34 citations · 36 across the 8 of their papers we have counts for
9 papers
Search Engines Never Say No: How Frozen Agents React When the Retrieval Tool Refuses
Ramraj Chandradevan, Sayontan Ghosh, Vinoth Selvendran
A search tool never says no: it returns its top-k passages even when the index holds no answer, so the agent sees irrelevant text instead of a miss signal. We ask what frozen searc…
Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation
Zhiwei Zhang, Xiaomin Li, Yudi Lin +8
Large Language Models (LLMs) trained with reinforcement learning and verifiable rewards have achieved strong results on complex reasoning tasks. Recent work extends this paradigm t…
Bradley-Terry and Multi-Objective Reward Modeling Are Complementary
Zhiwei Zhang, Hui Liu, Xiaomin Li +10
Reward models trained on human preference data have demonstrated strong effectiveness in aligning Large Language Models (LLMs) with human intent under the framework of Reinforcemen…
Generative Query Reformulation Using Ensemble Prompting, Document Fusion, and Relevance Feedback
Kaustubh D. Dhole, Ramraj Chandradevan, Eugene Agichtein
Query Reformulation (QR) is a set of techniques used to transform a user's original search query to a text that better aligns with the user's intent and improves their search exper…
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation
Ramraj Chandradevan, Kaustubh D. Dhole, Eugene Agichtein
State-of-the-art neural rankers pre-trained on large task-specific training data such as MS-MARCO, have been shown to exhibit strong performance on various ranking tasks without do…
QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration
Kaustubh D. Dhole, Shivam Bajaj, Ramraj Chandradevan +1
Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. P…
An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback
Kaustubh D. Dhole, Ramraj Chandradevan, Eugene Agichtein
While search is the predominant method of accessing information, formulating effective queries remains a challenging task, especially for situations where the users are not familia…
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval
Eugene Yang, Suraj Nair, Ramraj Chandradevan +2
Pretrained language models have improved effectiveness on numerous tasks, including ad-hoc retrieval. Recent work has shown that continuing to pretrain a language model with auxili…
Learning Clinical Outcomes from Heterogeneous Genomic Data Sources
Safoora Yousefi, Amirreza Shaban, Mohamed Amgad +2
Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine la…