Papers by Asim Munawar
Learning Neuro-Symbolic World Models with Conversational Proprioception (2023.acl-short)
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| Challenge: | Existing neuro-symbolic approaches to natural language-based interactions are model-free, but there is a need for model-based approaches. |
| Approach: | They propose a model-free approach to learning a logical policy in a text-based game . they use a neural network to enhance the internal logic state with a memory of previous actions . |
| Outcome: | The proposed method can learn neuro-symbolic world models on the TextWorld-Commonsense set of games. |
NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls (2025.emnlp-main)
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Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, Mayank Agarwal, Maxwell Crouse, Yara Rizk, Kelsey Bradford, Asim Munawar, Sadhana Kumaravel, Saurabh Goyal, Xin Wang, Luis A. Lastras, Pavan Kapanipathi
| Challenge: | Existing benchmarks and datasets for tool calling have lagged behind . nested sequencing is a common problem in LLMs, but it is not enough to evaluate them. |
| Approach: | They propose a benchmark to evaluate LLMs on nested sequences of API calls, i.e. sequences where the output of one API call is passed as input to a subsequent call. |
| Outcome: | The proposed model achieves a full sequence match accuracy of 28% and a win-rate of 60% on nested sequences of API calls. |
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. |
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks (2024.emnlp-industry)
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Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, G P Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachindra Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Soria, Dheeraj Sreedhar, Praveen Venkateswaran, Merve Unuvar, David Cox, Salim Roukos, Luis Lastras, Pavan Kapanipathi
| Challenge: | Existing research explores the use of Large Language Models (LLMs) as the backbone of agentic systems. |
| Approach: | They propose a model trained using a multi-task training approach on seven fundamental tasks encompassed in function calling that has better generalizability on multiple tasks across seven evaluation benchmarks. |
| Outcome: | The proposed model outperforms more than 15 other models on out-of-domain datasets and ranks among the top on the Berkeley Function Calling Leaderboard (BFCL). |
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. |
Ensemble-Instruct: Instruction Tuning Data Generation with a Heterogeneous Mixture of LMs (2023.findings-emnlp)
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Young-Suk Lee, Md Sultan, Yousef El-Kurdi, Tahira Naseem, Asim Munawar, Radu Florian, Salim Roukos, Ramón Astudillo
| Challenge: | Empirical studies with different instruction-tuned LMs show that our proposed method yields higher-quality instruction tuning data than Self-Instruct. |
| Approach: | They propose to use in-context learning techniques to train strong conversational agents . they propose to categorize and simplify ICL templates to make prompt learning easier . |
| Outcome: | Empirical results show that the proposed method yields higher-quality instruction tuning data than Self-Instruct and improves performance of both vanilla and instruction-tuned LMs. |
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. |
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. |
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. |
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. |