Papers by Amrita Saha
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Prompt Tuning has been successful as a parameter-efficient method of conditioning large-scale pre-trained language models to perform downstream tasks. |
| Approach: | They propose to use a vector-quantized input-contextualized prompt as an extension to the soft prompt tuning framework to learn contextualization of soft prompt tokens. |
| Outcome: | The proposed prompt outperforms soft prompt tuning by an average margin of 1.19% on various language understanding tasks like SuperGLUE, QA, Relation classification, NER and NLI. |
Personalized Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation (2023.emnlp-main)
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| Challenge: | Recent studies have shown that close-sourced LLMs lack the ability to integrate into real-world applications due to their high associated costs and ethical concerns. |
| Approach: | They propose to use student model to refine its own solution by querying ChatGPT to generate task instruction and solution pairs and querying data to refine model. |
| Outcome: | The proposed model outperforms standard distillation with only one third of the data. |
Beyond ’Aha!’: Toward Systematic Meta-Abilities Alignment in Large Reasoning Models (2026.findings-acl)
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| Challenge: | Prior work has shown that outcome-based reinforcement learning (RL) can incidentally elicit advanced reasoning behaviors such as self-correction, backtracking, and verification. |
| Approach: | They explicitly align large reasoning models with three meta-abilities: deduction, induction, and abduction, using automatically generated, self-verifiable tasks. |
| Outcome: | The proposed model aligns models with deduction, induction, and abduction meta-abilities using automatically generated, self-verifiable tasks. |
Learning Label Modular Prompts for Text Classification in the Wild (2022.emnlp-main)
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| Challenge: | Recent advances in parameter efficient tuning of pretrained language models have limited performance. |
| Approach: | They propose a label-modular prompt tuning framework for text classification tasks that emulates the transient nature of real-world. |
| Outcome: | The proposed framework outperforms baselines in two formidable settings and shows strong generalisation ability. |
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension (P18-1)
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| Challenge: | DuoRC contains 186,089 unique question-answer pairs created from 7680 movie plots . |
| Approach: | They propose a novel dataset for Reading Comprehension that motivates new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |
| Outcome: | The proposed dataset motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets. |