Papers by Praveen Venkateswaran
FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows (2025.emnlp-industry)
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Evelyn Duesterwald, Siyu Huo, Vatche Isahagian, K. R. Jayaram, Ritesh Kumar, Vinod Muthusamy, Punleuk Oum, Debashish Saha, Gegi Thomas, Praveen Venkateswaran
| Challenge: | Large Language Models (LLMs) can be used to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts. |
| Approach: | They propose to use large language models to convert natural language (NL) instructions into structured business process automation (BPA) process artifacts. |
| Outcome: | The proposed model can be used to translate NL into Python and convert it into widely adopted business process definition languages. |
OptiSeq: Ordering Examples On-The-Fly for In-Context Learning (2025.findings-emnlp)
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Rahul Atul Bhope, Praveen Venkateswaran, K. R. Jayaram, Vatche Isahagian, Vinod Muthusamy, Nalini Venkatasubramanian
| Challenge: | In-context-learning (ICL) is fragile and requires a lot of examples to perform. |
| Approach: | They propose a purely inference-time, dataset-free optimization method that efficiently determines the best example order. |
| Outcome: | The proposed method improves in-context-learning accuracy by 5.5 - 10.5 percentage points across multiple tasks. |
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). |
Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)
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Vinod Muthusamy, Yara Rizk, Kiran Kate, Praveen Venkateswaran, Vatche Isahagian, Ashu Gulati, Parijat Dube
| Challenge: | Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs. |
| Approach: | They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal. |
| Outcome: | The proposed use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, and the need for new metrics and benchmarks. |
TaskDiff: A Similarity Metric for Task-Oriented Conversations (2023.emnlp-main)
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| Challenge: | Popularity of chatGPT and Llama 2 has led to a race to build custom task-oriented conversational assistants in enterprise domains like finance and retail. |
| Approach: | They propose a conversational similarity metric that uses different dialogue components to compute similarity. |
| Outcome: | Experiments on a benchmark dataset show that the proposed metric outperforms existing approaches and is more robust than previous approaches. |
DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning (2023.emnlp-main)
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| Challenge: | Existing approaches to task-oriented conversation system DST use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models and elicit slot values from the dialogue context. |
| Approach: | They propose a generalizable in-context tuning approach that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates. |
| Outcome: | Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings using a much smaller model. |
Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering (2026.eacl-long)
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| Challenge: | In many real-world applications, users rely on natural language instructions to guide large language models (LLMs) However, LLMs do not attend to these instructions reliably, and users lack simple mechanisms to emphasize their importance beyond modifying prompt wording or structure. |
| Approach: | They propose an inference-time method that enables users to emphasize specific parts of their prompt by steering the model’s attention toward them, aligning the model's perceived importance of different tokens with user intent. |
| Outcome: | The proposed method improves instruction following across tasks involving multiple instructions and generalizes across models of varying scales. |
AI Steerability 360: A Toolkit for Steering Large Language Models (2026.acl-demo)
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Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran, Ching-Yun Ko, Pierre Dognin, Moninder Singh, Tejaswini Pedapati, Avinash Balakrishnan, Matthew Riemer, Dennis Wei, Inge Vejsbjerg, Elizabeth M. Daly, Kush R. Varshney
| Challenge: | The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. |
| Approach: | The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. |
| Outcome: | The toolkit is available under an Apache 2.0 license and is available on https://github.com/IBM/AISteer360. |