligence and has been identified as a major point of weakness in neural methods for semantic parsing. |
| Approach: | They propose a neural parsing generation method that constructs logical forms from the bottom up, beginning from the logical form’s leaves. |
| Outcome: | The proposed method outperforms general-purpose parsers on a CFQ dataset and two other Text-to-SQL datasets while also being competitive with parser that have been tailored to each task. |