Papers by Sriram Balasubramanian
Tool Preferences in Agentic LLMs are Unreliable (2025.emnlp-main)
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Kazem Faghih, Wenxiao Wang, Yize Cheng, Siddhant Bharti, Gaurang Sriramanan, Sriram Balasubramanian, Parsa Hosseini, Soheil Feizi
| Challenge: | Large language models (LLMs) can now access a wide range of external tools thanks to the Model Context Protocol (MCP). |
| Approach: | They expose a vulnerability in prevalent tool/function-calling protocols by editing tool descriptions to find out which tools are used by LLMs. |
| Outcome: | The proposed changes in the tool descriptions can increase the usage of tools from LLMs when competing with alternatives. |
A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models (2025.findings-emnlp)
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| Challenge: | Chain-of-thought reasoning improves performance of large language models, but is it faithfully reflecting internal processes? |
| Approach: | They propose a new evaluation pipeline for categorizing bias articulation patterns and a novel evaluation pipeline to examine CoT faithfulness in large vision-language models. |
| Outcome: | The proposed evaluation pipeline enables significantly more precise analysis of CoT reasoning than previous methods. |
Decomposition-Enhanced Training for Post-Hoc Attributions in Language Models (2026.eacl-long)
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Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami, Ryan A. Rossi, Varun Manjunatha, Roshan Santhosh, Ruiyi Zhang, Soheil Feizi, Nedim Lipka
| Challenge: | Existing methods for extractive QA struggle in multi-hop, abstractive, and semi-extractive settings. |
| Approach: | They propose a method that prompts models to produce answer decompositions as intermediate reasoning steps. |
| Outcome: | The proposed method outperforms existing methods and matches or exceeds state-of-the-art frontier models. |