Papers by Chandan Gautam
Class Name Guided Out-of-Scope Intent Classification (2024.findings-emnlp)
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Chandan Gautam, Sethupathy Parameswaran, Aditya Kane, Yuan Fang, Savitha Ramasamy, Suresh Sundaram, Sunil Sahu, Xiaoli Li
| Challenge: | SCOOS leverages semantic cues embedded in class labels to improve classification accuracy. |
| Approach: | They propose a method to create a compact feature space around class label semantics . they use a shared latent space between ID features and class names to minimize losses . |
| Outcome: | The proposed method outperforms existing methods for out-of-scope intent detection and ID intent classification. |
Counter Turing Test (CT2): AI-Generated Text Detection is Not as Easy as You May Think - Introducing AI Detectability Index (ADI) (2023.emnlp-main)
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Megha Chakraborty, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Shreya Gautam, Tanay Kumar, Krish Sharma, Niyar Barman, Chandan Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | a number of issues have arisen regarding the risk and consequences of AI-generated text detection. |
| Approach: | They propose a counter-turing test to evaluate the robustness of existing AGTD methods . they propose ADI, a quantifiable spectrum to assess detectability of LLMs . |
| Outcome: | The proposed method evaluates the robustness of existing AGTD methods . it shows that larger LLMs tend to have lower ADI, indicating they are less detectable . |
Refinement Matters: Textual Description Needs to be Refined for Zero-shot Learning (2022.findings-emnlp)
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| Challenge: | Zero-Shot Learning (ZSL) is a new form of learning that uses textual description and attribute to transfer knowledge from seen to unseen classes. |
| Approach: | They propose a non-generative gating-based attribute refinement network for ZSL that uses a circle loss-guided attribute embedder to refine the attributes. |
| Outcome: | The proposed approach outperforms generative methods and most generative ones in all three scenarios. |