Papers by Nikhil Verma
Personal Large Language Model Agents: A Case Study on Tailored Travel Planning (2024.emnlp-industry)
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Harmanpreet Singh, Nikhil Verma, Yixiao Wang, Manasa Bharadwaj, Homa Fashandi, Kevin Ferreira, Chul Lee
| Challenge: | Large Language Models (LLMs) are becoming more autonomous and capable of handling real-world tasks through their access to tools, various planning strategies, and memory, referred to as LLM agents. |
| Approach: | They introduce a personalized version of TravelPlanner and establish baselines for personal LLM agents by comparing generic and personal plans. |
| Outcome: | The proposed model encapsulates user-related information, preferences, and personal concepts and provides baselines for personal LLM agents. |
COUNT: COntrastive UNlikelihood Text Style Transfer for Text Detoxification (2023.findings-emnlp)
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Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Manasa Bharadwaj, Nikhil Verma, Ali Pesaranghader, Scott Sanner
| Challenge: | Text detoxification is a task to ensure the generation of non-toxic and safe text. |
| Approach: | They propose a novel contrastive unlikelihood objective that combines rephrasing and identity mapping to effectively isolate and focus learning on non-toxic style transfer. |
| Outcome: | The proposed method achieves significant improvements in fluency, content preservation, and detoxification on two parallel datasets. |
LEAP & LEAN: Look-ahead Planning and Agile Navigation for LLM Agents (2025.acl-industry)
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| Challenge: | Existing approaches to train large-scale models with extensive datasets are limited by their inadequate planning capabilities compared to humans. |
| Approach: | They propose a paradigm that enhances the performance of Large Language Models (LLMs) they use look-ahead planning to refine action selection and LEAN to streamline navigation through agile prompt construction. |
| Outcome: | The proposed framework outperforms agents trained via imitation learning, reinforcement learning, and reasoning-based approaches without any fine-tuning. |
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems (2025.emnlp-industry)
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| Challenge: | Existing evaluations focus on the correctness of the final output, overlooking inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs. |
| Approach: | They propose a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph. |
| Outcome: | The proposed framework shows that outcome-only metrics are insufficient for evaluating multi-agent performance on GSM8K. |
Neural Conversational QA: Learning to Reason vs Exploiting Patterns (2020.emnlp-main)
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| Challenge: | Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. |
| Approach: | They propose to modify a data-set with fewer spurious patterns to exploit them . they also propose to build a heuristic-based program to exploit spurious clues . |
| Outcome: | The proposed program exploits spurious patterns in the ShARC dataset, compared to neural models. |
Page Stream Segmentation with LLMs: Challenges and Applications in Insurance Document Automation (2025.coling-industry)
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| Challenge: | Existing models for page Stream Segmentation (PSS) are inadequate for high-stakes applications. |
| Approach: | They propose to use large language models for page Stream Segmentation to apply parameter-efficient fine-tuning to real-world insurance data. |
| Outcome: | The proposed model outperforms baseline models in page- and stream-level segmentation accuracy. |
CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades (2026.acl-industry)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable proficiency across diverse benchmarks, spanning scientific question answering to medical diagnosis tasks. |
| Approach: | They propose to insert multi-agent deliberation directly at each tier’s escalation boundary to enable consensus-driven resolution of ambiguities internally without invoking higher-cost upgrades. |
| Outcome: | The proposed architecture outperforms strong single-model cascades and standalone multi-agent systems across five benchmarks spanning science, medicine, and general knowledge by up to 26.75%. |