Papers by Akshara Prabhakar
Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning (2024.findings-emnlp)
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| Challenge: | Chain-of-Thought (CoT) prompting has been shown to enhance the multi-step reasoning capabilities of Large Language Models (LLMs). |
| Approach: | They propose to use CoT prompting to analyze a symbolic reasoning task where letters are shifted forward some number of steps in the alphabet. |
| Outcome: | The proposed model performs well on a symbolic reasoning task, with three LLMs performing the task using CoT prompts. |
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)
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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Existing infrastructure for efficient agentic data processing and model training remains underdeveloped. |
| Approach: | They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 . |
| Outcome: | The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks. |
LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks (2025.coling-industry)
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| Challenge: | Low-Rank Adaptation (LoRA) is a popular technique for parameter-efficient fine-tuning of Large Language Models. |
| Approach: | They propose to combine LoRA modules to achieve skill composition . they propose to use concatenation of LoRAs to optimize weights for different LoRA training . |
| Outcome: | The proposed model outperforms existing models and data- merging techniques on math-word problems and domain-specialized corpora. |
Commonsense and Named Entity Aware Knowledge Grounded Dialogue Generation (2022.naacl-main)
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| Challenge: | Empirical results show that our proposed model outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment. |
| Approach: | They propose a model which uses large-scale commonsense and named entity based knowledge to ground dialogue on external knowledge and topic-specific knowledge associated with each utterance. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on two benchmark datasets. |
CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments (2025.naacl-long)
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Kung-Hsiang Huang, Akshara Prabhakar, Sidharth Dhawan, Yixin Mao, Huan Wang, Silvio Savarese, Caiming Xiong, Philippe Laban, Chien-Sheng Wu
| Challenge: | Existing benchmarks for evaluating CRM agents on work-related tasks are limited due to data privacy concerns. |
| Approach: | They propose a benchmark to evaluate AI agents on real-world CRM tasks . they use 16 commonly used industrial objects with high interconnectivity to simulate real data distributions. |
| Outcome: | The new benchmark evaluates AI agents on real-world customer service tasks . it includes 16 commonly used industrial objects with high interconnectivity . the results highlight the need for enhanced agent capabilities in function-calling and rule-following . |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |