Search Results for author: Sagnik Mukherjee

Found 8 papers, 4 papers with code

AUTOSUMM: Automatic Model Creation for Text Summarization

no code implementations EMNLP 2021 Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella

Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets.

Abstractive Text Summarization Deep Learning +3

Infogent: An Agent-Based Framework for Web Information Aggregation

1 code implementation24 Oct 2024 Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim, Zhenhailong Wang, Dilek Hakkani-Tur, Heng Ji

Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of linear sequence of actions with an end state that marks task completion.

Navigate

Cultural Conditioning or Placebo? On the Effectiveness of Socio-Demographic Prompting

no code implementations17 Jun 2024 Sagnik Mukherjee, Muhammad Farid Adilazuarda, Sunayana Sitaram, Kalika Bali, Alham Fikri Aji, Monojit Choudhury

We observe that all models except GPT-4 show significant variations in their responses on both kinds of datasets for both kinds of prompts, casting doubt on the robustness of the culturally-conditioned prompting as a method for eliciting cultural bias in models or as an alignment strategy.

Ethics MMLU

Towards Measuring and Modeling "Culture" in LLMs: A Survey

1 code implementation5 Mar 2024 Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Fikri Aji, Jacki O'Neill, Ashutosh Modi, Monojit Choudhury

We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs).

Survey

On the Robustness of Reading Comprehension Models to Entity Renaming

1 code implementation NAACL 2022 Jun Yan, Yang Xiao, Sagnik Mukherjee, Bill Yuchen Lin, Robin Jia, Xiang Ren

We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when the same questions are asked about an entity whose name has been changed?

Continual Pretraining Machine Reading Comprehension

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