Papers by Krishna P. Gummadi
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective (2026.acl-long)
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Bishwamittra Ghosh, Soumi Das, Till Speicher, Qinyuan Wu, Mohammad Aflah Khan, Deepak Garg, Krishna P. Gummadi, Evimaria Terzi
| Challenge: | Prior studies comparing FT and ICL have yielded mixed and inconclusive results due to inconsistent experimental setups. |
| Approach: | They propose a formal language learning task with precise language boundaries, controlled string sampling, and no data contamination to enable a rigorous comparison. |
| Outcome: | The proposed task offers precise language boundaries, controlled string sampling, and no data contamination. |
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient approach for fine-tuning large language models. |
| Approach: | They propose a low-rank Adaptation framework that automatically selects and merges LoRA adapters at the instance level without additional training. |
| Outcome: | The proposed framework outperforms training-based baselines on some tasks upto a margin of 3.6% while remaining competitive on other tasks and maintaining inference throughput. |
TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability (2025.emnlp-demos)
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Mohammad Aflah Khan, Ameya Godbole, Johnny Wei, Ryan Yixiang Wang, James Flemings, Krishna P. Gummadi, Willie Neiswanger, Robin Jia
| Challenge: | Existing workflows for pretraining large language models are cumbersome, fragmented and inaccessible. |
| Approach: | They propose an open-source library for editing, inspection, and analysis of large language model datasets. |
| Outcome: | TokenSmith is an open-source library for editing, inspection, and analysis of large language model datasets. |
Characterizing Web Search in The Age of Generative AI (2026.findings-acl)
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Elisabeth Kirsten, Jost Große Perdekamp, Qinyuan Wu, Mihir Upadhyay, Krishna P. Gummadi, Muhammad Bilal Zafar
| Challenge: | generative search is a new search paradigm that uses LLMs to retrieve information from the web . traditional web search returns a ranked list of independent web pages . |
| Approach: | They compare generative search with traditional web search, which returns ranked results as a list of independent web pages. |
| Outcome: | The results show that generative search systems achieve topical coverage comparable to traditional search, but differ in retrieval footprints and synthesis strategies. |